ISG Software Research Analyst Perspectives

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class="minimal-mobile-menu-link"><span>Model Building & Large Language Models</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/artificial-intelligence/natural-language-processing" class="minimal-mobile-menu-link"><span>Natural Language Processing</span></a></span> </li> </ul> </li> <li class="minimal-has-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/customerexperience" class="minimal-mobile-menu-link"><span>Customer Experience</span></a></span> <ul class="minimal-mobile-menu-submenu level-3"> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/customerexperience/agentmanagement" class="minimal-mobile-menu-link"><span>Agent Management</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/customerexperience/contactcenter" class="minimal-mobile-menu-link"><span>Contact Center</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/customerexperience/customerexperiencemanagement" class="minimal-mobile-menu-link"><span>Customer Experience Management</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/customerexperience/fieldservice" class="minimal-mobile-menu-link"><span>Field Service</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/customerexperience/intelligentselfservice" class="minimal-mobile-menu-link"><span>Intelligent Self-Service</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/customerexperience/voiceofthecustomer" class="minimal-mobile-menu-link"><span>Voice of the Customer</span></a></span> </li> </ul> </li> <li class="minimal-has-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/digitalbusiness" class="minimal-mobile-menu-link"><span>Digital Business</span></a></span> <ul class="minimal-mobile-menu-submenu level-3"> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/digitalbusiness/digital-applications" class="minimal-mobile-menu-link"><span>Digital Applications</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/digitalbusiness/digitalcommunications" class="minimal-mobile-menu-link"><span>Digital Communications</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/digitalbusiness/digital-intelligence" class="minimal-mobile-menu-link"><span>Digital Intelligence</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/digitalbusiness/digital-operations" class="minimal-mobile-menu-link"><span>Digital Operations</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/digitalbusiness/experiencemanagement" class="minimal-mobile-menu-link"><span>Experience Management</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/digitalbusiness/generative-ai" class="minimal-mobile-menu-link"><span>Generative AI</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/digitalbusiness/performancemanagement" class="minimal-mobile-menu-link"><span>Performance Management</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/digitalbusiness/sustainabilityandesg" class="minimal-mobile-menu-link"><span>Sustainability & ESG</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/digitalbusiness/workmanagement" class="minimal-mobile-menu-link"><span>Work Management</span></a></span> </li> </ul> </li> <li class="minimal-has-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/digitaltechnology" class="minimal-mobile-menu-link"><span>Digital Technology</span></a></span> <ul class="minimal-mobile-menu-submenu level-3"> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/digitaltechnology/businesscontinuity" class="minimal-mobile-menu-link"><span>Business Continuity</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/digitaltechnology/cloudcomputing" class="minimal-mobile-menu-link"><span>Cloud Computing</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/digitaltechnology/devops" class="minimal-mobile-menu-link"><span>DevOps and Platforms</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/digitaltechnology/digitalsecurity" class="minimal-mobile-menu-link"><span>Digital Security</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/digitaltechnology/intelligentautomation" class="minimal-mobile-menu-link"><span>Intelligent Automation</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/digitaltechnology/internetofthings" class="minimal-mobile-menu-link"><span>IoT and Edge Computing</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/digitaltechnology/itops" class="minimal-mobile-menu-link"><span>ITOps</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/digitaltechnology/it-service-management" class="minimal-mobile-menu-link"><span>IT Service Management</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/digitaltechnology/observability" class="minimal-mobile-menu-link"><span>Observability</span></a></span> </li> </ul> </li> <li class="minimal-has-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/humancapitalmanagement" class="minimal-mobile-menu-link"><span>Human Capital Management</span></a></span> <ul class="minimal-mobile-menu-submenu level-3"> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/humancapitalmanagement/employeeexperience" class="minimal-mobile-menu-link"><span>Employee Experience</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/humancapitalmanagement/learningmanagement" class="minimal-mobile-menu-link"><span>Learning Management</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/humancapitalmanagement/payrollmanagement" class="minimal-mobile-menu-link"><span>Payroll Management</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/humancapitalmanagement/talentmanagement" class="minimal-mobile-menu-link"><span>Talent Management</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/humancapitalmanagement/totalcompensationmanagement" class="minimal-mobile-menu-link"><span>Total Compensation Management</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/humancapitalmanagement/workforcemanagement" class="minimal-mobile-menu-link"><span>Workforce Management</span></a></span> </li> </ul> </li> <li class="minimal-has-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/marketing" class="minimal-mobile-menu-link"><span>Marketing</span></a></span> <ul class="minimal-mobile-menu-submenu level-3"> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/marketing/customerdataplatforms" class="minimal-mobile-menu-link"><span>Customer Data Platforms</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/marketing/digitalexperienceplatforms" class="minimal-mobile-menu-link"><span>Digital Experience Platform</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/marketing/digitalmarketing" class="minimal-mobile-menu-link"><span>Digital Marketing</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/marketing/intelligentmarketing" class="minimal-mobile-menu-link"><span>Intelligent Marketing</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/marketing/marketingperformancemanagement" class="minimal-mobile-menu-link"><span>Marketing Performance Management</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/marketing/productexperiencemanagement" class="minimal-mobile-menu-link"><span>Product Experience Management</span></a></span> </li> </ul> </li> <li class="minimal-has-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/officeoffinance" class="minimal-mobile-menu-link"><span>Office of Finance</span></a></span> <ul class="minimal-mobile-menu-submenu level-3"> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/officeoffinance/businessplanning" class="minimal-mobile-menu-link"><span>Business Planning</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/officeoffinance/digitalfinance" class="minimal-mobile-menu-link"><span>Digital Finance</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/officeoffinance/erpandcontinuousaccounting" class="minimal-mobile-menu-link"><span>ERP & Continuous Accounting</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/officeoffinance/consolidate-and-close-management" class="minimal-mobile-menu-link"><span>Consolidate & Close Management</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/officeoffinance/procure-to-pay" class="minimal-mobile-menu-link"><span>Procure-to-Pay</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/officeoffinance/order-to-cash" class="minimal-mobile-menu-link"><span>Order-to-Cash</span></a></span> </li> </ul> </li> <li class="minimal-has-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/officeofrevenue" class="minimal-mobile-menu-link"><span>Office of Revenue</span></a></span> <ul class="minimal-mobile-menu-submenu level-3"> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/officeofrevenue/digitalcommerce" class="minimal-mobile-menu-link"><span>Digital Commerce</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/officeofrevenue/partnermanagement" class="minimal-mobile-menu-link"><span>Partner Management</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/officeofrevenue/revenuemanagement" class="minimal-mobile-menu-link"><span>Revenue Management</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/officeofrevenue/revenueperformancemanagement" class="minimal-mobile-menu-link"><span>Revenue Performance Management</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/officeofrevenue/salesengagement" class="minimal-mobile-menu-link"><span>Sales Engagement</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/officeofrevenue/subscriptionmanagement" class="minimal-mobile-menu-link"><span>Subscription Management</span></a></span> </li> </ul> </li> <li class="minimal-has-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/operationsandsupplychain" class="minimal-mobile-menu-link"><span>Operations & Supply Chain</span></a></span> <ul class="minimal-mobile-menu-submenu level-3"> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/operationsandsupplychain/continuousplanning" class="minimal-mobile-menu-link"><span>Continuous Planning</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/operationsandsupplychain/continuousupplychainanderp" class="minimal-mobile-menu-link"><span>Continuous Supply Chain & ERP</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/operationsandsupplychain/productinformationmanagement" class="minimal-mobile-menu-link"><span>Product Information Management</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/operationsandsupplychain/propertytechnology" class="minimal-mobile-menu-link"><span>Property Technology</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/operationsandsupplychain/supplierrelationshipmanagement" class="minimal-mobile-menu-link"><span>Supplier Relationship Management</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/operationsandsupplychain/sustainabilitymanagement" class="minimal-mobile-menu-link"><span>Sustainability Management</span></a></span> </li> </ul> </li> </ul> </li> <li class="minimal-has-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/vertical-industries" class="minimal-mobile-menu-link"><span>Industries</span></a></span> <ul class="minimal-mobile-menu-submenu level-2"> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/vertical-industry/automotive" class="minimal-mobile-menu-link"><span>Automotive</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/vertical-industry/banking" class="minimal-mobile-menu-link"><span>Banking</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/vertical-industry/consumer-products" class="minimal-mobile-menu-link"><span>Consumer Products</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/vertical-industry/financial-services" class="minimal-mobile-menu-link"><span>Financial Services</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/vertical-industry/food-beverage" class="minimal-mobile-menu-link"><span>Food & Beverage</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/vertical-industry/healthcare" class="minimal-mobile-menu-link"><span>Healthcare</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/vertical-industry/hospitality" class="minimal-mobile-menu-link"><span>Hospitality</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/vertical-industry/insurance" class="minimal-mobile-menu-link"><span>Insurance</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/vertical-industry/manufacturing" class="minimal-mobile-menu-link"><span>Manufacturing</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/vertical-industry/media-entertainment" class="minimal-mobile-menu-link"><span>Media & Entertainment</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/vertical-industry/real-estate" class="minimal-mobile-menu-link"><span>Real Estate</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/vertical-industry/retail" class="minimal-mobile-menu-link"><span>Retail</span></a></span> </li> </ul> </li> <li class="minimal-has-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/research" class="minimal-mobile-menu-link"><span>Research</span></a></span> <ul class="minimal-mobile-menu-submenu level-2"> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/research/market-agendas" class="minimal-mobile-menu-link"><span>Market Agendas</span></a></span> </li> <li class="minimal-has-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://blog.ventanaresearch.com/" class="minimal-mobile-menu-link"><span>Analyst Perspectives</span></a></span> <ul class="minimal-mobile-menu-submenu level-3"> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://davidmenninger.ventanaresearch.com/" class="minimal-mobile-menu-link"><span>David Menninger</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://jefforr.ventanaresearch.com/" class="minimal-mobile-menu-link"><span>Jeff Orr</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://keithdawson.isg-research.net" class="minimal-mobile-menu-link"><span>Keith Dawson</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://marksmith.ventanaresearch.com/" class="minimal-mobile-menu-link"><span>Mark Smith</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://mattaslett.isg-research.net" class="minimal-mobile-menu-link"><span>Matt Aslett</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://matthewbrown.isg-research.net" class="minimal-mobile-menu-link"><span>Matthew Brown</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://robertkugel.ventanaresearch.com/" class="minimal-mobile-menu-link"><span>Robert Kugel</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://stephenhurrell.ventanaresearch.com/" 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We offer a portfolio of advisory, research, thought leadership and digital education services to help optimize market strategy, planning and execution.</p> <p><strong><a href="https://www.ventanaresearch.com/services/vendors/analyst_relations" rel="noopener">Analyst Relations</a></strong></p> <p><strong><a href="https://www.ventanaresearch.com/services/vendors/demand_generation" rel="noopener">Demand Generation</a></strong></p> <p><strong><a href="https://www.ventanaresearch.com/services/vendors/product-marketing" rel="noopener">Product Marketing</a></strong></p> <p><strong><a href="https://www.ventanaresearch.com/vendor-coverage" target="_self" role="menuitem">Market Coverage</a></strong></p> <p><strong><a href="https://www.ventanaresearch.com/briefing" target="_self" role="menuitem">Request a Briefing</a></strong><br><br><br></p></div> <span id="hs_cos_wrapper_widget_1687168884328_" class="hs_cos_wrapper hs_cos_wrapper_widget hs_cos_wrapper_type_simple_menu" style="" data-hs-cos-general-type="widget" data-hs-cos-type="simple_menu"><ul></ul></span> <div class="bottom_rich_text"> </div> </div> </div> </div> </div> </div> <script src="https://blog.isg-research.net/hs-fs/hub/2035844/hub_generated/template_assets/90531223305/1725882478811/Ventana_Research_2023/js/mmenu.min.js"></script> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> <div id="hs_cos_wrapper_Structured_data" class="hs_cos_wrapper hs_cos_wrapper_widget hs_cos_wrapper_type_module" style="" data-hs-cos-general-type="widget" data-hs-cos-type="module"></div> <div class="body-container-wrapper"> <div class="blog-header background-image post_page_2" data-bg-color="secondary" style="background-size:cover;background-position:50%;background-repeat:no-repeat;background-image:url(https://blog.isg-research.net/hubfs/_images/_backgrounds/314838419_1200px.jpg)">  <div class="row-fluid"> <div class="blog-header__inner dnd-row"> <div class="blog--post__image dnd-section">  <h1 class="analyst-header-title">Analyst Perspectives</h1>  </div> </div> </div> </div> <div class="content-wrapper-listing blog-single"> <div class="blog-content-wrapper"> <div>  <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://blog.isg-research.net/vector-search-and-rag-improve-trust-in-generative-ai">Vector Search and RAG Improve Trust in Generative AI</a></h2> <div class="author-meta"> Posted by <a href="https://blog.isg-research.net/author/matt-aslett"> Matt Aslett </a> on <span class="post-date">31 October 2023 </span> </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://blog.isg-research.net/hs-fs/hubfs/Vector_Search_and_RAG_Improve_Trust_in_Generative_AI.png?width=200&name=Vector_Search_and_RAG_Improve_Trust_in_Generative_AI.png" width="200" !important: srcset="https://blog.isg-research.net/hs-fs/hubfs/Vector_Search_and_RAG_Improve_Trust_in_Generative_AI.png?width=100&name=Vector_Search_and_RAG_Improve_Trust_in_Generative_AI.png 100w, https://blog.isg-research.net/hs-fs/hubfs/Vector_Search_and_RAG_Improve_Trust_in_Generative_AI.png?width=200&name=Vector_Search_and_RAG_Improve_Trust_in_Generative_AI.png 200w, https://blog.isg-research.net/hs-fs/hubfs/Vector_Search_and_RAG_Improve_Trust_in_Generative_AI.png?width=300&name=Vector_Search_and_RAG_Improve_Trust_in_Generative_AI.png 300w, https://blog.isg-research.net/hs-fs/hubfs/Vector_Search_and_RAG_Improve_Trust_in_Generative_AI.png?width=400&name=Vector_Search_and_RAG_Improve_Trust_in_Generative_AI.png 400w, https://blog.isg-research.net/hs-fs/hubfs/Vector_Search_and_RAG_Improve_Trust_in_Generative_AI.png?width=500&name=Vector_Search_and_RAG_Improve_Trust_in_Generative_AI.png 500w, https://blog.isg-research.net/hs-fs/hubfs/Vector_Search_and_RAG_Improve_Trust_in_Generative_AI.png?width=600&name=Vector_Search_and_RAG_Improve_Trust_in_Generative_AI.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p>I <a href="https://mattaslett.ventanaresearch.com/large-language-models-and-generative-ai-beware-misplaced-trust" rel="noopener" target="_blank">previously discussed</a> the trust and accuracy limitations of large language models, suggesting that data and analytics vendors provide guidance about potentially inaccurate results and the risks of creating a misplaced level of trust. In the months that have followed, we are seeing some clarity from these vendors about the approaches organizations can take to increase trust and accuracy when developing applications that incorporate generative AI, including fine-tuning and prompt engineering. It...</p> </div> <a href="https://blog.isg-research.net/vector-search-and-rag-improve-trust-in-generative-ai"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://blog.isg-research.net/topic/analytics">Analytics</a>, <a href="https://blog.isg-research.net/topic/business-intelligence">Business Intelligence</a>, <a href="https://blog.isg-research.net/topic/data">Data</a>, <a href="https://blog.isg-research.net/topic/digital-technology">Digital Technology</a>, <a href="https://blog.isg-research.net/topic/natural-language-processing">natural language processing</a>, <a href="https://blog.isg-research.net/topic/data-operations">data operations</a>, <a href="https://blog.isg-research.net/topic/analytics-data">Analytics & Data</a>, <a href="https://blog.isg-research.net/topic/operational-data-platforms">operational data platforms</a>, <a href="https://blog.isg-research.net/topic/analytic-data-platforms">Analytic Data Platforms</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://blog.isg-research.net/the-buyers-guide-for-data-observability-classifies-and-rates-vendors">The Buyers Guide for Data Observability Classifies and Rates Vendors</a></h2> <div class="author-meta"> Posted by <a href="https://blog.isg-research.net/author/matt-aslett"> Matt Aslett </a> on <span class="post-date">26 October 2023 </span> </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_Data_Observability_Classifies_and_Rates_Vendors.png.png?width=200&name=The_Buyers_Guide_for_Data_Observability_Classifies_and_Rates_Vendors.png.png" width="200" !important: srcset="https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_Data_Observability_Classifies_and_Rates_Vendors.png.png?width=100&name=The_Buyers_Guide_for_Data_Observability_Classifies_and_Rates_Vendors.png.png 100w, https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_Data_Observability_Classifies_and_Rates_Vendors.png.png?width=200&name=The_Buyers_Guide_for_Data_Observability_Classifies_and_Rates_Vendors.png.png 200w, https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_Data_Observability_Classifies_and_Rates_Vendors.png.png?width=300&name=The_Buyers_Guide_for_Data_Observability_Classifies_and_Rates_Vendors.png.png 300w, https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_Data_Observability_Classifies_and_Rates_Vendors.png.png?width=400&name=The_Buyers_Guide_for_Data_Observability_Classifies_and_Rates_Vendors.png.png 400w, https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_Data_Observability_Classifies_and_Rates_Vendors.png.png?width=500&name=The_Buyers_Guide_for_Data_Observability_Classifies_and_Rates_Vendors.png.png 500w, https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_Data_Observability_Classifies_and_Rates_Vendors.png.png?width=600&name=The_Buyers_Guide_for_Data_Observability_Classifies_and_Rates_Vendors.png.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p><span data-contrast="auto">I am happy to share insights gleaned from our latest Buyers Guide, an assessment of how well vendors’ offerings meet buyers’ requirements. The</span><a href="https://www.ventanaresearch.com/buyers_guide/data/data_observability_2023"><span data-contrast="none">Ventana Research 2023 Data Observability Buyers Guide</span></a><span data-contrast="auto"> is the distillation of a year of market and product research by Ventana Research. Drawing on our Benchmark Research, we apply a structured methodology built on evaluation categories that reflect the real-world criteria incorporated in a request for proposal to data vendors supporting the spectrum of...</span></p> </div> <a href="https://blog.isg-research.net/the-buyers-guide-for-data-observability-classifies-and-rates-vendors"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://blog.isg-research.net/topic/data">Data</a>, <a href="https://blog.isg-research.net/topic/data-operations">data operations</a>, <a href="https://blog.isg-research.net/topic/data-observability">Data Observability</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://blog.isg-research.net/data-observability-buyers-guide-market-observations">Data Observability Buyers Guide: Market Observations</a></h2> <div class="author-meta"> Posted by <a href="https://blog.isg-research.net/author/matt-aslett"> Matt Aslett </a> on <span class="post-date">25 October 2023 </span> </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://blog.isg-research.net/hs-fs/hubfs/Data_Observability_Buyers_Guide_Market_Observations.png.png?width=200&name=Data_Observability_Buyers_Guide_Market_Observations.png.png" width="200" !important: srcset="https://blog.isg-research.net/hs-fs/hubfs/Data_Observability_Buyers_Guide_Market_Observations.png.png?width=100&name=Data_Observability_Buyers_Guide_Market_Observations.png.png 100w, https://blog.isg-research.net/hs-fs/hubfs/Data_Observability_Buyers_Guide_Market_Observations.png.png?width=200&name=Data_Observability_Buyers_Guide_Market_Observations.png.png 200w, https://blog.isg-research.net/hs-fs/hubfs/Data_Observability_Buyers_Guide_Market_Observations.png.png?width=300&name=Data_Observability_Buyers_Guide_Market_Observations.png.png 300w, https://blog.isg-research.net/hs-fs/hubfs/Data_Observability_Buyers_Guide_Market_Observations.png.png?width=400&name=Data_Observability_Buyers_Guide_Market_Observations.png.png 400w, https://blog.isg-research.net/hs-fs/hubfs/Data_Observability_Buyers_Guide_Market_Observations.png.png?width=500&name=Data_Observability_Buyers_Guide_Market_Observations.png.png 500w, https://blog.isg-research.net/hs-fs/hubfs/Data_Observability_Buyers_Guide_Market_Observations.png.png?width=600&name=Data_Observability_Buyers_Guide_Market_Observations.png.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p><span data-contrast="auto">The 2023 Ventana Research Buyers Guide for Data Observability research enables me to provide observations about how the market has advanced. </span></p> </div> <a href="https://blog.isg-research.net/data-observability-buyers-guide-market-observations"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://blog.isg-research.net/topic/data">Data</a>, <a href="https://blog.isg-research.net/topic/data-operations">data operations</a>, <a href="https://blog.isg-research.net/topic/data-observability">Data Observability</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://blog.isg-research.net/nice-puts-knowledge-at-the-heart-of-its-ai-strategy">NICE Puts Knowledge at the Heart of Its AI Strategy</a></h2> <div class="author-meta"> Posted by <a href="https://blog.isg-research.net/author/keith-dawson"> Keith Dawson </a> on <span class="post-date">25 October 2023 </span> </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://blog.isg-research.net/hs-fs/hubfs/NICE_Puts_Knowledge_at_the_Heart_of_Its_AI_Strategy.png?width=200&name=NICE_Puts_Knowledge_at_the_Heart_of_Its_AI_Strategy.png" width="200" !important: srcset="https://blog.isg-research.net/hs-fs/hubfs/NICE_Puts_Knowledge_at_the_Heart_of_Its_AI_Strategy.png?width=100&name=NICE_Puts_Knowledge_at_the_Heart_of_Its_AI_Strategy.png 100w, https://blog.isg-research.net/hs-fs/hubfs/NICE_Puts_Knowledge_at_the_Heart_of_Its_AI_Strategy.png?width=200&name=NICE_Puts_Knowledge_at_the_Heart_of_Its_AI_Strategy.png 200w, https://blog.isg-research.net/hs-fs/hubfs/NICE_Puts_Knowledge_at_the_Heart_of_Its_AI_Strategy.png?width=300&name=NICE_Puts_Knowledge_at_the_Heart_of_Its_AI_Strategy.png 300w, https://blog.isg-research.net/hs-fs/hubfs/NICE_Puts_Knowledge_at_the_Heart_of_Its_AI_Strategy.png?width=400&name=NICE_Puts_Knowledge_at_the_Heart_of_Its_AI_Strategy.png 400w, https://blog.isg-research.net/hs-fs/hubfs/NICE_Puts_Knowledge_at_the_Heart_of_Its_AI_Strategy.png?width=500&name=NICE_Puts_Knowledge_at_the_Heart_of_Its_AI_Strategy.png 500w, https://blog.isg-research.net/hs-fs/hubfs/NICE_Puts_Knowledge_at_the_Heart_of_Its_AI_Strategy.png?width=600&name=NICE_Puts_Knowledge_at_the_Heart_of_Its_AI_Strategy.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p><a href="https://www.nice.com/">NICE</a> held its annual Analyst Summit this month in the magnificent high-altitude mountains of Peru, a fantastic environment in which to hear top executives share insights into the changing company and industry. NICE has been very successful in navigating the industry transition from voice-centric interactions to a digital-first posture. Consumer behavior is one of the underlying causes of this shift, forcing many organizations to rethink customer experience strategies.</p> </div> <a href="https://blog.isg-research.net/nice-puts-knowledge-at-the-heart-of-its-ai-strategy"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://blog.isg-research.net/topic/customer-experience">Customer Experience</a>, <a href="https://blog.isg-research.net/topic/voice-of-the-customer">Voice of the Customer</a>, <a href="https://blog.isg-research.net/topic/contact-center">Contact Center</a>, <a href="https://blog.isg-research.net/topic/agent-management">agent management</a>, <a href="https://blog.isg-research.net/topic/customer-experience-management">Customer Experience Management</a>, <a href="https://blog.isg-research.net/topic/intelligent-self-service">Intelligent Self-Service</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://blog.isg-research.net/stream-and-event-processing-require-real-time-analytics">Stream and Event Processing Require Real-Time Analytics</a></h2> <div class="author-meta"> Posted by <a href="https://blog.isg-research.net/author/david-menninger"> David Menninger </a> on <span class="post-date">25 October 2023 </span> </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://blog.isg-research.net/hs-fs/hubfs/Stream_and_Event_Processing_Require_Real-Time_Analytics.png?width=200&name=Stream_and_Event_Processing_Require_Real-Time_Analytics.png" width="200" !important: srcset="https://blog.isg-research.net/hs-fs/hubfs/Stream_and_Event_Processing_Require_Real-Time_Analytics.png?width=100&name=Stream_and_Event_Processing_Require_Real-Time_Analytics.png 100w, https://blog.isg-research.net/hs-fs/hubfs/Stream_and_Event_Processing_Require_Real-Time_Analytics.png?width=200&name=Stream_and_Event_Processing_Require_Real-Time_Analytics.png 200w, https://blog.isg-research.net/hs-fs/hubfs/Stream_and_Event_Processing_Require_Real-Time_Analytics.png?width=300&name=Stream_and_Event_Processing_Require_Real-Time_Analytics.png 300w, https://blog.isg-research.net/hs-fs/hubfs/Stream_and_Event_Processing_Require_Real-Time_Analytics.png?width=400&name=Stream_and_Event_Processing_Require_Real-Time_Analytics.png 400w, https://blog.isg-research.net/hs-fs/hubfs/Stream_and_Event_Processing_Require_Real-Time_Analytics.png?width=500&name=Stream_and_Event_Processing_Require_Real-Time_Analytics.png 500w, https://blog.isg-research.net/hs-fs/hubfs/Stream_and_Event_Processing_Require_Real-Time_Analytics.png?width=600&name=Stream_and_Event_Processing_Require_Real-Time_Analytics.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p>In my past perspectives, I’ve written about the evolution from <a href="https://davidmenninger.ventanaresearch.com/historical-databases-becoming-a-thing-of-the-past" rel="noopener" target="_blank">data at rest to data in motion</a> and the fact that <a href="https://davidmenninger.ventanaresearch.com/dont-rely-on-dashboards-for-real-time-analytics" rel="noopener" target="_blank">you can’t rely on dashboards for real-time analytics</a>. Organizations are becoming more and more event-driven and operating based on streaming data. As well, analytics are becoming more and more intertwined with operations. More than one-fifth of organizations (22%) describe their analytics workloads as real time in our Data and Analytics Benchmark Research and nearly half (47%) of...</p> </div> <a href="https://blog.isg-research.net/stream-and-event-processing-require-real-time-analytics"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://blog.isg-research.net/topic/analytics">Analytics</a>, <a href="https://blog.isg-research.net/topic/business-intelligence">Business Intelligence</a>, <a href="https://blog.isg-research.net/topic/data">Data</a>, <a href="https://blog.isg-research.net/topic/digital-technology">Digital Technology</a>, <a href="https://blog.isg-research.net/topic/streaming-analytics">Streaming Analytics</a>, <a href="https://blog.isg-research.net/topic/analytics-data">Analytics & Data</a>, <a href="https://blog.isg-research.net/topic/streaming-data-events">Streaming Data & Events</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://blog.isg-research.net/the-buyers-guide-for-data-orchestration-classifies-and-rates-vendors">The Buyers Guide for Data Orchestration Classifies and Rates Vendors</a></h2> <div class="author-meta"> Posted by <a href="https://blog.isg-research.net/author/matt-aslett"> Matt Aslett </a> on <span class="post-date">24 October 2023 </span> </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_Data_Orchestration_Classifies_and_Rates_Vendors.png?width=200&name=The_Buyers_Guide_for_Data_Orchestration_Classifies_and_Rates_Vendors.png" width="200" !important: srcset="https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_Data_Orchestration_Classifies_and_Rates_Vendors.png?width=100&name=The_Buyers_Guide_for_Data_Orchestration_Classifies_and_Rates_Vendors.png 100w, https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_Data_Orchestration_Classifies_and_Rates_Vendors.png?width=200&name=The_Buyers_Guide_for_Data_Orchestration_Classifies_and_Rates_Vendors.png 200w, https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_Data_Orchestration_Classifies_and_Rates_Vendors.png?width=300&name=The_Buyers_Guide_for_Data_Orchestration_Classifies_and_Rates_Vendors.png 300w, https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_Data_Orchestration_Classifies_and_Rates_Vendors.png?width=400&name=The_Buyers_Guide_for_Data_Orchestration_Classifies_and_Rates_Vendors.png 400w, https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_Data_Orchestration_Classifies_and_Rates_Vendors.png?width=500&name=The_Buyers_Guide_for_Data_Orchestration_Classifies_and_Rates_Vendors.png 500w, https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_Data_Orchestration_Classifies_and_Rates_Vendors.png?width=600&name=The_Buyers_Guide_for_Data_Orchestration_Classifies_and_Rates_Vendors.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p><span data-contrast="auto">I am happy to share insights gleaned from our latest Buyers Guide, an assessment of how well vendors’ offerings meet buyers’ requirements. The </span><a href="https://www.ventanaresearch.com/buyers_guide/data/data_orchestration_2023"><span data-contrast="none">Ventana Research 2023 Data Orchestration Buyers Guide</span></a><span data-contrast="auto"> is the distillation of a year of market and product research by Ventana Research. Drawing on our Benchmark Research, we apply a structured methodology built on evaluation categories that reflect the real-world criteria incorporated in a request for proposal to data vendors supporting the spectrum of...</span></p> </div> <a href="https://blog.isg-research.net/the-buyers-guide-for-data-orchestration-classifies-and-rates-vendors"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://blog.isg-research.net/topic/data">Data</a>, <a href="https://blog.isg-research.net/topic/data-operations">data operations</a>, <a href="https://blog.isg-research.net/topic/data-orchestration">Data Orchestration</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://blog.isg-research.net/data-orchestration-buyers-guide-market-observations">Data Orchestration Buyers Guide: Market Observations</a></h2> <div class="author-meta"> Posted by <a href="https://blog.isg-research.net/author/matt-aslett"> Matt Aslett </a> on <span class="post-date">23 October 2023 </span> </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://blog.isg-research.net/hs-fs/hubfs/Data_Orchestration_Buyers_Guide_Market_Observations.png?width=200&name=Data_Orchestration_Buyers_Guide_Market_Observations.png" width="200" !important: srcset="https://blog.isg-research.net/hs-fs/hubfs/Data_Orchestration_Buyers_Guide_Market_Observations.png?width=100&name=Data_Orchestration_Buyers_Guide_Market_Observations.png 100w, https://blog.isg-research.net/hs-fs/hubfs/Data_Orchestration_Buyers_Guide_Market_Observations.png?width=200&name=Data_Orchestration_Buyers_Guide_Market_Observations.png 200w, https://blog.isg-research.net/hs-fs/hubfs/Data_Orchestration_Buyers_Guide_Market_Observations.png?width=300&name=Data_Orchestration_Buyers_Guide_Market_Observations.png 300w, https://blog.isg-research.net/hs-fs/hubfs/Data_Orchestration_Buyers_Guide_Market_Observations.png?width=400&name=Data_Orchestration_Buyers_Guide_Market_Observations.png 400w, https://blog.isg-research.net/hs-fs/hubfs/Data_Orchestration_Buyers_Guide_Market_Observations.png?width=500&name=Data_Orchestration_Buyers_Guide_Market_Observations.png 500w, https://blog.isg-research.net/hs-fs/hubfs/Data_Orchestration_Buyers_Guide_Market_Observations.png?width=600&name=Data_Orchestration_Buyers_Guide_Market_Observations.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p><span data-contrast="auto">The 2023 Ventana Research Buyers Guide for Data Orchestration research enables me to provide observations about how the market has advanced. </span></p> </div> <a href="https://blog.isg-research.net/data-orchestration-buyers-guide-market-observations"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://blog.isg-research.net/topic/data">Data</a>, <a href="https://blog.isg-research.net/topic/data-operations">data operations</a>, <a href="https://blog.isg-research.net/topic/data-orchestration">Data Orchestration</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://blog.isg-research.net/the-buyers-guide-for-data-pipelines-classifies-and-rates-vendors">The Buyers Guide for Data Pipelines Classifies and Rates Vendors</a></h2> <div class="author-meta"> Posted by <a href="https://blog.isg-research.net/author/matt-aslett"> Matt Aslett </a> on <span class="post-date">20 October 2023 </span> </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_Data_Pipelines_Classifies_and_Rates_Vendors.png?width=200&name=The_Buyers_Guide_for_Data_Pipelines_Classifies_and_Rates_Vendors.png" width="200" !important: srcset="https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_Data_Pipelines_Classifies_and_Rates_Vendors.png?width=100&name=The_Buyers_Guide_for_Data_Pipelines_Classifies_and_Rates_Vendors.png 100w, https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_Data_Pipelines_Classifies_and_Rates_Vendors.png?width=200&name=The_Buyers_Guide_for_Data_Pipelines_Classifies_and_Rates_Vendors.png 200w, https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_Data_Pipelines_Classifies_and_Rates_Vendors.png?width=300&name=The_Buyers_Guide_for_Data_Pipelines_Classifies_and_Rates_Vendors.png 300w, https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_Data_Pipelines_Classifies_and_Rates_Vendors.png?width=400&name=The_Buyers_Guide_for_Data_Pipelines_Classifies_and_Rates_Vendors.png 400w, https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_Data_Pipelines_Classifies_and_Rates_Vendors.png?width=500&name=The_Buyers_Guide_for_Data_Pipelines_Classifies_and_Rates_Vendors.png 500w, https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_Data_Pipelines_Classifies_and_Rates_Vendors.png?width=600&name=The_Buyers_Guide_for_Data_Pipelines_Classifies_and_Rates_Vendors.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p><span data-contrast="auto">I am happy to share insights gleaned from our latest Buyers Guide, an assessment of how well vendors’ offerings meet buyers’ requirements. The </span><a href="https://www.ventanaresearch.com/buyers_guide/data/data_pipelines_2023"><span data-contrast="none">Ventana Research 2023 Data Pipelines Buyers Guide</span></a><span data-contrast="auto"> is the distillation of a year of market and product research by Ventana Research. Drawing on our Benchmark Research, we apply a structured methodology built on evaluation categories that reflect the real-world criteria incorporated in a request for proposal to Data vendors supporting the spectrum of Data...</span></p> </div> <a href="https://blog.isg-research.net/the-buyers-guide-for-data-pipelines-classifies-and-rates-vendors"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://blog.isg-research.net/topic/data">Data</a>, <a href="https://blog.isg-research.net/topic/data-operations">data operations</a>, <a href="https://blog.isg-research.net/topic/data-pipelines">Data Pipelines</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://blog.isg-research.net/the-buyers-guide-for-business-planning-classifies-and-rates-vendors">The Buyers Guide for Business Planning Classifies and Rates Vendors</a></h2> <div class="author-meta"> Posted by <a href="https://blog.isg-research.net/author/robert-kugel"> Robert Kugel </a> on <span class="post-date">19 October 2023 </span> </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_Business_Planning_Classifies_and_Rates_Vendors.png?width=200&name=The_Buyers_Guide_for_Business_Planning_Classifies_and_Rates_Vendors.png" width="200" !important: srcset="https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_Business_Planning_Classifies_and_Rates_Vendors.png?width=100&name=The_Buyers_Guide_for_Business_Planning_Classifies_and_Rates_Vendors.png 100w, https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_Business_Planning_Classifies_and_Rates_Vendors.png?width=200&name=The_Buyers_Guide_for_Business_Planning_Classifies_and_Rates_Vendors.png 200w, https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_Business_Planning_Classifies_and_Rates_Vendors.png?width=300&name=The_Buyers_Guide_for_Business_Planning_Classifies_and_Rates_Vendors.png 300w, https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_Business_Planning_Classifies_and_Rates_Vendors.png?width=400&name=The_Buyers_Guide_for_Business_Planning_Classifies_and_Rates_Vendors.png 400w, https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_Business_Planning_Classifies_and_Rates_Vendors.png?width=500&name=The_Buyers_Guide_for_Business_Planning_Classifies_and_Rates_Vendors.png 500w, https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_Business_Planning_Classifies_and_Rates_Vendors.png?width=600&name=The_Buyers_Guide_for_Business_Planning_Classifies_and_Rates_Vendors.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p><span data-contrast="auto">I am happy to share insights gleaned from our latest Buyers Guide, an assessment of how well vendors’ offerings meet buyers’ requirements. The </span><a href="https://www.ventanaresearch.com/buyers_guide/office_of_finance/business_planning_2023"><span data-contrast="none">Ventana Research 2023 Business Planning Buyers Guide</span></a><span data-contrast="auto"> is the distillation of a year of market and product research by Ventana Research. Drawing on our Benchmark Research, we apply a structured methodology built on evaluation categories that reflect the real-world criteria incorporated in a request for proposal to Office of Finance vendors supporting the...</span></p> </div> <a href="https://blog.isg-research.net/the-buyers-guide-for-business-planning-classifies-and-rates-vendors"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://blog.isg-research.net/topic/office-of-finance">Office of Finance</a>, <a href="https://blog.isg-research.net/topic/business-planning">Business Planning</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://blog.isg-research.net/data-pipelines-buyers-guide-market-observations">Data Pipelines Buyers Guide: Market Observations</a></h2> <div class="author-meta"> Posted by <a href="https://blog.isg-research.net/author/matt-aslett"> Matt Aslett </a> on <span class="post-date">19 October 2023 </span> </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://blog.isg-research.net/hs-fs/hubfs/Data_Pipelines_Buyers_Guide_Market_Observations.png?width=200&name=Data_Pipelines_Buyers_Guide_Market_Observations.png" width="200" !important: srcset="https://blog.isg-research.net/hs-fs/hubfs/Data_Pipelines_Buyers_Guide_Market_Observations.png?width=100&name=Data_Pipelines_Buyers_Guide_Market_Observations.png 100w, https://blog.isg-research.net/hs-fs/hubfs/Data_Pipelines_Buyers_Guide_Market_Observations.png?width=200&name=Data_Pipelines_Buyers_Guide_Market_Observations.png 200w, https://blog.isg-research.net/hs-fs/hubfs/Data_Pipelines_Buyers_Guide_Market_Observations.png?width=300&name=Data_Pipelines_Buyers_Guide_Market_Observations.png 300w, https://blog.isg-research.net/hs-fs/hubfs/Data_Pipelines_Buyers_Guide_Market_Observations.png?width=400&name=Data_Pipelines_Buyers_Guide_Market_Observations.png 400w, https://blog.isg-research.net/hs-fs/hubfs/Data_Pipelines_Buyers_Guide_Market_Observations.png?width=500&name=Data_Pipelines_Buyers_Guide_Market_Observations.png 500w, https://blog.isg-research.net/hs-fs/hubfs/Data_Pipelines_Buyers_Guide_Market_Observations.png?width=600&name=Data_Pipelines_Buyers_Guide_Market_Observations.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p><span data-contrast="auto">The 2023 Ventana Research Buyers Guide for Data Pipelines research enables me to provide observations about how the market has advanced. </span></p> </div> <a href="https://blog.isg-research.net/data-pipelines-buyers-guide-market-observations"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://blog.isg-research.net/topic/data">Data</a>, <a href="https://blog.isg-research.net/topic/data-operations">data operations</a>, <a href="https://blog.isg-research.net/topic/data-pipelines">Data Pipelines</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://blog.isg-research.net/business-planning-buyers-guide-market-observations">Business Planning Buyers Guide: Market Observations</a></h2> <div class="author-meta"> Posted by <a href="https://blog.isg-research.net/author/robert-kugel"> Robert Kugel </a> on <span class="post-date">18 October 2023 </span> </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://blog.isg-research.net/hs-fs/hubfs/Business_Planning_Buyers_Guide_Market_Observations.png?width=200&name=Business_Planning_Buyers_Guide_Market_Observations.png" width="200" !important: srcset="https://blog.isg-research.net/hs-fs/hubfs/Business_Planning_Buyers_Guide_Market_Observations.png?width=100&name=Business_Planning_Buyers_Guide_Market_Observations.png 100w, https://blog.isg-research.net/hs-fs/hubfs/Business_Planning_Buyers_Guide_Market_Observations.png?width=200&name=Business_Planning_Buyers_Guide_Market_Observations.png 200w, https://blog.isg-research.net/hs-fs/hubfs/Business_Planning_Buyers_Guide_Market_Observations.png?width=300&name=Business_Planning_Buyers_Guide_Market_Observations.png 300w, https://blog.isg-research.net/hs-fs/hubfs/Business_Planning_Buyers_Guide_Market_Observations.png?width=400&name=Business_Planning_Buyers_Guide_Market_Observations.png 400w, https://blog.isg-research.net/hs-fs/hubfs/Business_Planning_Buyers_Guide_Market_Observations.png?width=500&name=Business_Planning_Buyers_Guide_Market_Observations.png 500w, https://blog.isg-research.net/hs-fs/hubfs/Business_Planning_Buyers_Guide_Market_Observations.png?width=600&name=Business_Planning_Buyers_Guide_Market_Observations.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p><span data-contrast="auto">The 2023 Ventana Research Buyers Guide for Business Planning research enables me to provide observations about how the market has advanced. </span></p> </div> <a href="https://blog.isg-research.net/business-planning-buyers-guide-market-observations"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://blog.isg-research.net/topic/office-of-finance">Office of Finance</a>, <a href="https://blog.isg-research.net/topic/business-planning">Business Planning</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://blog.isg-research.net/the-buyers-guide-for-dataops-classifies-and-rates-vendors">The Buyers Guide for DataOps Classifies and Rates Vendors</a></h2> <div class="author-meta"> Posted by <a href="https://blog.isg-research.net/author/matt-aslett"> Matt Aslett </a> on <span class="post-date">18 October 2023 </span> </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_DataOps_Classifies_and_Rates_Vendors.png?width=200&name=The_Buyers_Guide_for_DataOps_Classifies_and_Rates_Vendors.png" width="200" !important: srcset="https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_DataOps_Classifies_and_Rates_Vendors.png?width=100&name=The_Buyers_Guide_for_DataOps_Classifies_and_Rates_Vendors.png 100w, https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_DataOps_Classifies_and_Rates_Vendors.png?width=200&name=The_Buyers_Guide_for_DataOps_Classifies_and_Rates_Vendors.png 200w, https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_DataOps_Classifies_and_Rates_Vendors.png?width=300&name=The_Buyers_Guide_for_DataOps_Classifies_and_Rates_Vendors.png 300w, https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_DataOps_Classifies_and_Rates_Vendors.png?width=400&name=The_Buyers_Guide_for_DataOps_Classifies_and_Rates_Vendors.png 400w, https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_DataOps_Classifies_and_Rates_Vendors.png?width=500&name=The_Buyers_Guide_for_DataOps_Classifies_and_Rates_Vendors.png 500w, https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_DataOps_Classifies_and_Rates_Vendors.png?width=600&name=The_Buyers_Guide_for_DataOps_Classifies_and_Rates_Vendors.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p><span data-contrast="auto">I am happy to share insights gleaned from our latest Buyers Guide, an assessment of how well vendors’ offerings meet buyers’ requirements. The </span><a href="https://www.ventanaresearch.com/buyers_guide/data/dataops_2023"><span data-contrast="none">Ventana Research 2023 DataOps Buyers Guide</span></a><span data-contrast="auto"> is the distillation of a year of market and product research by Ventana Research. Drawing on our Benchmark Research, we apply a structured methodology built on evaluation categories that reflect the real-world criteria incorporated in a request for proposal to Data vendors supporting the spectrum of DataOps....</span></p> </div> <a href="https://blog.isg-research.net/the-buyers-guide-for-dataops-classifies-and-rates-vendors"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://blog.isg-research.net/topic/data">Data</a>, <a href="https://blog.isg-research.net/topic/data-ops">Data Ops</a>, <a href="https://blog.isg-research.net/topic/data-operations">data operations</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://blog.isg-research.net/databricks-utilizes-unity-catalog-to-support-generative-ai-development">Databricks Utilizes Unity Catalog to Support Generative AI Development</a></h2> <div class="author-meta"> Posted by <a href="https://blog.isg-research.net/author/matt-aslett"> Matt Aslett </a> on <span class="post-date">17 October 2023 </span> </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://blog.isg-research.net/hs-fs/hubfs/Databricks_Utilizes_Unity_Catalog_to_Support_Generative_AI_Development.png?width=200&name=Databricks_Utilizes_Unity_Catalog_to_Support_Generative_AI_Development.png" width="200" !important: srcset="https://blog.isg-research.net/hs-fs/hubfs/Databricks_Utilizes_Unity_Catalog_to_Support_Generative_AI_Development.png?width=100&name=Databricks_Utilizes_Unity_Catalog_to_Support_Generative_AI_Development.png 100w, https://blog.isg-research.net/hs-fs/hubfs/Databricks_Utilizes_Unity_Catalog_to_Support_Generative_AI_Development.png?width=200&name=Databricks_Utilizes_Unity_Catalog_to_Support_Generative_AI_Development.png 200w, https://blog.isg-research.net/hs-fs/hubfs/Databricks_Utilizes_Unity_Catalog_to_Support_Generative_AI_Development.png?width=300&name=Databricks_Utilizes_Unity_Catalog_to_Support_Generative_AI_Development.png 300w, https://blog.isg-research.net/hs-fs/hubfs/Databricks_Utilizes_Unity_Catalog_to_Support_Generative_AI_Development.png?width=400&name=Databricks_Utilizes_Unity_Catalog_to_Support_Generative_AI_Development.png 400w, https://blog.isg-research.net/hs-fs/hubfs/Databricks_Utilizes_Unity_Catalog_to_Support_Generative_AI_Development.png?width=500&name=Databricks_Utilizes_Unity_Catalog_to_Support_Generative_AI_Development.png 500w, https://blog.isg-research.net/hs-fs/hubfs/Databricks_Utilizes_Unity_Catalog_to_Support_Generative_AI_Development.png?width=600&name=Databricks_Utilizes_Unity_Catalog_to_Support_Generative_AI_Development.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p>I <a href="/databricks-lakehouse-platform-maximizes-analytical-value" rel="noopener" target="_blank">previously described</a> how Databricks had positioned its Lakehouse Platform as the basis for data engineering, data science and data warehousing. The lakehouse design pattern provides a flexible environment for storing and processing data from multiple enterprise applications and workloads for multiple use cases. I assert that by 2025, 8 in 10 current data lake adopters will invest in data lakehouse architecture to improve the business value generated from the accumulated data.</p> </div> <a href="https://blog.isg-research.net/databricks-utilizes-unity-catalog-to-support-generative-ai-development"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://blog.isg-research.net/topic/analytics">Analytics</a>, <a href="https://blog.isg-research.net/topic/business-intelligence">Business Intelligence</a>, <a href="https://blog.isg-research.net/topic/data-governance">Data Governance</a>, <a href="https://blog.isg-research.net/topic/data-management">Data Management</a>, <a href="https://blog.isg-research.net/topic/data">Data</a>, <a href="https://blog.isg-research.net/topic/digital-technology">Digital Technology</a>, <a href="https://blog.isg-research.net/topic/analytics-data">Analytics & Data</a>, <a href="https://blog.isg-research.net/topic/analytic-data-platforms">Analytic Data Platforms</a>, <a href="">AI and Machine Learning</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://blog.isg-research.net/the-buyers-guide-for-mobile-analytics-classifies-and-rates-vendors">The Buyers Guide for Mobile Analytics Classifies and Rates Vendors</a></h2> <div class="author-meta"> Posted by <a href="https://blog.isg-research.net/author/david-menninger"> David Menninger </a> on <span class="post-date">17 October 2023 </span> </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_Mobile_Analytics_Classifies_and_Rates_Vendors.png?width=200&name=The_Buyers_Guide_for_Mobile_Analytics_Classifies_and_Rates_Vendors.png" width="200" !important: srcset="https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_Mobile_Analytics_Classifies_and_Rates_Vendors.png?width=100&name=The_Buyers_Guide_for_Mobile_Analytics_Classifies_and_Rates_Vendors.png 100w, https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_Mobile_Analytics_Classifies_and_Rates_Vendors.png?width=200&name=The_Buyers_Guide_for_Mobile_Analytics_Classifies_and_Rates_Vendors.png 200w, https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_Mobile_Analytics_Classifies_and_Rates_Vendors.png?width=300&name=The_Buyers_Guide_for_Mobile_Analytics_Classifies_and_Rates_Vendors.png 300w, https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_Mobile_Analytics_Classifies_and_Rates_Vendors.png?width=400&name=The_Buyers_Guide_for_Mobile_Analytics_Classifies_and_Rates_Vendors.png 400w, https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_Mobile_Analytics_Classifies_and_Rates_Vendors.png?width=500&name=The_Buyers_Guide_for_Mobile_Analytics_Classifies_and_Rates_Vendors.png 500w, https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_Mobile_Analytics_Classifies_and_Rates_Vendors.png?width=600&name=The_Buyers_Guide_for_Mobile_Analytics_Classifies_and_Rates_Vendors.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p><span data-contrast="auto">I am happy to share insights gleaned from our latest Buyers Guide, an assessment of how well vendors’ offerings meet buyers’ requirements. The </span><a href="https://www.ventanaresearch.com/buyers_guide/analytics/mobile_analytics_2023"><span data-contrast="none">Ventana Research 2023 Mobile Analytics Buyers Guide</span></a><span data-contrast="auto"> is the distillation of a year of market and product research by Ventana Research. Drawing on our Benchmark Research, we apply a structured methodology built on evaluation categories that reflect the real-world criteria incorporated in a request for proposal to Analytics vendors supporting the spectrum...</span></p> </div> <a href="https://blog.isg-research.net/the-buyers-guide-for-mobile-analytics-classifies-and-rates-vendors"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://blog.isg-research.net/topic/analytics">Analytics</a>, <a href="https://blog.isg-research.net/topic/mobile-analytics">Mobile Analytics</a>, <a href="https://blog.isg-research.net/topic/analytics-data">Analytics & Data</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://blog.isg-research.net/visier-extends-people-analytics-beyond-the-data-analyst-and-scientist">Visier Extends People Analytics Beyond the Data Analyst and Scientist</a></h2> <div class="author-meta"> Posted by <a href="https://blog.isg-research.net/author/isg-software-research"> ISG Software Research </a> on <span class="post-date">17 October 2023 </span> </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://blog.isg-research.net/hs-fs/hubfs/Visier_Extends_People_Analytics.png?width=200&name=Visier_Extends_People_Analytics.png" width="200" !important: srcset="https://blog.isg-research.net/hs-fs/hubfs/Visier_Extends_People_Analytics.png?width=100&name=Visier_Extends_People_Analytics.png 100w, https://blog.isg-research.net/hs-fs/hubfs/Visier_Extends_People_Analytics.png?width=200&name=Visier_Extends_People_Analytics.png 200w, https://blog.isg-research.net/hs-fs/hubfs/Visier_Extends_People_Analytics.png?width=300&name=Visier_Extends_People_Analytics.png 300w, https://blog.isg-research.net/hs-fs/hubfs/Visier_Extends_People_Analytics.png?width=400&name=Visier_Extends_People_Analytics.png 400w, https://blog.isg-research.net/hs-fs/hubfs/Visier_Extends_People_Analytics.png?width=500&name=Visier_Extends_People_Analytics.png 500w, https://blog.isg-research.net/hs-fs/hubfs/Visier_Extends_People_Analytics.png?width=600&name=Visier_Extends_People_Analytics.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p>Human resources has long been stereotyped as the department that relies on intuition and experience in decision-making. Enterprises have evolved to understand the importance of their workforce to achieving business outcomes and not just as a line item in the expense column of a balance sheet, earning HR executives a seat in Lin Manuel Miranda’s eponymous “room where it happens.” In doing so, those very executives must be armed with the data that ties worker performance to business outcomes;...</p> </div> <a href="https://blog.isg-research.net/visier-extends-people-analytics-beyond-the-data-analyst-and-scientist"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://blog.isg-research.net/topic/human-capital-management">Human Capital Management</a>, <a href="https://blog.isg-research.net/topic/workforce-management">Workforce Management</a>, <a href="https://blog.isg-research.net/topic/total-compensation-management">Total Compensation Management</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://blog.isg-research.net/dataops-buyers-guide-market-observations">DataOps Buyers Guide: Market Observations</a></h2> <div class="author-meta"> Posted by <a href="https://blog.isg-research.net/author/matt-aslett"> Matt Aslett </a> on <span class="post-date">16 October 2023 </span> </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://blog.isg-research.net/hs-fs/hubfs/DataOps_Buyers_Guide_Market_Observations.png?width=200&name=DataOps_Buyers_Guide_Market_Observations.png" width="200" !important: srcset="https://blog.isg-research.net/hs-fs/hubfs/DataOps_Buyers_Guide_Market_Observations.png?width=100&name=DataOps_Buyers_Guide_Market_Observations.png 100w, https://blog.isg-research.net/hs-fs/hubfs/DataOps_Buyers_Guide_Market_Observations.png?width=200&name=DataOps_Buyers_Guide_Market_Observations.png 200w, https://blog.isg-research.net/hs-fs/hubfs/DataOps_Buyers_Guide_Market_Observations.png?width=300&name=DataOps_Buyers_Guide_Market_Observations.png 300w, https://blog.isg-research.net/hs-fs/hubfs/DataOps_Buyers_Guide_Market_Observations.png?width=400&name=DataOps_Buyers_Guide_Market_Observations.png 400w, https://blog.isg-research.net/hs-fs/hubfs/DataOps_Buyers_Guide_Market_Observations.png?width=500&name=DataOps_Buyers_Guide_Market_Observations.png 500w, https://blog.isg-research.net/hs-fs/hubfs/DataOps_Buyers_Guide_Market_Observations.png?width=600&name=DataOps_Buyers_Guide_Market_Observations.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p><span data-contrast="auto">The 2023 Ventana Research Buyers Guide for DataOps research enables me to provide observations about how the market has advanced. </span></p> </div> <a href="https://blog.isg-research.net/dataops-buyers-guide-market-observations"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://blog.isg-research.net/topic/data">Data</a>, <a href="https://blog.isg-research.net/topic/data-ops">Data Ops</a>, <a href="https://blog.isg-research.net/topic/data-operations">data operations</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://blog.isg-research.net/mobile-analytics-buyers-guide-market-observations">Mobile Analytics Buyers Guide: Market Observations</a></h2> <div class="author-meta"> Posted by <a href="https://blog.isg-research.net/author/david-menninger"> David Menninger </a> on <span class="post-date">16 October 2023 </span> </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://blog.isg-research.net/hs-fs/hubfs/Mobile_Analytics_Buyers_Guide_Market_Observations.png?width=200&name=Mobile_Analytics_Buyers_Guide_Market_Observations.png" width="200" !important: srcset="https://blog.isg-research.net/hs-fs/hubfs/Mobile_Analytics_Buyers_Guide_Market_Observations.png?width=100&name=Mobile_Analytics_Buyers_Guide_Market_Observations.png 100w, https://blog.isg-research.net/hs-fs/hubfs/Mobile_Analytics_Buyers_Guide_Market_Observations.png?width=200&name=Mobile_Analytics_Buyers_Guide_Market_Observations.png 200w, https://blog.isg-research.net/hs-fs/hubfs/Mobile_Analytics_Buyers_Guide_Market_Observations.png?width=300&name=Mobile_Analytics_Buyers_Guide_Market_Observations.png 300w, https://blog.isg-research.net/hs-fs/hubfs/Mobile_Analytics_Buyers_Guide_Market_Observations.png?width=400&name=Mobile_Analytics_Buyers_Guide_Market_Observations.png 400w, https://blog.isg-research.net/hs-fs/hubfs/Mobile_Analytics_Buyers_Guide_Market_Observations.png?width=500&name=Mobile_Analytics_Buyers_Guide_Market_Observations.png 500w, https://blog.isg-research.net/hs-fs/hubfs/Mobile_Analytics_Buyers_Guide_Market_Observations.png?width=600&name=Mobile_Analytics_Buyers_Guide_Market_Observations.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p><span data-contrast="auto">The 2023 Ventana Research Buyers Guide for Mobile Analytics research enables me to provide observations about how the market has advanced. </span></p> </div> <a href="https://blog.isg-research.net/mobile-analytics-buyers-guide-market-observations"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://blog.isg-research.net/topic/analytics">Analytics</a>, <a href="https://blog.isg-research.net/topic/mobile-analytics">Mobile Analytics</a>, <a href="https://blog.isg-research.net/topic/analytics-data">Analytics & Data</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://blog.isg-research.net/the-buyers-guide-for-collaborative-analytics-classifies-and-rates-vendors">The Buyers Guide for Collaborative Analytics Classifies and Rates Vendors</a></h2> <div class="author-meta"> Posted by <a href="https://blog.isg-research.net/author/david-menninger"> David Menninger </a> on <span class="post-date">13 October 2023 </span> </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_Collaborative_Analytics_Classifies_and_Rates_Vendors.png?width=200&name=The_Buyers_Guide_for_Collaborative_Analytics_Classifies_and_Rates_Vendors.png" width="200" !important: srcset="https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_Collaborative_Analytics_Classifies_and_Rates_Vendors.png?width=100&name=The_Buyers_Guide_for_Collaborative_Analytics_Classifies_and_Rates_Vendors.png 100w, https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_Collaborative_Analytics_Classifies_and_Rates_Vendors.png?width=200&name=The_Buyers_Guide_for_Collaborative_Analytics_Classifies_and_Rates_Vendors.png 200w, https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_Collaborative_Analytics_Classifies_and_Rates_Vendors.png?width=300&name=The_Buyers_Guide_for_Collaborative_Analytics_Classifies_and_Rates_Vendors.png 300w, https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_Collaborative_Analytics_Classifies_and_Rates_Vendors.png?width=400&name=The_Buyers_Guide_for_Collaborative_Analytics_Classifies_and_Rates_Vendors.png 400w, https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_Collaborative_Analytics_Classifies_and_Rates_Vendors.png?width=500&name=The_Buyers_Guide_for_Collaborative_Analytics_Classifies_and_Rates_Vendors.png 500w, https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_Collaborative_Analytics_Classifies_and_Rates_Vendors.png?width=600&name=The_Buyers_Guide_for_Collaborative_Analytics_Classifies_and_Rates_Vendors.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p><span data-contrast="auto">I am happy to share insights gleaned from our latest Buyers Guide, an assessment of how well vendors’ offerings meet buyers’ requirements. The </span><a href="https://www.ventanaresearch.com/buyers_guide/analytics/collaborative_analytics_2023"><span data-contrast="none">Ventana Research 2023 Collaborative Analytics Buyers Guide</span></a><span data-contrast="auto"> is the distillation of a year of market and product research by Ventana Research. Drawing on our Benchmark Research, we apply a structured methodology built on evaluation categories that reflect the real-world criteria incorporated in a request for proposal to Analytics vendors supporting the...</span></p> </div> <a href="https://blog.isg-research.net/the-buyers-guide-for-collaborative-analytics-classifies-and-rates-vendors"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://blog.isg-research.net/topic/analytics">Analytics</a>, <a href="https://blog.isg-research.net/topic/collaborative-analytics">Collaborative Analytics</a>, <a href="https://blog.isg-research.net/topic/analytics-data">Analytics & Data</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://blog.isg-research.net/collaborative-analytics-buyers-guide-market-observations">Collaborative Analytics Buyers Guide: Market Observations</a></h2> <div class="author-meta"> Posted by <a href="https://blog.isg-research.net/author/david-menninger"> David Menninger </a> on <span class="post-date">12 October 2023 </span> </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://blog.isg-research.net/hs-fs/hubfs/Collaborative_Analytics_Buyers_Guide_Market_Observations.png?width=200&name=Collaborative_Analytics_Buyers_Guide_Market_Observations.png" width="200" !important: srcset="https://blog.isg-research.net/hs-fs/hubfs/Collaborative_Analytics_Buyers_Guide_Market_Observations.png?width=100&name=Collaborative_Analytics_Buyers_Guide_Market_Observations.png 100w, https://blog.isg-research.net/hs-fs/hubfs/Collaborative_Analytics_Buyers_Guide_Market_Observations.png?width=200&name=Collaborative_Analytics_Buyers_Guide_Market_Observations.png 200w, https://blog.isg-research.net/hs-fs/hubfs/Collaborative_Analytics_Buyers_Guide_Market_Observations.png?width=300&name=Collaborative_Analytics_Buyers_Guide_Market_Observations.png 300w, https://blog.isg-research.net/hs-fs/hubfs/Collaborative_Analytics_Buyers_Guide_Market_Observations.png?width=400&name=Collaborative_Analytics_Buyers_Guide_Market_Observations.png 400w, https://blog.isg-research.net/hs-fs/hubfs/Collaborative_Analytics_Buyers_Guide_Market_Observations.png?width=500&name=Collaborative_Analytics_Buyers_Guide_Market_Observations.png 500w, https://blog.isg-research.net/hs-fs/hubfs/Collaborative_Analytics_Buyers_Guide_Market_Observations.png?width=600&name=Collaborative_Analytics_Buyers_Guide_Market_Observations.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p><span data-contrast="auto">The 2023 Ventana Research Buyers Guide for Collaborative Analytics research enables me to provide observations about how the market has advanced. </span></p> </div> <a href="https://blog.isg-research.net/collaborative-analytics-buyers-guide-market-observations"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://blog.isg-research.net/topic/analytics">Analytics</a>, <a href="https://blog.isg-research.net/topic/collaborative-analytics">Collaborative Analytics</a>, <a href="https://blog.isg-research.net/topic/analytics-data">Analytics & Data</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://blog.isg-research.net/cultivating-contact-center-customer-experience-thinking-for-success">Cultivating Contact Center, Customer Experience Thinking for Success</a></h2> <div class="author-meta"> Posted by <a href="https://blog.isg-research.net/author/keith-dawson"> Keith Dawson </a> on <span class="post-date">12 October 2023 </span> </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://blog.isg-research.net/hs-fs/hubfs/Cultivating_Contact_Center_Customer_Experience_Thinking_for_Success.png?width=200&name=Cultivating_Contact_Center_Customer_Experience_Thinking_for_Success.png" width="200" !important: srcset="https://blog.isg-research.net/hs-fs/hubfs/Cultivating_Contact_Center_Customer_Experience_Thinking_for_Success.png?width=100&name=Cultivating_Contact_Center_Customer_Experience_Thinking_for_Success.png 100w, https://blog.isg-research.net/hs-fs/hubfs/Cultivating_Contact_Center_Customer_Experience_Thinking_for_Success.png?width=200&name=Cultivating_Contact_Center_Customer_Experience_Thinking_for_Success.png 200w, https://blog.isg-research.net/hs-fs/hubfs/Cultivating_Contact_Center_Customer_Experience_Thinking_for_Success.png?width=300&name=Cultivating_Contact_Center_Customer_Experience_Thinking_for_Success.png 300w, https://blog.isg-research.net/hs-fs/hubfs/Cultivating_Contact_Center_Customer_Experience_Thinking_for_Success.png?width=400&name=Cultivating_Contact_Center_Customer_Experience_Thinking_for_Success.png 400w, https://blog.isg-research.net/hs-fs/hubfs/Cultivating_Contact_Center_Customer_Experience_Thinking_for_Success.png?width=500&name=Cultivating_Contact_Center_Customer_Experience_Thinking_for_Success.png 500w, https://blog.isg-research.net/hs-fs/hubfs/Cultivating_Contact_Center_Customer_Experience_Thinking_for_Success.png?width=600&name=Cultivating_Contact_Center_Customer_Experience_Thinking_for_Success.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p>The environment around customer experience is being disrupted by a series of technical and organizational shifts ranging from artificial intelligence to work-from-home to deep customer analytics. When times change, it’s helpful for people to have a framework to organize their thinking and actions. I believe there are two dominant frameworks used to organize business approaches to customers. One is rooted in the efficiency of the contact center (let’s call it “CC thinking”), and the more...</p> </div> <a href="https://blog.isg-research.net/cultivating-contact-center-customer-experience-thinking-for-success"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://blog.isg-research.net/topic/customer-experience">Customer Experience</a>, <a href="https://blog.isg-research.net/topic/voice-of-the-customer">Voice of the Customer</a>, <a href="https://blog.isg-research.net/topic/contact-center">Contact Center</a>, <a href="https://blog.isg-research.net/topic/agent-management">agent management</a>, <a href="https://blog.isg-research.net/topic/customer-experience-management">Customer Experience Management</a>, <a href="https://blog.isg-research.net/topic/intelligent-self-service">Intelligent Self-Service</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://blog.isg-research.net/align-early-career-hcm-technology-with-digital-first-generation">Align Early-Career HCM Technology with Digital-First Generation</a></h2> <div class="author-meta"> Posted by <a href="https://blog.isg-research.net/author/isg-software-research"> ISG Software Research </a> on <span class="post-date">11 October 2023 </span> </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://blog.isg-research.net/hs-fs/hubfs/Align_Early-Career_HCM_Technology.png?width=200&name=Align_Early-Career_HCM_Technology.png" width="200" !important: srcset="https://blog.isg-research.net/hs-fs/hubfs/Align_Early-Career_HCM_Technology.png?width=100&name=Align_Early-Career_HCM_Technology.png 100w, https://blog.isg-research.net/hs-fs/hubfs/Align_Early-Career_HCM_Technology.png?width=200&name=Align_Early-Career_HCM_Technology.png 200w, https://blog.isg-research.net/hs-fs/hubfs/Align_Early-Career_HCM_Technology.png?width=300&name=Align_Early-Career_HCM_Technology.png 300w, https://blog.isg-research.net/hs-fs/hubfs/Align_Early-Career_HCM_Technology.png?width=400&name=Align_Early-Career_HCM_Technology.png 400w, https://blog.isg-research.net/hs-fs/hubfs/Align_Early-Career_HCM_Technology.png?width=500&name=Align_Early-Career_HCM_Technology.png 500w, https://blog.isg-research.net/hs-fs/hubfs/Align_Early-Career_HCM_Technology.png?width=600&name=Align_Early-Career_HCM_Technology.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p>When my father entered the workforce in the 1950s, his path was relatively clear. He graduated high school, went to work in his chosen trade, did a stint in the Navy Reserves, and continued in the same or adjacent career until he retired. The same was true for my uncle, who chose to go to college but joined a firm working in his field, worked hard to be promoted, and changed companies one time before retiring as an executive. But the nature of work and job-hunting has undergone a radical...</p> </div> <a href="https://blog.isg-research.net/align-early-career-hcm-technology-with-digital-first-generation"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://blog.isg-research.net/topic/human-capital-management">Human Capital Management</a>, <a href="https://blog.isg-research.net/topic/talent-management">Talent Management</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://blog.isg-research.net/the-buyers-guide-for-embedded-analytics-classifies-and-rates-vendors">The Buyers Guide for Embedded Analytics Classifies and Rates Vendors</a></h2> <div class="author-meta"> Posted by <a href="https://blog.isg-research.net/author/david-menninger"> David Menninger </a> on <span class="post-date">11 October 2023 </span> </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_Embedded_Analytics_Classifies_and_Rates_Vendors.png?width=200&name=The_Buyers_Guide_for_Embedded_Analytics_Classifies_and_Rates_Vendors.png" width="200" !important: srcset="https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_Embedded_Analytics_Classifies_and_Rates_Vendors.png?width=100&name=The_Buyers_Guide_for_Embedded_Analytics_Classifies_and_Rates_Vendors.png 100w, https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_Embedded_Analytics_Classifies_and_Rates_Vendors.png?width=200&name=The_Buyers_Guide_for_Embedded_Analytics_Classifies_and_Rates_Vendors.png 200w, https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_Embedded_Analytics_Classifies_and_Rates_Vendors.png?width=300&name=The_Buyers_Guide_for_Embedded_Analytics_Classifies_and_Rates_Vendors.png 300w, https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_Embedded_Analytics_Classifies_and_Rates_Vendors.png?width=400&name=The_Buyers_Guide_for_Embedded_Analytics_Classifies_and_Rates_Vendors.png 400w, https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_Embedded_Analytics_Classifies_and_Rates_Vendors.png?width=500&name=The_Buyers_Guide_for_Embedded_Analytics_Classifies_and_Rates_Vendors.png 500w, https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_Embedded_Analytics_Classifies_and_Rates_Vendors.png?width=600&name=The_Buyers_Guide_for_Embedded_Analytics_Classifies_and_Rates_Vendors.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p><span data-contrast="auto">I am happy to share insights gleaned from our latest Buyers Guide, an assessment of how well vendors’ offerings meet buyers’ requirements. The </span><a href="https://www.ventanaresearch.com/buyers_guide/analytics/embedded_analytics_2023"><span data-contrast="none">Ventana Research 2023 Embedded Analytics Buyers Guide</span></a><span data-contrast="auto"> is the distillation of a year of market and product research by Ventana Research. Drawing on our Benchmark Research, we apply a structured methodology built on evaluation categories that reflect the real-world criteria incorporated in a request for proposal to Analytics vendors supporting the spectrum...</span></p> </div> <a href="https://blog.isg-research.net/the-buyers-guide-for-embedded-analytics-classifies-and-rates-vendors"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://blog.isg-research.net/topic/embedded-analytics">embedded analytics</a>, <a href="https://blog.isg-research.net/topic/analytics">Analytics</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://blog.isg-research.net/embedded-analytics-buyers-guide-market-observations">Embedded Analytics Buyers Guide: Market Observations</a></h2> <div class="author-meta"> Posted by <a href="https://blog.isg-research.net/author/david-menninger"> David Menninger </a> on <span class="post-date">10 October 2023 </span> </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://blog.isg-research.net/hs-fs/hubfs/Embedded_Analytics_Buyers_Guide_Market_Observations.png?width=200&name=Embedded_Analytics_Buyers_Guide_Market_Observations.png" width="200" !important: srcset="https://blog.isg-research.net/hs-fs/hubfs/Embedded_Analytics_Buyers_Guide_Market_Observations.png?width=100&name=Embedded_Analytics_Buyers_Guide_Market_Observations.png 100w, https://blog.isg-research.net/hs-fs/hubfs/Embedded_Analytics_Buyers_Guide_Market_Observations.png?width=200&name=Embedded_Analytics_Buyers_Guide_Market_Observations.png 200w, https://blog.isg-research.net/hs-fs/hubfs/Embedded_Analytics_Buyers_Guide_Market_Observations.png?width=300&name=Embedded_Analytics_Buyers_Guide_Market_Observations.png 300w, https://blog.isg-research.net/hs-fs/hubfs/Embedded_Analytics_Buyers_Guide_Market_Observations.png?width=400&name=Embedded_Analytics_Buyers_Guide_Market_Observations.png 400w, https://blog.isg-research.net/hs-fs/hubfs/Embedded_Analytics_Buyers_Guide_Market_Observations.png?width=500&name=Embedded_Analytics_Buyers_Guide_Market_Observations.png 500w, https://blog.isg-research.net/hs-fs/hubfs/Embedded_Analytics_Buyers_Guide_Market_Observations.png?width=600&name=Embedded_Analytics_Buyers_Guide_Market_Observations.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p><span data-contrast="auto">The 2023 Ventana Research Buyers Guide for Embedded Analytics research enables me to provide observations about how the market has advanced. </span></p> </div> <a href="https://blog.isg-research.net/embedded-analytics-buyers-guide-market-observations"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://blog.isg-research.net/topic/embedded-analytics">embedded analytics</a>, <a href="https://blog.isg-research.net/topic/analytics">Analytics</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://blog.isg-research.net/the-buyers-guide-for-augmented-analytics-classifies-and-rates-vendors">The Buyers Guide for Augmented Analytics Classifies and Rates Vendors</a></h2> <div class="author-meta"> Posted by <a href="https://blog.isg-research.net/author/david-menninger"> David Menninger </a> on <span class="post-date">06 October 2023 </span> </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_Augmented_Analytics_Classifies_and_Rates_Vendors.png?width=200&name=The_Buyers_Guide_for_Augmented_Analytics_Classifies_and_Rates_Vendors.png" width="200" !important: srcset="https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_Augmented_Analytics_Classifies_and_Rates_Vendors.png?width=100&name=The_Buyers_Guide_for_Augmented_Analytics_Classifies_and_Rates_Vendors.png 100w, https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_Augmented_Analytics_Classifies_and_Rates_Vendors.png?width=200&name=The_Buyers_Guide_for_Augmented_Analytics_Classifies_and_Rates_Vendors.png 200w, https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_Augmented_Analytics_Classifies_and_Rates_Vendors.png?width=300&name=The_Buyers_Guide_for_Augmented_Analytics_Classifies_and_Rates_Vendors.png 300w, https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_Augmented_Analytics_Classifies_and_Rates_Vendors.png?width=400&name=The_Buyers_Guide_for_Augmented_Analytics_Classifies_and_Rates_Vendors.png 400w, https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_Augmented_Analytics_Classifies_and_Rates_Vendors.png?width=500&name=The_Buyers_Guide_for_Augmented_Analytics_Classifies_and_Rates_Vendors.png 500w, https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_Augmented_Analytics_Classifies_and_Rates_Vendors.png?width=600&name=The_Buyers_Guide_for_Augmented_Analytics_Classifies_and_Rates_Vendors.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p><span data-contrast="auto">I am happy to share insights gleaned from our latest Buyers Guide, an assessment of how well vendors’ offerings meet buyers’ requirements. The </span><a href="https://www.ventanaresearch.com/buyers_guide/analytics/augmented_analytics_2023"><span data-contrast="none">Ventana Research 2023 Augmented Analytics Buyers Guide</span></a><span data-contrast="auto"> is the distillation of a year of market and product research by Ventana Research. Drawing on our Benchmark Research, we apply a structured methodology built on evaluation categories that reflect the real-world criteria incorporated in a request for proposal to Analytics and Data vendors supporting...</span></p> </div> <a href="https://blog.isg-research.net/the-buyers-guide-for-augmented-analytics-classifies-and-rates-vendors"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://blog.isg-research.net/topic/analytics">Analytics</a>, <a href="">AI and Machine Learning</a>, <a href="https://blog.isg-research.net/topic/augmented-analytics">Augmented Analytics</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://blog.isg-research.net/augmented-analytics-buyers-guide-2023-market-observations">Augmented Analytics Buyers Guide 2023: Market Observations</a></h2> <div class="author-meta"> Posted by <a href="https://blog.isg-research.net/author/david-menninger"> David Menninger </a> on <span class="post-date">05 October 2023 </span> </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://blog.isg-research.net/hs-fs/hubfs/Augmented_Analytics_Buyers_Guide_2023_Market_Observations.png?width=200&name=Augmented_Analytics_Buyers_Guide_2023_Market_Observations.png" width="200" !important: srcset="https://blog.isg-research.net/hs-fs/hubfs/Augmented_Analytics_Buyers_Guide_2023_Market_Observations.png?width=100&name=Augmented_Analytics_Buyers_Guide_2023_Market_Observations.png 100w, https://blog.isg-research.net/hs-fs/hubfs/Augmented_Analytics_Buyers_Guide_2023_Market_Observations.png?width=200&name=Augmented_Analytics_Buyers_Guide_2023_Market_Observations.png 200w, https://blog.isg-research.net/hs-fs/hubfs/Augmented_Analytics_Buyers_Guide_2023_Market_Observations.png?width=300&name=Augmented_Analytics_Buyers_Guide_2023_Market_Observations.png 300w, https://blog.isg-research.net/hs-fs/hubfs/Augmented_Analytics_Buyers_Guide_2023_Market_Observations.png?width=400&name=Augmented_Analytics_Buyers_Guide_2023_Market_Observations.png 400w, https://blog.isg-research.net/hs-fs/hubfs/Augmented_Analytics_Buyers_Guide_2023_Market_Observations.png?width=500&name=Augmented_Analytics_Buyers_Guide_2023_Market_Observations.png 500w, https://blog.isg-research.net/hs-fs/hubfs/Augmented_Analytics_Buyers_Guide_2023_Market_Observations.png?width=600&name=Augmented_Analytics_Buyers_Guide_2023_Market_Observations.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p><span data-contrast="auto">The 2023 Ventana Research Buyers Guide for Augmented Analytics research enables me to provide observations about how the market has advanced. </span></p> </div> <a href="https://blog.isg-research.net/augmented-analytics-buyers-guide-2023-market-observations"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://blog.isg-research.net/topic/analytics">Analytics</a>, <a href="">AI and Machine Learning</a>, <a href="https://blog.isg-research.net/topic/augmented-analytics">Augmented Analytics</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://blog.isg-research.net/infoblox-ddi-boosts-network-security-resilience">Infoblox DDI Boosts Network Security Resilience</a></h2> <div class="author-meta"> Posted by <a href="https://blog.isg-research.net/author/jeff-orr"> Jeff Orr </a> on <span class="post-date">05 October 2023 </span> </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://blog.isg-research.net/hs-fs/hubfs/Infoblox_DDI_Boosts_Network_Security_Resilience.png?width=200&name=Infoblox_DDI_Boosts_Network_Security_Resilience.png" width="200" !important: srcset="https://blog.isg-research.net/hs-fs/hubfs/Infoblox_DDI_Boosts_Network_Security_Resilience.png?width=100&name=Infoblox_DDI_Boosts_Network_Security_Resilience.png 100w, https://blog.isg-research.net/hs-fs/hubfs/Infoblox_DDI_Boosts_Network_Security_Resilience.png?width=200&name=Infoblox_DDI_Boosts_Network_Security_Resilience.png 200w, https://blog.isg-research.net/hs-fs/hubfs/Infoblox_DDI_Boosts_Network_Security_Resilience.png?width=300&name=Infoblox_DDI_Boosts_Network_Security_Resilience.png 300w, https://blog.isg-research.net/hs-fs/hubfs/Infoblox_DDI_Boosts_Network_Security_Resilience.png?width=400&name=Infoblox_DDI_Boosts_Network_Security_Resilience.png 400w, https://blog.isg-research.net/hs-fs/hubfs/Infoblox_DDI_Boosts_Network_Security_Resilience.png?width=500&name=Infoblox_DDI_Boosts_Network_Security_Resilience.png 500w, https://blog.isg-research.net/hs-fs/hubfs/Infoblox_DDI_Boosts_Network_Security_Resilience.png?width=600&name=Infoblox_DDI_Boosts_Network_Security_Resilience.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p>In today’s rapidly evolving digital security landscape, organizations face the challenge of maintaining robust network security resilience. Chief information officers battling increasingly sophisticated cyber threats and the growing complexity of enterprise networks must enhance network security posture with advanced technologies to stay ahead of potential threats. One such technology is DDI – <a href="https://jefforr.ventanaresearch.com/build-network-security-resilience-with-ddi-benefits-and-challenges" rel="noopener" target="_blank">the integration of DNS, DHCP and IPAM</a>.</p> </div> <a href="https://blog.isg-research.net/infoblox-ddi-boosts-network-security-resilience"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://blog.isg-research.net/topic/digital-business">Digital Business</a>, <a href="https://blog.isg-research.net/topic/digital-security">Digital Security</a>, <a href="https://blog.isg-research.net/topic/resilience">Resilience</a>, <a href="https://blog.isg-research.net/topic/ddi">DDI</a>, <a href="https://blog.isg-research.net/topic/dns">DNS</a>, <a href="https://blog.isg-research.net/topic/dhcp">DHCP</a>, <a href="https://blog.isg-research.net/topic/ipam">IPAM</a>, <a href="https://blog.isg-research.net/topic/security-posture">Security Posture</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://blog.isg-research.net/the-buyers-guide-for-analytics-and-data-classifies-and-rates-vendors">The Buyers Guide for Analytics and Data Classifies and Rates Vendors</a></h2> <div class="author-meta"> Posted by <a href="https://blog.isg-research.net/author/david-menninger"> David Menninger </a> on <span class="post-date">04 October 2023 </span> </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_Analytics_and_Data_Classifies_and_Rates_Vendors.png?width=200&name=The_Buyers_Guide_for_Analytics_and_Data_Classifies_and_Rates_Vendors.png" width="200" !important: srcset="https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_Analytics_and_Data_Classifies_and_Rates_Vendors.png?width=100&name=The_Buyers_Guide_for_Analytics_and_Data_Classifies_and_Rates_Vendors.png 100w, https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_Analytics_and_Data_Classifies_and_Rates_Vendors.png?width=200&name=The_Buyers_Guide_for_Analytics_and_Data_Classifies_and_Rates_Vendors.png 200w, https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_Analytics_and_Data_Classifies_and_Rates_Vendors.png?width=300&name=The_Buyers_Guide_for_Analytics_and_Data_Classifies_and_Rates_Vendors.png 300w, https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_Analytics_and_Data_Classifies_and_Rates_Vendors.png?width=400&name=The_Buyers_Guide_for_Analytics_and_Data_Classifies_and_Rates_Vendors.png 400w, https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_Analytics_and_Data_Classifies_and_Rates_Vendors.png?width=500&name=The_Buyers_Guide_for_Analytics_and_Data_Classifies_and_Rates_Vendors.png 500w, https://blog.isg-research.net/hs-fs/hubfs/The_Buyers_Guide_for_Analytics_and_Data_Classifies_and_Rates_Vendors.png?width=600&name=The_Buyers_Guide_for_Analytics_and_Data_Classifies_and_Rates_Vendors.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p><span data-contrast="auto">I am happy to share insights gleaned from our latest Buyers Guide, an assessment of how well vendors’ offerings meet buyers’ requirements. The </span><a href="https://www.ventanaresearch.com/buyers_guide/analytics/analytics_and_data_2023"><span data-contrast="none">Ventana Research 2023 Analytics and Data Buyers Guide</span></a><span data-contrast="auto"> is the distillation of a year of market and product research by Ventana Research. Drawing on our Benchmark Research, we apply a structured methodology built on evaluation categories that reflect the real-world criteria incorporated in a request for proposal to Analytics vendors supporting the spectrum...</span></p> </div> <a href="https://blog.isg-research.net/the-buyers-guide-for-analytics-and-data-classifies-and-rates-vendors"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://blog.isg-research.net/topic/analytics">Analytics</a>, <a href="https://blog.isg-research.net/topic/analytics-data">Analytics & Data</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://blog.isg-research.net/analytics-and-data-buyers-guide-2023-market-observations">Analytics and Data Buyers Guide 2023: Market Observations</a></h2> <div class="author-meta"> Posted by <a href="https://blog.isg-research.net/author/david-menninger"> David Menninger </a> on <span class="post-date">03 October 2023 </span> </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://blog.isg-research.net/hs-fs/hubfs/Analytics_and_Data_Buyers_Guide_2023_Market_Observations.png?width=200&name=Analytics_and_Data_Buyers_Guide_2023_Market_Observations.png" width="200" !important: srcset="https://blog.isg-research.net/hs-fs/hubfs/Analytics_and_Data_Buyers_Guide_2023_Market_Observations.png?width=100&name=Analytics_and_Data_Buyers_Guide_2023_Market_Observations.png 100w, https://blog.isg-research.net/hs-fs/hubfs/Analytics_and_Data_Buyers_Guide_2023_Market_Observations.png?width=200&name=Analytics_and_Data_Buyers_Guide_2023_Market_Observations.png 200w, https://blog.isg-research.net/hs-fs/hubfs/Analytics_and_Data_Buyers_Guide_2023_Market_Observations.png?width=300&name=Analytics_and_Data_Buyers_Guide_2023_Market_Observations.png 300w, https://blog.isg-research.net/hs-fs/hubfs/Analytics_and_Data_Buyers_Guide_2023_Market_Observations.png?width=400&name=Analytics_and_Data_Buyers_Guide_2023_Market_Observations.png 400w, https://blog.isg-research.net/hs-fs/hubfs/Analytics_and_Data_Buyers_Guide_2023_Market_Observations.png?width=500&name=Analytics_and_Data_Buyers_Guide_2023_Market_Observations.png 500w, https://blog.isg-research.net/hs-fs/hubfs/Analytics_and_Data_Buyers_Guide_2023_Market_Observations.png?width=600&name=Analytics_and_Data_Buyers_Guide_2023_Market_Observations.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p>The 2023 Ventana Research Buyers Guide for Analytics and Data research enables me to provide observations about how the market has advanced.</p> </div> <a href="https://blog.isg-research.net/analytics-and-data-buyers-guide-2023-market-observations"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://blog.isg-research.net/topic/analytics">Analytics</a>, <a href="https://blog.isg-research.net/topic/analytics-data">Analytics & Data</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://blog.isg-research.net/build-network-security-resilience-with-ddi-benefits-and-challenges">Build Network Security Resilience with DDI: Benefits and Challenges</a></h2> <div class="author-meta"> Posted by <a href="https://blog.isg-research.net/author/jeff-orr"> Jeff Orr </a> on <span class="post-date">03 October 2023 </span> </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://blog.isg-research.net/hs-fs/hubfs/Build_Network_Security_Resilience_with_DDI_Benefits_and_Challenges.png?width=200&name=Build_Network_Security_Resilience_with_DDI_Benefits_and_Challenges.png" width="200" !important: srcset="https://blog.isg-research.net/hs-fs/hubfs/Build_Network_Security_Resilience_with_DDI_Benefits_and_Challenges.png?width=100&name=Build_Network_Security_Resilience_with_DDI_Benefits_and_Challenges.png 100w, https://blog.isg-research.net/hs-fs/hubfs/Build_Network_Security_Resilience_with_DDI_Benefits_and_Challenges.png?width=200&name=Build_Network_Security_Resilience_with_DDI_Benefits_and_Challenges.png 200w, https://blog.isg-research.net/hs-fs/hubfs/Build_Network_Security_Resilience_with_DDI_Benefits_and_Challenges.png?width=300&name=Build_Network_Security_Resilience_with_DDI_Benefits_and_Challenges.png 300w, https://blog.isg-research.net/hs-fs/hubfs/Build_Network_Security_Resilience_with_DDI_Benefits_and_Challenges.png?width=400&name=Build_Network_Security_Resilience_with_DDI_Benefits_and_Challenges.png 400w, https://blog.isg-research.net/hs-fs/hubfs/Build_Network_Security_Resilience_with_DDI_Benefits_and_Challenges.png?width=500&name=Build_Network_Security_Resilience_with_DDI_Benefits_and_Challenges.png 500w, https://blog.isg-research.net/hs-fs/hubfs/Build_Network_Security_Resilience_with_DDI_Benefits_and_Challenges.png?width=600&name=Build_Network_Security_Resilience_with_DDI_Benefits_and_Challenges.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p>Network security resilience is the ability to withstand and recover from cyber-attacks. To achieve this, organizations need to use DDI technology. DDI stands for DNS, DHCP and IPAM. These are three services that help devices connect and communicate on a network.</p> </div> <a href="https://blog.isg-research.net/build-network-security-resilience-with-ddi-benefits-and-challenges"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://blog.isg-research.net/topic/digital-business">Digital Business</a>, <a href="https://blog.isg-research.net/topic/digital-security">Digital Security</a>, <a href="https://blog.isg-research.net/topic/ddi">DDI</a>, <a href="https://blog.isg-research.net/topic/dns">DNS</a>, <a href="https://blog.isg-research.net/topic/dhcp">DHCP</a>, <a href="https://blog.isg-research.net/topic/ipam">IPAM</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://blog.isg-research.net/cloudera-facilitates-versatility-with-enterprise-data-and-ai">Cloudera Facilitates Versatility with Enterprise Data and AI</a></h2> <div class="author-meta"> Posted by <a href="https://blog.isg-research.net/author/matt-aslett"> Matt Aslett </a> on <span class="post-date">03 October 2023 </span> </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://blog.isg-research.net/hs-fs/hubfs/Cloudera_Facilitates_Versatility_with_Enterprise_Data_and_AI.png?width=200&name=Cloudera_Facilitates_Versatility_with_Enterprise_Data_and_AI.png" width="200" !important: srcset="https://blog.isg-research.net/hs-fs/hubfs/Cloudera_Facilitates_Versatility_with_Enterprise_Data_and_AI.png?width=100&name=Cloudera_Facilitates_Versatility_with_Enterprise_Data_and_AI.png 100w, https://blog.isg-research.net/hs-fs/hubfs/Cloudera_Facilitates_Versatility_with_Enterprise_Data_and_AI.png?width=200&name=Cloudera_Facilitates_Versatility_with_Enterprise_Data_and_AI.png 200w, https://blog.isg-research.net/hs-fs/hubfs/Cloudera_Facilitates_Versatility_with_Enterprise_Data_and_AI.png?width=300&name=Cloudera_Facilitates_Versatility_with_Enterprise_Data_and_AI.png 300w, https://blog.isg-research.net/hs-fs/hubfs/Cloudera_Facilitates_Versatility_with_Enterprise_Data_and_AI.png?width=400&name=Cloudera_Facilitates_Versatility_with_Enterprise_Data_and_AI.png 400w, https://blog.isg-research.net/hs-fs/hubfs/Cloudera_Facilitates_Versatility_with_Enterprise_Data_and_AI.png?width=500&name=Cloudera_Facilitates_Versatility_with_Enterprise_Data_and_AI.png 500w, https://blog.isg-research.net/hs-fs/hubfs/Cloudera_Facilitates_Versatility_with_Enterprise_Data_and_AI.png?width=600&name=Cloudera_Facilitates_Versatility_with_Enterprise_Data_and_AI.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p>The data platforms market may appear to have little or nothing to do with haute couture, but it is one of the data sectors most strongly influenced by the fickle finger of fashion. In recent years, various architectural approaches to data storage and processing have enjoyed a phase in the limelight, including data warehouse, data mart, data hub, data lake, cloud data warehouse, object storage, data lakehouse, data fabric and data mesh. These approaches are often heralded as the next big thing,...</p> </div> <a href="https://blog.isg-research.net/cloudera-facilitates-versatility-with-enterprise-data-and-ai"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://blog.isg-research.net/topic/cloud-computing">Cloud Computing</a>, <a href="https://blog.isg-research.net/topic/data-governance">Data Governance</a>, <a href="https://blog.isg-research.net/topic/data-management">Data Management</a>, <a href="https://blog.isg-research.net/topic/data">Data</a>, <a href="https://blog.isg-research.net/topic/digital-technology">Digital Technology</a>, <a href="https://blog.isg-research.net/topic/data-operations">data operations</a>, <a href="https://blog.isg-research.net/topic/analytics-data">Analytics & Data</a>, <a href="https://blog.isg-research.net/topic/streaming-data-events">Streaming Data & Events</a>, <a href="https://blog.isg-research.net/topic/operational-data-platforms">operational data platforms</a>, <a href="https://blog.isg-research.net/topic/analytic-data-platforms">Analytic Data Platforms</a>, <a href="">AI and Machine Learning</a> <hr> </div> <script> // Check active classes var checkClass = function() { if ( $('.fil_item').hasClass('hide') ) { $('.fil_item').removeClass('hide'); } }; // Category filters $('.all').click( function() { checkClass(); }); // Active tag $('.bttn').click(function(){ $('.bttn').removeClass('active'); $(this).addClass('active'); }) </script> </div> <div class="blog-numeric-pagination span12 ">   </div> </div> <div class="blog-sidebar">   <style> .blog-sidebar h3 { font-size: 18px; margin-bottom: 30px; position: relative; color: var(--light_blue); padding-top:40px; } .btn-secondary, .filter-expand-link { text-transform: uppercase; background-color: #113049!important; border-color: #113049!important; color: #ffffff!important; transition: all .15s ease-in-out; animation-timing-function: ease-in-out; font-family: 'Open Sans', sans-serif; font-style: normal; font-weight: 500; text-decoration: none; font-size: 0.875rem; line-height: 1.4000000000000001rem; border-radius: 6px; height: 3.5rem; text-align: center; padding: 0 2.8125rem; border: 1px solid; display: -webkit-inline-box; display: -ms-inline-flexbox; display: inline-flex; -webkit-box-align: center; -ms-flex-align: center; align-items: center; } .filter-expand-link { text-transform: uppercase; background-color: #113049!important; border-color: #113049!important; color: #ffffff!important; transition: all .15s ease-in-out; animation-timing-function: ease-in-out; font-family: 'Open Sans', sans-serif; font-style: normal; font-weight: 500; text-decoration: none; font-size: 0.875rem; line-height: 1.4000000000000001rem; border-radius: 6px; height: 3.5rem; text-align: center; padding: 0 2.8125rem; border: 1px solid; display: -webkit-inline-box; display: -ms-inline-flexbox; display: inline-flex; -webkit-box-align: center; -ms-flex-align: center; align-items: center; } </style> <span class="hs-cta-wrapper" id="hs-cta-wrapper-960cd0b7-7494-40d3-ac1c-5825d5a075be"><span class="hs-cta-node hs-cta-960cd0b7-7494-40d3-ac1c-5825d5a075be" id="hs-cta-960cd0b7-7494-40d3-ac1c-5825d5a075be"><a href="https://cta-redirect.hubspot.com/cta/redirect/2035844/960cd0b7-7494-40d3-ac1c-5825d5a075be"><img class="hs-cta-img" id="hs-cta-img-960cd0b7-7494-40d3-ac1c-5825d5a075be" style="border-width:0px;" src="https://no-cache.hubspot.com/cta/default/2035844/960cd0b7-7494-40d3-ac1c-5825d5a075be.png" alt="JOIN OUR COMMUNITY"></a></span><script charset="utf-8" src="https://js.hscta.net/cta/current.js"></script><script type="text/javascript"> hbspt.cta.load(2035844, '960cd0b7-7494-40d3-ac1c-5825d5a075be', {"useNewLoader":"true","region":"na1"}); </script></span> <span id="hs_cos_wrapper_module_1495215326694198" class="hs_cos_wrapper hs_cos_wrapper_widget hs_cos_wrapper_type_rich_text" style="" data-hs-cos-general-type="widget" data-hs-cos-type="rich_text"><h3><span style="color: #284a7b;">Our Analyst Perspective Policy</span></h3> <ul> <li style="display: none; list-style: none;"> <p><span style="color: #284a7b;">Ventana Research’s Analyst Perspectives are fact-based analysis and guidance on business, </span><g class="gr_ gr_12 gr-alert gr_gramm gr_inline_cards gr_run_anim Punctuation only-ins replaceWithoutSep" id="12" data-gr-id="12">industry</g><span style="color: #284a7b;"> and technology vendor trends. Each Analyst Perspective presents the view of the analyst who is an established subject matter expert on new developments, business and technology trends, findings from our research, or best practice insights.</span></p> <p><span style="color: #284a7b;">Each is prepared and reviewed in accordance with Ventana Research’s strict standards for accuracy and objectivity and reviewed to ensure it delivers reliable and actionable insights. It is reviewed and edited by research management and is approved by the Chief Research Officer; no individual or organization outside of Ventana Research reviews any Analyst Perspective before it is published. If you have any issue with an Analyst Perspective, please email them to <a href="mailto:ChiefResearchOfficer@ventanaresearch.com" style="color: #284a7b;">ChiefResearchOfficer@ventanaresearch.com</a></span></p> </li> </ul> <a class="btn-secondary" href="https://www.ventanaresearch.com/analyst-perspective-policy">View Policy</a></span>  <div id="hs_cos_wrapper_module_168621267066296" class="hs_cos_wrapper hs_cos_wrapper_widget hs_cos_wrapper_type_module" style="" data-hs-cos-general-type="widget" data-hs-cos-type="module"><style> </style> <h3 style="color: #255a89;">Our Analysts</h3>  <div class="row-fluid"> <div class="span6" style="text-align: center;"> <a href="https://davidmenninger.isg-research.net"> <img class="analyst_img" style="width: 100px;" src="https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Menninger.David%20Pic.Circle.png?width=100&name=Menninger.David%20Pic.Circle.png" alt="David Menninger" width="100" data-constrained="true" caption="false" sizes="(max-width: 100px) 100vw, 100px" srcset="https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Menninger.David%20Pic.Circle.png?width=50&name=Menninger.David%20Pic.Circle.png 50w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Menninger.David%20Pic.Circle.png?width=100&name=Menninger.David%20Pic.Circle.png 100w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Menninger.David%20Pic.Circle.png?width=150&name=Menninger.David%20Pic.Circle.png 150w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Menninger.David%20Pic.Circle.png?width=200&name=Menninger.David%20Pic.Circle.png 200w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Menninger.David%20Pic.Circle.png?width=250&name=Menninger.David%20Pic.Circle.png 250w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Menninger.David%20Pic.Circle.png?width=300&name=Menninger.David%20Pic.Circle.png 300w"> <h5 class="analyst_name">David Menninger</h5> </a> </div> <div class="span6" style="text-align: center;"> <a href="https://jefforr.isg-research.net"> <img class="analyst_img" style="width: 100px;" src="https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Orr.Jeff.Circle.png?width=100&name=Orr.Jeff.Circle.png" alt="Jeff Orr" width="100" data-constrained="true" caption="false" srcset="https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Orr.Jeff.Circle.png?width=50&name=Orr.Jeff.Circle.png 50w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Orr.Jeff.Circle.png?width=100&name=Orr.Jeff.Circle.png 100w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Orr.Jeff.Circle.png?width=150&name=Orr.Jeff.Circle.png 150w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Orr.Jeff.Circle.png?width=200&name=Orr.Jeff.Circle.png 200w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Orr.Jeff.Circle.png?width=250&name=Orr.Jeff.Circle.png 250w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Orr.Jeff.Circle.png?width=300&name=Orr.Jeff.Circle.png 300w" sizes="(max-width: 100px) 100vw, 100px"> <h5 class="analyst_name">Jeff Orr</h5> </a> </div> </div>   <div class="row-fluid"> <div class="span6" style="text-align: center;"> <a href="https://keithdawson.isg-research.net"> <img class="analyst_img" style="width: 100px;" src="https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Dawson.Keith%20Pic.Circle.png?width=100&name=Dawson.Keith%20Pic.Circle.png" alt="Keith Dawson" width="100" data-constrained="true" caption="false" sizes="(max-width: 100px) 100vw, 100px" srcset="https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Dawson.Keith%20Pic.Circle.png?width=50&name=Dawson.Keith%20Pic.Circle.png 50w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Dawson.Keith%20Pic.Circle.png?width=100&name=Dawson.Keith%20Pic.Circle.png 100w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Dawson.Keith%20Pic.Circle.png?width=150&name=Dawson.Keith%20Pic.Circle.png 150w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Dawson.Keith%20Pic.Circle.png?width=200&name=Dawson.Keith%20Pic.Circle.png 200w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Dawson.Keith%20Pic.Circle.png?width=250&name=Dawson.Keith%20Pic.Circle.png 250w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Dawson.Keith%20Pic.Circle.png?width=300&name=Dawson.Keith%20Pic.Circle.png 300w"> <h5 class="analyst_name">Keith Dawson</h5> </a> </div> <div class="span6" style="text-align:center;"> <a href="https://marksmith.isg-research.net"> <img class="analyst_img" style="width: 100px;" src="https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Smith.Mark%20Pic.Circle.png?width=100&name=Smith.Mark%20Pic.Circle.png" alt="Mark Smith" width="100" data-constrained="true" caption="false" sizes="(max-width: 100px) 100vw, 100px" srcset="https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Smith.Mark%20Pic.Circle.png?width=50&name=Smith.Mark%20Pic.Circle.png 50w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Smith.Mark%20Pic.Circle.png?width=100&name=Smith.Mark%20Pic.Circle.png 100w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Smith.Mark%20Pic.Circle.png?width=150&name=Smith.Mark%20Pic.Circle.png 150w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Smith.Mark%20Pic.Circle.png?width=200&name=Smith.Mark%20Pic.Circle.png 200w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Smith.Mark%20Pic.Circle.png?width=250&name=Smith.Mark%20Pic.Circle.png 250w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Smith.Mark%20Pic.Circle.png?width=300&name=Smith.Mark%20Pic.Circle.png 300w"> <h5 class="analyst_name">Mark Smith</h5> </a> </div> </div>   <div class="row-fluid"> <div class="span6" style="text-align: center;"> <a href="https://mattaslett.isg-research.net"> <img class="analyst_img" style="width: 100px;" src="https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Aslett.Matt%20Pic.Circle.png?width=100&name=Aslett.Matt%20Pic.Circle.png" alt="Matt Aslett" width="100" data-constrained="true" caption="false" sizes="(max-width: 100px) 100vw, 100px" srcset="https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Aslett.Matt%20Pic.Circle.png?width=50&name=Aslett.Matt%20Pic.Circle.png 50w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Aslett.Matt%20Pic.Circle.png?width=100&name=Aslett.Matt%20Pic.Circle.png 100w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Aslett.Matt%20Pic.Circle.png?width=150&name=Aslett.Matt%20Pic.Circle.png 150w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Aslett.Matt%20Pic.Circle.png?width=200&name=Aslett.Matt%20Pic.Circle.png 200w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Aslett.Matt%20Pic.Circle.png?width=250&name=Aslett.Matt%20Pic.Circle.png 250w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Aslett.Matt%20Pic.Circle.png?width=300&name=Aslett.Matt%20Pic.Circle.png 300w"> <h5 class="analyst_name">Matt Aslett</h5> </a> </div> <div class="span6" style="text-align: center;"> <a href="https://matthewbrown.isg-research.net/"> <img class="analyst_img" style="width: 100px;" src="https://blog.isg-research.net/hs-fs/hubfs/_images/_team/profile_vr_matthew_brown_circle_900x900.png?width=100&name=profile_vr_matthew_brown_circle_900x900.png" alt="Matthew Brown" width="100" data-constrained="true" caption="false" sizes="(max-width: 100px) 100vw, 100px" srcset="https://blog.isg-research.net/hs-fs/hubfs/_images/_team/profile_vr_matthew_brown_circle_900x900.png?width=50&name=profile_vr_matthew_brown_circle_900x900.png 50w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/profile_vr_matthew_brown_circle_900x900.png?width=100&name=profile_vr_matthew_brown_circle_900x900.png 100w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/profile_vr_matthew_brown_circle_900x900.png?width=150&name=profile_vr_matthew_brown_circle_900x900.png 150w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/profile_vr_matthew_brown_circle_900x900.png?width=200&name=profile_vr_matthew_brown_circle_900x900.png 200w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/profile_vr_matthew_brown_circle_900x900.png?width=250&name=profile_vr_matthew_brown_circle_900x900.png 250w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/profile_vr_matthew_brown_circle_900x900.png?width=300&name=profile_vr_matthew_brown_circle_900x900.png 300w"> <h5 class="analyst_name">Matthew Brown</h5> </a> </div> </div>   <div class="row-fluid"> <div class="span6" style="text-align: center;"> <a href="https://robertkugel.isg-research.net"> <img class="analyst_img" style="width: 100px;" 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https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Kugel.Robert%20Pic.Circle.png?width=300&name=Kugel.Robert%20Pic.Circle.png 300w"> <h5 class="analyst_name">Robert Kugel</h5> </a> </div> <div class="span6" style="text-align: center;"> <a href="https://stephenhurrell.isg-research.net"> <img class="analyst_img" style="width: 100px;" src="https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Hurrell.Stephen%20Pic.Circle.png?width=100&name=Hurrell.Stephen%20Pic.Circle.png" alt="Stephen Hurrell" width="100" data-constrained="true" caption="false" sizes="(max-width: 100px) 100vw, 100px" srcset="https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Hurrell.Stephen%20Pic.Circle.png?width=50&name=Hurrell.Stephen%20Pic.Circle.png 50w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Hurrell.Stephen%20Pic.Circle.png?width=100&name=Hurrell.Stephen%20Pic.Circle.png 100w, 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2018 <span class="filter-link-count" dir="ltr">(5)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2018/01">January 2018 <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2017/12">December 2017 <span class="filter-link-count" dir="ltr">(8)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2017/11">November 2017 <span class="filter-link-count" dir="ltr">(6)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2017/10">October 2017 <span class="filter-link-count" dir="ltr">(6)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2017/09">September 2017 <span class="filter-link-count" dir="ltr">(6)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2017/08">August 2017 <span class="filter-link-count" dir="ltr">(5)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2017/07">July 2017 <span class="filter-link-count" dir="ltr">(5)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2017/06">June 2017 <span class="filter-link-count" dir="ltr">(7)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2017/05">May 2017 <span class="filter-link-count" dir="ltr">(12)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2017/04">April 2017 <span class="filter-link-count" dir="ltr">(9)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2017/03">March 2017 <span class="filter-link-count" dir="ltr">(11)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2017/02">February 2017 <span class="filter-link-count" dir="ltr">(7)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2017/01">January 2017 <span class="filter-link-count" dir="ltr">(11)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2016/12">December 2016 <span class="filter-link-count" dir="ltr">(6)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2016/11">November 2016 <span class="filter-link-count" dir="ltr">(14)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2016/10">October 2016 <span class="filter-link-count" dir="ltr">(5)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2016/09">September 2016 <span class="filter-link-count" dir="ltr">(11)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2016/08">August 2016 <span class="filter-link-count" dir="ltr">(9)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2016/07">July 2016 <span class="filter-link-count" dir="ltr">(13)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2016/06">June 2016 <span class="filter-link-count" dir="ltr">(10)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2016/05">May 2016 <span class="filter-link-count" dir="ltr">(10)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2016/04">April 2016 <span class="filter-link-count" dir="ltr">(5)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2016/03">March 2016 <span class="filter-link-count" dir="ltr">(13)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2016/02">February 2016 <span class="filter-link-count" dir="ltr">(12)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2016/01">January 2016 <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2015/12">December 2015 <span class="filter-link-count" dir="ltr">(16)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2015/11">November 2015 <span class="filter-link-count" dir="ltr">(13)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2015/10">October 2015 <span class="filter-link-count" dir="ltr">(7)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2015/09">September 2015 <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2015/08">August 2015 <span class="filter-link-count" dir="ltr">(6)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2015/07">July 2015 <span class="filter-link-count" dir="ltr">(8)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2015/06">June 2015 <span class="filter-link-count" dir="ltr">(8)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2015/05">May 2015 <span class="filter-link-count" dir="ltr">(6)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2015/04">April 2015 <span class="filter-link-count" dir="ltr">(9)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2015/03">March 2015 <span class="filter-link-count" dir="ltr">(7)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2015/02">February 2015 <span class="filter-link-count" dir="ltr">(22)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2015/01">January 2015 <span class="filter-link-count" dir="ltr">(14)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2014/12">December 2014 <span class="filter-link-count" dir="ltr">(8)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2014/11">November 2014 <span class="filter-link-count" dir="ltr">(6)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2014/10">October 2014 <span class="filter-link-count" dir="ltr">(6)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2014/09">September 2014 <span class="filter-link-count" dir="ltr">(10)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2014/08">August 2014 <span class="filter-link-count" dir="ltr">(7)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2014/07">July 2014 <span class="filter-link-count" dir="ltr">(7)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2014/06">June 2014 <span class="filter-link-count" dir="ltr">(16)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2014/05">May 2014 <span class="filter-link-count" dir="ltr">(9)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2014/04">April 2014 <span class="filter-link-count" dir="ltr">(15)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2014/03">March 2014 <span class="filter-link-count" dir="ltr">(14)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2014/02">February 2014 <span class="filter-link-count" dir="ltr">(13)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2014/01">January 2014 <span class="filter-link-count" dir="ltr">(16)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2013/12">December 2013 <span class="filter-link-count" dir="ltr">(6)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2013/11">November 2013 <span class="filter-link-count" dir="ltr">(15)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2013/10">October 2013 <span class="filter-link-count" dir="ltr">(10)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2013/09">September 2013 <span class="filter-link-count" dir="ltr">(11)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2013/08">August 2013 <span class="filter-link-count" dir="ltr">(17)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2013/07">July 2013 <span class="filter-link-count" dir="ltr">(12)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2013/06">June 2013 <span class="filter-link-count" dir="ltr">(13)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2013/05">May 2013 <span class="filter-link-count" dir="ltr">(21)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2013/04">April 2013 <span class="filter-link-count" dir="ltr">(19)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2013/03">March 2013 <span class="filter-link-count" dir="ltr">(35)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2013/02">February 2013 <span class="filter-link-count" dir="ltr">(27)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2013/01">January 2013 <span class="filter-link-count" dir="ltr">(31)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2012/12">December 2012 <span class="filter-link-count" dir="ltr">(20)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2012/11">November 2012 <span class="filter-link-count" dir="ltr">(21)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2012/10">October 2012 <span class="filter-link-count" dir="ltr">(25)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2012/09">September 2012 <span class="filter-link-count" dir="ltr">(21)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2012/08">August 2012 <span class="filter-link-count" dir="ltr">(26)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2012/07">July 2012 <span class="filter-link-count" dir="ltr">(24)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2012/06">June 2012 <span class="filter-link-count" dir="ltr">(27)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2012/05">May 2012 <span class="filter-link-count" dir="ltr">(32)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2012/04">April 2012 <span class="filter-link-count" dir="ltr">(23)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2012/03">March 2012 <span class="filter-link-count" dir="ltr">(21)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2012/02">February 2012 <span class="filter-link-count" dir="ltr">(13)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2012/01">January 2012 <span class="filter-link-count" dir="ltr">(14)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2011/12">December 2011 <span class="filter-link-count" dir="ltr">(14)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2011/11">November 2011 <span class="filter-link-count" dir="ltr">(19)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2011/10">October 2011 <span class="filter-link-count" dir="ltr">(28)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2011/09">September 2011 <span class="filter-link-count" dir="ltr">(26)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2011/08">August 2011 <span class="filter-link-count" dir="ltr">(20)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2011/07">July 2011 <span class="filter-link-count" dir="ltr">(12)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2011/06">June 2011 <span class="filter-link-count" dir="ltr">(22)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2011/05">May 2011 <span class="filter-link-count" dir="ltr">(28)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2011/04">April 2011 <span class="filter-link-count" dir="ltr">(19)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2011/03">March 2011 <span class="filter-link-count" dir="ltr">(22)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2011/02">February 2011 <span class="filter-link-count" dir="ltr">(15)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2011/01">January 2011 <span class="filter-link-count" dir="ltr">(8)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2010/12">December 2010 <span class="filter-link-count" dir="ltr">(21)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2010/11">November 2010 <span class="filter-link-count" dir="ltr">(132)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/archive/2010/06">June 2010 <span class="filter-link-count" dir="ltr">(1)</span></a> </li> </ul> <a class="filter-expand-link" href="#">see all</a> </div> </div> </span> <span id="hs_cos_wrapper_post_filter" class="hs_cos_wrapper hs_cos_wrapper_widget hs_cos_wrapper_type_post_filter" style="" data-hs-cos-general-type="widget" data-hs-cos-type="post_filter"><div class="block"> <h3>Posts by Topic</h3> <div class="widget-module"> <ul> <li> <a href="https://blog.isg-research.net/topic/10q">10Q <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li> <a href="https://blog.isg-research.net/topic/vendor-references">[Vendor references] <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li> <a href="https://blog.isg-research.net/topic/absolute-data">Absolute Data <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li> <a href="https://blog.isg-research.net/topic/accessibility">Accessibility <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li> <a href="https://blog.isg-research.net/topic/accountants">accountants <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/accounting">Accounting <span class="filter-link-count" dir="ltr">(7)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/accounting-close">accounting close <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/accounting-software">accounting software <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/accounting-analytics-erp-eam-crm-hcm-innovation-cl">accounting, analytics, ERP, EAM, CRM, HCM, innovat <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/accounts-payable">Accounts Payable <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/accounts-receivable">Accounts Receivable <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/acd">ACD <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/acquisition">acquisition <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/agent-management">agent management <span class="filter-link-count" dir="ltr">(110)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/agile">agile <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/ai">AI <span class="filter-link-count" dir="ltr">(30)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/ai-machine-learning">AI & Machine Learning <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/ai-and-machine-learning">AI and Machine Learning <span class="filter-link-count" dir="ltr">(270)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/aiops">AIOps <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/alert">alert <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/alpine-data-labs">alpine data labs <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/alteryx">alteryx <span class="filter-link-count" dir="ltr">(15)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/alteryx-inspire">Alteryx Inspire <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/analysis">analysis <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/analytic-catalyst">analytic catalyst <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/analytic-data-platforms">Analytic Data Platforms <span class="filter-link-count" dir="ltr">(45)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/analytic-operations">Analytic Operations <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/analytical-application">analytical application <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/analytical-discovery">analytical discovery <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/analytics">Analytics <span class="filter-link-count" dir="ltr">(955)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/analytics-data">Analytics & Data <span class="filter-link-count" dir="ltr">(124)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/analytics-and-data">Analytics and Data <span class="filter-link-count" dir="ltr">(8)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/analytics-in-the-cloud">analytics in the cloud <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/and-verint">and Verint <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/application">application <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/arcplan">arcplan <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/artificial-intelligence">Artificial intelligence <span class="filter-link-count" dir="ltr">(14)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/asc-606">asc 606 <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/asc842">ASC842 <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/assets">assets <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/audit">audit <span class="filter-link-count" dir="ltr">(17)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/augmented-analytics">Augmented Analytics <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/automation">automation <span class="filter-link-count" dir="ltr">(5)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/awards">Awards <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/azure">azure <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/backup">Backup <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/balanced-scorecard">balanced scorecard <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/banking">banking <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/benchmark">benchmark <span class="filter-link-count" dir="ltr">(16)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/beps">BEPS <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/beqom">beqom <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/best-pracices">best pracices <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/bi-platform">BI platform <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/big-analytics">big analytics <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/big-data">Big Data <span class="filter-link-count" dir="ltr">(431)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/big-query">big query <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/billing-and-recurring-revenue">Billing and Recurring Revenue <span class="filter-link-count" dir="ltr">(11)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/billing-software">billing software <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/blockchain">blockchain <span class="filter-link-count" dir="ltr">(34)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/blockchain-distributed-ledger-dlt-erp-scm-supply-c">blockchain, distributed ledger, DLT, ERP, SCM, sup <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/board-international">BOARD International <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/board-of-directors">board of directors <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/bots">bots <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/box">box <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/bribery">bribery <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/budget">budget <span class="filter-link-count" dir="ltr">(5)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/budgeting">Budgeting <span class="filter-link-count" dir="ltr">(74)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/budgeting-and-planning">budgeting and planning <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/budgeting-software">budgeting software <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/business">Business <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/business-activity-monitoring">business activity monitoring <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/business-analytics">Business Analytics <span class="filter-link-count" dir="ltr">(663)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/business-applications">Business Applications <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/business-collaboration">Business Collaboration <span class="filter-link-count" dir="ltr">(361)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/business-continuity">Business Continuity <span class="filter-link-count" dir="ltr">(22)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/business-digital-commerce">business digital commerce <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/business-innovation">business innovation <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/business-intelligence">Business Intelligence <span class="filter-link-count" dir="ltr">(666)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/business-intelligence-3">business intelligence <span class="filter-link-count" dir="ltr">(38)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/business-intelligence-2">business intelligence <span class="filter-link-count" dir="ltr">(15)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/business-intelligence-business-analytics-cloud-clo">Business Intelligence, Business Analytics, Cloud, <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/business-mobility">Business Mobility <span class="filter-link-count" dir="ltr">(160)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/business-model">business model <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/business-objects">Business Objects <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/business-objects-predictive-analytics">Business Objects predictive analytics <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/business-performance">Business Performance <span class="filter-link-count" dir="ltr">(739)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/business-performance-management-bpm">Business Performance Management (BPM) <span class="filter-link-count" dir="ltr">(14)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/business-plan">business plan <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/business-planning">Business Planning <span class="filter-link-count" dir="ltr">(95)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/business-process">business process <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/business-process-execution">business process execution <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/business-process-management">Business Process Management <span class="filter-link-count" dir="ltr">(5)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/business-process-monitoring">business process monitoring <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/business-process-outsourcing">business process outsourcing <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/business-suite">Business Suite <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/business-technology">Business Technology <span class="filter-link-count" dir="ltr">(40)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/business-technology-innovation">Business Technology Innovation <span class="filter-link-count" dir="ltr">(15)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/business-value">business value <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/calabrio">Calabrio <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/call-center">Call Center <span class="filter-link-count" dir="ltr">(301)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/call-center-analytics">call center analytics <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/call-copy">Call Copy <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/call-routing">Call Routing <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/callidus-software">Callidus Software <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/calliduscloud">CallidusCloud <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/callminer">Callminer <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/calltower">CallTower <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/cambridge-semantics">Cambridge Semantics <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/camelon-software">Camelon Software <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/candidate-engagement">candidate engagement <span class="filter-link-count" dir="ltr">(30)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/candidate-experience">Candidate Experience <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/capacity-utilization">capacity utilization <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/capital-adequacy">capital adequacy <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/capital-budget">capital budget <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/capital-markets">capital markets <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/capital-spending">capital spending <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/cash-management">cash management <span class="filter-link-count" dir="ltr">(9)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/ccaas">CCaaS <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/cdh3">CDH3 <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/cdp">CDP <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/celergo-hcm">Celergo HCM <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/cem">CEM <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/ceo">CEO <span class="filter-link-count" dir="ltr">(22)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/ceo-cfo-financial-performance-management-forecasti">CEO, CFO, Financial Performance Management, Foreca <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/cep">CEP <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/ceridian">Ceridian <span class="filter-link-count" dir="ltr">(7)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/cfo">CFO <span class="filter-link-count" dir="ltr">(164)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/challenge">challenge <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/channel">channel <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/channelinsight">ChannelInsight <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/chargebacks">chargebacks <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/chart-of-accounts">chart of accounts <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/chatbots">Chatbots <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/chatter">Chatter <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/chief-risk-officer">Chief Risk Officer <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/chro">CHRO <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/ci-cd">CI/CD <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/cicero">Cicero <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/cincom">Cincom <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/cio">CIO <span class="filter-link-count" dir="ltr">(112)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/cio-callenges">cio callenges <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/cirro">Cirro <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/cisco">Cisco <span class="filter-link-count" dir="ltr">(6)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/ciso">CISO <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/clarabridge">Clarabridge <span class="filter-link-count" dir="ltr">(5)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/clarity-systems">Clarity Systems <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/clickfox">Clickfox <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/close">close <span class="filter-link-count" dir="ltr">(43)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/closed-loop">closed loop <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/closing">closing <span class="filter-link-count" dir="ltr">(35)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/cloud">Cloud <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/cloud-analytics">cloud analytics <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/cloud-business-intelligence">cloud business intelligence <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/cloud-computing">Cloud Computing <span class="filter-link-count" dir="ltr">(863)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/cloud-erp">cloud ERP <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/cloud9-analytics">Cloud9 Analytics <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/cloudera">Cloudera <span class="filter-link-count" dir="ltr">(17)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/cloudops">CloudOps <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/cmo">CMO <span class="filter-link-count" dir="ltr">(24)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/coaching">coaching <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/coda">CODA <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/cognitive-computing">Cognitive Computing <span class="filter-link-count" dir="ltr">(9)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/cognos">Cognos <span class="filter-link-count" dir="ltr">(6)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/cognos-analytics">cognos analytics <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/cognos-enterprise">cognos enterprise <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/cognos-express">cognos express <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/cognos-insight">cognos insight <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/collaboration">Collaboration <span class="filter-link-count" dir="ltr">(263)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/collaboration-for-business">collaboration for business <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/collaborative-conversational-computing">Collaborative & Conversational Computing <span class="filter-link-count" dir="ltr">(8)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/collaborative-analytics">Collaborative Analytics <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/collaborative-computing">collaborative computing <span class="filter-link-count" dir="ltr">(34)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/commerce">Commerce <span class="filter-link-count" dir="ltr">(5)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/commission">commission <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/commodity-model">commodity model <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/commodity-models">commodity models <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/communications">communications <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/compensation">Compensation <span class="filter-link-count" dir="ltr">(64)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/competition">competition <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/complex-event-processing">Complex Event Processing <span class="filter-link-count" dir="ltr">(11)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/compliance">compliance <span class="filter-link-count" dir="ltr">(30)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/composite-document">Composite document <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/composite-software">Composite Software <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/computer-vision">Computer Vision <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/computing">computing <span class="filter-link-count" dir="ltr">(6)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/concur">Concur <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/confirmit">Confirmit <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/connectwise">ConnectWise <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/connotate">Connotate <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/consolidate-and-close-management">Consolidate and Close Management <span class="filter-link-count" dir="ltr">(10)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/consolidate-close-report">Consolidate/Close/Report <span class="filter-link-count" dir="ltr">(17)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/consolidating">consolidating <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/consolidation">Consolidation <span class="filter-link-count" dir="ltr">(28)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/consulting">Consulting <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/contact-center">Contact Center <span class="filter-link-count" dir="ltr">(437)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/contact-center-analytics">Contact Center Analytics <span class="filter-link-count" dir="ltr">(226)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/contactual">Contactual <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/container">container <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/content-management">Content Management <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/contingency">contingency <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/contingency-planning">contingency planning <span class="filter-link-count" dir="ltr">(5)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/continuous-accounting">Continuous Accounting <span class="filter-link-count" dir="ltr">(9)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/continuous-improvement">continuous improvement <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/continuous-payroll">Continuous Payroll <span class="filter-link-count" dir="ltr">(27)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/continuous-planning">Continuous Planning <span class="filter-link-count" dir="ltr">(60)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/continuous-supply-chain">continuous supply chain <span class="filter-link-count" dir="ltr">(42)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/continuous-supply-chain-erp">Continuous Supply Chain & ERP <span class="filter-link-count" dir="ltr">(13)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/continuous-threat-exposure-management">Continuous Threat Exposure Management <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/contract">contract <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/contract-management">contract management <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/control">control <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/controller">Controller <span class="filter-link-count" dir="ltr">(36)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/controller-2">controller <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/controls">controls <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/conversational-ai">Conversational AI <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/conversational-computing">Conversational Computing <span class="filter-link-count" dir="ltr">(45)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/conversational-marketing">Conversational Marketing <span class="filter-link-count" dir="ltr">(7)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/coo">COO <span class="filter-link-count" dir="ltr">(10)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/core-hr">Core HR <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/cornerstone-ondemand">Cornerstone OnDemand <span class="filter-link-count" dir="ltr">(5)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/corporate-finance">Corporate Finance <span class="filter-link-count" dir="ltr">(8)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/corporate-governance">Corporate Governance <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/corvisa-cloud">Corvisa Cloud <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/cost-management">Cost Management <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/costing">costing <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/cpaas">CPaaS <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/cpq">CPQ <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/credit">credit <span class="filter-link-count" dir="ltr">(7)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/crisis">crisis <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/crm">CRM <span class="filter-link-count" dir="ltr">(267)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/crm-customer-service">CRM customer service <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/cro">CRO <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/cryptic">cryptic <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/crystal-reports">Crystal reports <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/ctalk-ltd">cTalk Ltd <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/ctem">CTEM <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/cto">CTO <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/currency">currency <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/customer">Customer <span class="filter-link-count" dir="ltr">(5)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/customer-contact-center">Customer & Contact Center <span class="filter-link-count" dir="ltr">(557)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/customer-analytics">Customer Analytics <span class="filter-link-count" dir="ltr">(263)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/customer-communications">Customer Communications <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/customer-data-management">Customer Data Management <span class="filter-link-count" dir="ltr">(47)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/customer-data-platforms">Customer Data Platforms <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/customer-digital-technology">Customer Digital Technology <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/customer-engagement">Customer Engagement <span class="filter-link-count" dir="ltr">(70)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/customer-excellence">Customer Excellence <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/customer-experience">Customer Experience <span class="filter-link-count" dir="ltr">(532)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/customer-experience-management">Customer Experience Management <span class="filter-link-count" dir="ltr">(55)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/customer-feedback">Customer Feedback <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/customer-feedback-management">Customer Feedback Management <span class="filter-link-count" dir="ltr">(123)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/customer-information-management">Customer Information Management <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/customer-intelligence">Customer Intelligence <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/customer-interaction-technology">Customer Interaction Technology <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/customer-journey-maps">Customer Journey Maps <span class="filter-link-count" dir="ltr">(6)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/customer-life-cycle">customer life cycle <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/customer-performance">Customer Performance <span class="filter-link-count" dir="ltr">(109)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/customer-relationship-management">Customer Relationship Management <span class="filter-link-count" dir="ltr">(6)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/customer-service">Customer Service <span class="filter-link-count" dir="ltr">(289)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/customer-service-and-support">customer service and support <span class="filter-link-count" dir="ltr">(20)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/cxm">CXM <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/cybersecurity">Cybersecurity <span class="filter-link-count" dir="ltr">(5)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/dashboard">dashboard <span class="filter-link-count" dir="ltr">(12)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/dashboards">Dashboards <span class="filter-link-count" dir="ltr">(7)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/data">Data <span class="filter-link-count" dir="ltr">(305)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/data-analytics">Data Analytics <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/data-artisan">data artisan <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/data-blending">data blending <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/data-catalog">Data catalog <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/data-democratization">Data Democratization <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/data-discovery">Data Discovery <span class="filter-link-count" dir="ltr">(12)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/data-fabric">data fabric <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/data-governance">Data Governance <span class="filter-link-count" dir="ltr">(155)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/data-integration">Data Integration <span class="filter-link-count" dir="ltr">(82)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/data-integrity">data integrity <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/data-intelligence">Data Intelligence <span class="filter-link-count" dir="ltr">(15)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/data-lake">Data Lake <span class="filter-link-count" dir="ltr">(7)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/data-lakes">data lakes <span class="filter-link-count" dir="ltr">(43)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/data-management">Data Management <span class="filter-link-count" dir="ltr">(88)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/data-mart">data mart <span class="filter-link-count" dir="ltr">(5)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/data-mining">Data Mining <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/data-observability">Data Observability <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/data-operations">data operations <span class="filter-link-count" dir="ltr">(101)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/data-ops">Data Ops <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/data-orchestration">Data Orchestration <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/data-pipelines">Data Pipelines <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/data-platforms">data platforms <span class="filter-link-count" dir="ltr">(45)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/data-preparation">Data Preparation <span class="filter-link-count" dir="ltr">(89)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/data-products">Data Products <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/data-quality">Data Quality <span class="filter-link-count" dir="ltr">(13)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/data-science">Data Science <span class="filter-link-count" dir="ltr">(61)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/data-scientist">Data Scientist <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/data-storage">Data Storage <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/data-virtualization">Data Virtualization <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/data-virtualization-data-integration-data-lake-big">Data virtualization, data integration, data lake, <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/data-visualization">Data Visualization <span class="filter-link-count" dir="ltr">(7)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/data-viz">data viz <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/data-warehousing">Data Warehousing <span class="filter-link-count" dir="ltr">(19)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/database">Database <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/database-as-a-service">Database as a Service <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/database-com">database.com <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/datameer">Datameer <span class="filter-link-count" dir="ltr">(9)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/dataops">dataops <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/dataprep">DataPrep <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/datawatch">Datawatch <span class="filter-link-count" dir="ltr">(14)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/dayforce">dayforce <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/ddi">DDI <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/debt">debt <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/decision">decision <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/decision-hub">Decision Hub <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/decision-intelligence">Decision Intelligence <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/decision-management">Decision Management <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/decusoft">Decusoft <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/deep-learning">Deep Learning <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/dell">Dell <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/dell-boomi">Dell Boomi <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/demand-chain">Demand Chain <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/demand-generation">Demand Generation <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/demand-management">demand management <span class="filter-link-count" dir="ltr">(5)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/demand-planning">Demand Planning <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/design">design <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/designed-data">designed data <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/desktop-analytics">Desktop Analytics <span class="filter-link-count" dir="ltr">(124)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/development">development <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/devops-and-platforms">DevOps and Platforms <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/devsecops">DevSecOps <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/df11">DF11 <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/dg">DG <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/dhcp">DHCP <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/digital-applications">Digital Applications <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/digital-business">Digital Business <span class="filter-link-count" dir="ltr">(47)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/digital-commerce">Digital Commerce <span class="filter-link-count" dir="ltr">(104)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/digital-communications">Digital Communications <span class="filter-link-count" dir="ltr">(8)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/digital-experience-platform">Digital Experience Platform <span class="filter-link-count" dir="ltr">(5)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/digital-finance">digital finance <span class="filter-link-count" dir="ltr">(105)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/digital-innovation-awards">Digital Innovation Awards <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/digital-intelligence">Digital Intelligence <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/digital-interviewing">Digital Interviewing <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/digital-marketing">Digital Marketing <span class="filter-link-count" dir="ltr">(28)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/digital-operations">Digital Operations <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/digital-publishing">Digital Publishing <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/digital-security">Digital Security <span class="filter-link-count" dir="ltr">(23)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/digital-technology">Digital Technology <span class="filter-link-count" dir="ltr">(292)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/digital-transformation">Digital transformation <span class="filter-link-count" dir="ltr">(20)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/direct-data-mapping">Direct Data Mapping <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/disaster-recovery">Disaster Recovery <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/discovery">Discovery <span class="filter-link-count" dir="ltr">(6)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/discovery-analytics">discovery analytics <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/discovery-tools">discovery tools <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/distribution">distribution <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/dns">DNS <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/document-management">Document Management <span class="filter-link-count" dir="ltr">(12)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/dodd-frank">Dodd-Frank <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/domestic-payroll">Domestic Payroll <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/domo">Domo <span class="filter-link-count" dir="ltr">(5)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/dovetail">Dovetail <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/dreamforce">Dreamforce <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/driver-analysis">driver analysis <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/driver-based">driver-based <span class="filter-link-count" dir="ltr">(14)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/driver-based-planning">driver-based planning <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/dynamics">Dynamics <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/dynamics-ax">Dynamics AX <span class="filter-link-count" dir="ltr">(9)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/dynamics-gp">Dynamics GP <span class="filter-link-count" dir="ltr">(9)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/dynamics-nav-dynamics-sl">Dynamics NAV Dynamics SL <span class="filter-link-count" dir="ltr">(9)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/e-discovery">e-discovery <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/echopass">Echopass <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/eclipse">Eclipse <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/edi">EDI <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/ediscovery">eDiscovery <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/editorial">Editorial <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/effectiveness">effectiveness <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/eg-solutions">eg Solutions <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/elasticube">elasticube <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/eloqua">Eloqua <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/embedded-analytics">embedded analytics <span class="filter-link-count" dir="ltr">(95)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/emc">EMC <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/employee-engagement">Employee Engagement <span class="filter-link-count" dir="ltr">(9)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/employee-experience">employee experience <span class="filter-link-count" dir="ltr">(47)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/employee-onboarding">Employee Onboarding <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/employee-productivity">Employee Productivity <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/empower">Empower <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/end-to-end">end-to-end <span class="filter-link-count" dir="ltr">(7)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/endeca">endeca <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/enghouse-interactive">Enghouse interactive <span class="filter-link-count" dir="ltr">(5)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/enkata">Enkata <span class="filter-link-count" dir="ltr">(6)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/enterprise-applications">Enterprise Applications <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/enterprise-data-strategy">Enterprise Data Strategy <span class="filter-link-count" dir="ltr">(8)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/enterprise-messaging-internet-of-things-iot-middle">Enterprise messaging, Internet of Things, IoT, mid <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/enterprise-profitability-management">enterprise profitability management <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/enterprise-resource-planning">Enterprise Resource Planning <span class="filter-link-count" dir="ltr">(72)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/enterprise-risk-management">enterprise risk management <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/enterprise-software">Enterprise Software <span class="filter-link-count" dir="ltr">(27)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/enterprise-spreadsheet">enterprise spreadsheet <span class="filter-link-count" dir="ltr">(7)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/enterprise-tax">Enterprise Tax <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/enterworks">Enterworks <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/environment-management">Environment Management <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/environmental">Environmental <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/envision">Envision <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/epicor">Epicor <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/epiphany">Epiphany <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/equifax">Equifax <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/equity-research">equity research <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/erm">ERM <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/erp">ERP <span class="filter-link-count" dir="ltr">(83)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/erp-and-continuous-accounting">ERP and Continuous Accounting <span class="filter-link-count" dir="ltr">(150)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/error">error <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/esg">ESG <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/esri">ESRI <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/essbase">Essbase <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/ethority">eThority <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/event-data">Event Data <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/event-discovery">Event discovery <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/event-stream">Event Stream <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/event-stream-processing">Event Stream Processing <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/event-streams">Event Streams <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/exacttarget">ExactTarget <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/exadata">exadata <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/exalytics">Exalytics <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/excel">Excel <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/excentive">Excentive <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/executive">executive <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/expense">expense <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/expense-management">expense management <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/experience-management">Experience Management <span class="filter-link-count" dir="ltr">(9)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/expert-systems">Expert Systems <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/exploration">Exploration <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/exploratory-analytics">exploratory analytics <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/explorium">Explorium <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/extended-close">extended close <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/extended-reality">extended reality <span class="filter-link-count" dir="ltr">(16)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/external-data-platform">External Data Platform <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/exterro">Exterro <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/f-type">F-Type <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/fasb">FASB <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/fast-close">Fast close <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/feedback">Feedback <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/fei">FEI <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/ferf-ceo">FERF CEO <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/field-service">Field Service <span class="filter-link-count" dir="ltr">(29)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/finance">finance <span class="filter-link-count" dir="ltr">(53)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/finance-analytics">Finance Analytics <span class="filter-link-count" dir="ltr">(7)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/finance-cloud">finance cloud <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/finance-financial-applications-financial-close">Finance Financial Applications Financial Close <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/finance-performance-management">Finance Performance Management <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/finance-transformation">finance transformation <span class="filter-link-count" dir="ltr">(9)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/financial">financial <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/financial-analytics">financial analytics <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/financial-applications">Financial Applications <span class="filter-link-count" dir="ltr">(5)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/financial-close">Financial Close <span class="filter-link-count" dir="ltr">(5)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/financial-governance">Financial Governance <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/financial-management">Financial Management <span class="filter-link-count" dir="ltr">(7)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/financial-model">financial model <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/financial-performance">Financial Performance <span class="filter-link-count" dir="ltr">(561)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/financial-performance-2">financial performance <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/financial-performance-management">Financial Performance Management <span class="filter-link-count" dir="ltr">(237)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/financial-performance-management-fpm">Financial Performance Management (FPM) <span class="filter-link-count" dir="ltr">(14)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/financial-planning">financial planning <span class="filter-link-count" dir="ltr">(6)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/financial-regulation">financial regulation <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/financial-reporting">financial reporting <span class="filter-link-count" dir="ltr">(15)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/financial-risk-management">financial risk management <span class="filter-link-count" dir="ltr">(5)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/financial-services">Financial Services <span class="filter-link-count" dir="ltr">(8)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/financial-software">financial software <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/financial-standards">financial standards <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/financial-statement">financial statement <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/financial-systems">financial systems <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/financialforce">FinancialForce <span class="filter-link-count" dir="ltr">(15)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/financialforce-com">FinancialForce.com <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/financials">financials <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/five9">Five9 <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/flow">flow <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/fms">FMS <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/for-example-genesys">for example Genesys <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/forecast">Forecast <span class="filter-link-count" dir="ltr">(9)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/forecasting">forecasting <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/fpa">FP&A <span class="filter-link-count" dir="ltr">(22)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/fpm">FPM <span class="filter-link-count" dir="ltr">(83)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/fraud">Fraud <span class="filter-link-count" dir="ltr">(8)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/fuel50">Fuel50 <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/fusion-applications">Fusion Applications <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/gaap">GAAP <span class="filter-link-count" dir="ltr">(10)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/gamifiacation">Gamifiacation <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/gartner">Gartner <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/genai">GenAI <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/generative-ai">Generative AI <span class="filter-link-count" dir="ltr">(37)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/genesys">Genesys <span class="filter-link-count" dir="ltr">(10)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/geographic-information-systems">Geographic Information Systems <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/gis">GIS <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/global-payroll">Global Payroll <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/globoforce">Globoforce <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/golden-records">Golden Records. <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/gooddata">Gooddata <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/google">Google <span class="filter-link-count" dir="ltr">(10)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/governance">Governance <span class="filter-link-count" dir="ltr">(49)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/governance-risk">Governance & Risk <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/governance-risk-and-compliance">Governance Risk and Compliance <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/governance-risk-compliance-grc">Governance, Risk & Compliance (GRC) <span class="filter-link-count" dir="ltr">(143)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/grc">GRC <span class="filter-link-count" dir="ltr">(55)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/grc-governance-risk-compliance-risk-management-wat">GRC, governance, risk, compliance, risk management <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/green">Green <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/greenplum">Greenplum <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/gui">GUI <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hadoop">Hadoop <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hana">HANA <span class="filter-link-count" dir="ltr">(9)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/harvard-business-review">Harvard Business Review <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hawq">HAWQ <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hcm">HCM <span class="filter-link-count" dir="ltr">(57)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hcm-analytics">HCM Analytics <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hcm-systems">HCM Systems <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hcm-hr-hrms-workforce-management-learning-manageme">HCM, HR, HRMS, Workforce Management, Learning Mana <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hdfs">HDFS <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hdinsights">HDinsights <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hdp">HDP <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/headcount-planning">headcount planning <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/healthcare-compliance">Healthcare Compliance <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/healthcare-reform">Healthcare Reform <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/heiler">Heiler <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hewlett-packard">Hewlett Packard <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hirevue">HireVue <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hiring">Hiring <span class="filter-link-count" dir="ltr">(10)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hive">Hive <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hive9-marketing-performance-management">Hive9 Marketing Performance Management <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hortonworks">Hortonworks <span class="filter-link-count" dir="ltr">(14)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hospitality">Hospitality <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/host-analytics">Host Analytics <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hp">HP <span class="filter-link-count" dir="ltr">(7)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hp-neoview">HP Neoview <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hp-touchpad">HP Touchpad <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hr">HR <span class="filter-link-count" dir="ltr">(56)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hr-analytics">HR Analytics <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hr-management">HR Management <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hr-managementhrms">HR Management(HRMS) <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hr-research">HR research <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hr-tech">HR Tech <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hr-technology">HR technology <span class="filter-link-count" dir="ltr">(5)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hr-technology-conference">HR Technology Conference <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hrpayroll">HR/Payroll <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hris">HRIS <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hrms">HRMS <span class="filter-link-count" dir="ltr">(47)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/html5">HTML5 <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hubspot">HubSpot <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/human-capital">Human Capital <span class="filter-link-count" dir="ltr">(45)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/human-capital-analytics">Human Capital Analytics <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/human-capital-management">Human Capital Management <span class="filter-link-count" dir="ltr">(308)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/human-concepts">Human Concepts <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/human-resources-management">Human Resources Management <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hunite">Hunite <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hybrid-cloud-platforms">Hybrid Cloud Platforms <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hybris">Hybris <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hyperion">Hyperion <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/i9">I9 <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/iac">IaC <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/iasb">IASB <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/ibm">IBM <span class="filter-link-count" dir="ltr">(73)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/ibm-business-analytics">IBM Business Analytics <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/ibm-db2">IBM DB2 <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/ibm-predictive-customer-intelligence">IBM Predictive Customer Intelligence <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/ibm-spss">IBM SPSS <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/ibm-watson">IBM Watson <span class="filter-link-count" dir="ltr">(8)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/ibp">IBP <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/ifrs">IFRS <span class="filter-link-count" dir="ltr">(11)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/ifrs16">IFRS16 <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/impala">Impala <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/in-memory">In-memory <span class="filter-link-count" dir="ltr">(26)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/in-memory-computing">In-Memory Computing <span class="filter-link-count" dir="ltr">(6)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/inclusion">Inclusion <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/incontact">InContact <span class="filter-link-count" dir="ltr">(6)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/industry-analyst">Industry Analyst <span class="filter-link-count" dir="ltr">(7)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/inetsoft">InetSoft <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/influencers">Influencers <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/infodiscovery">InfoDiscovery <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/infomgt-location">InfoMgt; Location <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/infopia">InfoPia <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/infor">Infor <span class="filter-link-count" dir="ltr">(31)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/infor-birst">Infor Birst <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/informatica">Informatica <span class="filter-link-count" dir="ltr">(22)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/informatica-cloud">Informatica Cloud <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/informatica-hparser">Informatica HParser <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/informatica-world">Informatica World <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/informatics">Informatics <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/information">Information <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/information-applications">Information Applications <span class="filter-link-count" dir="ltr">(222)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/information-availability">Information Availability <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/information-builder-iway">Information Builder iway <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/information-builders">Information Builders <span class="filter-link-count" dir="ltr">(13)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/information-builders-summit-2019">Information Builders Summit 2019 <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/information-discovery">Information Discovery <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/information-life-cycle-management">Information Life Cycle Management <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/information-management">Information Management <span class="filter-link-count" dir="ltr">(381)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/information-management-im">Information Management (IM) <span class="filter-link-count" dir="ltr">(11)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/information-optimization">Information Optimization <span class="filter-link-count" dir="ltr">(52)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/information-platform">Information Platform <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/information-technology">Information Technology <span class="filter-link-count" dir="ltr">(47)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/infosphere">InfoSphere <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/initiatives-management">Initiatives Management <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/initiatives-planning">Initiatives Planning <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/inkling">inkling <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/innovation-awards">Innovation Awards <span class="filter-link-count" dir="ltr">(5)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/insights">Insights <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/intacct">Intacct <span class="filter-link-count" dir="ltr">(9)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/integrated-business-planning">Integrated Business Planning <span class="filter-link-count" dir="ltr">(38)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/intelligent-automation">Intelligent Automation <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/intelligent-cx">Intelligent CX <span class="filter-link-count" dir="ltr">(8)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/intelligent-marketing">intelligent marketing <span class="filter-link-count" dir="ltr">(16)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/intelligent-memory">Intelligent Memory <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/intelligent-sales">intelligent sales <span class="filter-link-count" dir="ltr">(22)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/intelligent-self-service">Intelligent Self-Service <span class="filter-link-count" dir="ltr">(22)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/interactions">Interactions <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/interactive-intelligence">Interactive Intelligence <span class="filter-link-count" dir="ltr">(15)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/interactive-intelligence-jacada">Interactive Intelligence Jacada <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/internal-audit">Internal Audit <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/international-finance">International Finance <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/internet-of-everything">Internet of Everything <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/internet-of-things">Internet of Things <span class="filter-link-count" dir="ltr">(74)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/intradiem">Intradiem <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/intuit-quicken">Intuit Quicken <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/inventory-management">inventory management <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/inventory-optimization">Inventory Optimization <span class="filter-link-count" dir="ltr">(5)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/iot">IOT <span class="filter-link-count" dir="ltr">(7)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/iot-and-edge-computing">IoT and Edge Computing <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/iot-operationalintelligence-real-time">IOT, OperationalIntelligence, Real-time <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/ipam">IPAM <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/it">IT <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/it-analytics">IT Analytics <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/it-analytics-performance">IT Analytics & Performance <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/it-controls">IT controls <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/it-cost">IT cost <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/it-governance">IT governance <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/it-performance">IT Performance <span class="filter-link-count" dir="ltr">(178)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/it-performance-management-itpm">IT Performance Management (ITPM) <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/it-research">IT Research <span class="filter-link-count" dir="ltr">(22)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/it-risk-management">IT Risk Management <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/it-service-management">IT Service Management <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/itil">ITIL <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/itops">ITOps <span class="filter-link-count" dir="ltr">(5)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/itsm">ITSM <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/jacada">Jacada <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/jaspersoft">Jaspersoft <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/jd-edwards">JD Edwards <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/jda-software">JDA Software <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/jibe">Jibe <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/jobscience">Jobscience <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/jobvite">JobVite <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/johnson-controls-panoptix">Johnson Controls Panoptix <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/jonova">Jonova <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/journalism">Journalism <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/json">JSON <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/kalido">Kalido <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/kana">Kana <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/kapow">Kapow <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/kapplets">Kapplets <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/karmasphere">Karmasphere <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/kenandy">Kenandy <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/kenexa">Kenexa <span class="filter-link-count" dir="ltr">(8)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/key-performance-indicators">Key Performance Indicators <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/key-performance-indictors">Key Performance Indictors <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/kinaxis">Kinaxis <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/knoahsoft">KnoahSoft <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/knopahsoft">KnopahSoft <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/knowlagent">Knowlagent <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/knowledge-management">Knowledge Management <span class="filter-link-count" dir="ltr">(5)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/kofax">Kofax <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/kognitio">Kognitio <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/kpi">KPI <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/kri">KRI <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/kronos">Kronos <span class="filter-link-count" dir="ltr">(10)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/kxen">KXEN <span class="filter-link-count" dir="ltr">(5)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/kyriba">Kyriba <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/kyriba-financial-performance-management">Kyriba Financial Performance Management <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/lagan">Lagan <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/lan">LAN <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/lasr">LASR <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/lawson">Lawson <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/lawson-software">Lawson Software <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/learning">Learning <span class="filter-link-count" dir="ltr">(22)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/learning-management">Learning Management <span class="filter-link-count" dir="ltr">(66)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/learning-management-system">Learning Management System <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/lease-accounting">Lease Accounting <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/lease-and-tax-accounting">lease and tax accounting <span class="filter-link-count" dir="ltr">(8)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/lease-management">Lease Management <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/leasing">leasing <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/leonardo">Leonardo <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/linux">Linux <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/liveops">LiveOps <span class="filter-link-count" dir="ltr">(7)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/lms">LMS <span class="filter-link-count" dir="ltr">(18)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/local-ly">Local.ly <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/location-analytics">Location Analytics <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/location-intelligence">Location Intelligence <span class="filter-link-count" dir="ltr">(90)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/logi-analytics">Logi Analytics <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/logitech">Logitech <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/logixml">LogiXML <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/longview">LongView <span class="filter-link-count" dir="ltr">(7)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/looker">looker <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/lucent">Lucent <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/lumira">Lumira <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/lxp">LXP <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/machine-conversational-computing">Machine Conversational Computing <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/machine-data">Machine data <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/machine-learning">Machine Learning <span class="filter-link-count" dir="ltr">(36)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/machine-learning-and-cognitive-computing">Machine Learning and Cognitive Computing <span class="filter-link-count" dir="ltr">(21)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/machine-learning-digital-technology">Machine Learning Digital Technology <span class="filter-link-count" dir="ltr">(9)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/machine-learning-operations">Machine Learning Operations <span class="filter-link-count" dir="ltr">(11)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/management">Management <span class="filter-link-count" dir="ltr">(10)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/manticore-technology">Manticore Technology <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/manufacturing">Manufacturing <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/mapr">MapR <span class="filter-link-count" dir="ltr">(6)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/mapreduce">MapReduce <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/market-agenda">Market Agenda <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/market-research">Market Research <span class="filter-link-count" dir="ltr">(14)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/marketing">Marketing <span class="filter-link-count" dir="ltr">(102)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/marketing-automation">Marketing Automation <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/marketing-performance-management">Marketing Performance Management <span class="filter-link-count" dir="ltr">(11)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/marketing-planning">Marketing Planning <span class="filter-link-count" dir="ltr">(5)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/marketing-sales">marketing sales <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/marketo">Marketo <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/markettools">MarketTools <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/marklogic">MarkLogic <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/master-data-management">Master Data Management <span class="filter-link-count" dir="ltr">(26)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/maxager">Maxager <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/maximo">Maximo <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/mdm">MDM <span class="filter-link-count" dir="ltr">(10)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/mdx">MDX <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/meg-whitman">Meg Whitman <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/merced-systems">Merced Systems <span class="filter-link-count" dir="ltr">(7)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/mercer">Mercer <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/meridian-learning-learning-management-systems-lms-">Meridian, Learning, Learning Management Systems, L <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/metrics">Metrics <span class="filter-link-count" dir="ltr">(15)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/microsoft">Microsoft <span class="filter-link-count" dir="ltr">(37)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/microsoft-azure">Microsoft Azure <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/microsoft-excel">Microsoft Excel <span class="filter-link-count" dir="ltr">(7)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/microsoft-office">Microsoft Office <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/microsoft-powerpoint">Microsoft Powerpoint <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/microsoft-windows-phone">Microsoft Windows Phone <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/microsoft-linkedin-cmo-sales">Microsoft, LinkedIn, CMO, Sales <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/microstrategy">MicroStrategy <span class="filter-link-count" dir="ltr">(15)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/middleware">Middleware <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/migration">Migration <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/mlops">MLOps <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/mobile">Mobile <span class="filter-link-count" dir="ltr">(118)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/mobile-analytics">Mobile Analytics <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/mobile-applications">Mobile Applications <span class="filter-link-count" dir="ltr">(14)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/mobile-apps">Mobile Apps <span class="filter-link-count" dir="ltr">(42)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/mobile-bi-analytics-business-intelligence">Mobile BI, Analytics, Business Intelligence <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/mobile-business-intelligence">Mobile Business Intelligence <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/mobile-computing">mobile computing <span class="filter-link-count" dir="ltr">(31)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/mobile-intelligence">Mobile Intelligence <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/mobile-marketing">mobile marketing <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/mobile-marketing-digital-commerce">Mobile Marketing Digital Commerce <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/mobile-technology">Mobile Technology <span class="filter-link-count" dir="ltr">(83)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/mobile-marketing-location-communication">Mobile, Marketing Location Communication <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/mobility">Mobility <span class="filter-link-count" dir="ltr">(49)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/model-building-and-large-language-models">Model Building and Large Language Models <span class="filter-link-count" dir="ltr">(7)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/model-n">Model N <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/model-training">Model Training <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/modeling">Modeling <span class="filter-link-count" dir="ltr">(10)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/monarch">Monarch <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/mpp">MPP <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/mro">MRO <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/mulesoft">Mulesoft <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/multi-cloud">Multi-Cloud <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/na14">NA14 <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/narrative-science">Narrative Science <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/natural-language">Natural Language <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/natural-language-generation">Natural Language Generation <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/natural-language-processing">natural language processing <span class="filter-link-count" dir="ltr">(85)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/navetti">Navetti <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/nec">NEC <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/netapp">NetApp <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/netbase">NetBase <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/netezza">Netezza <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/netops">NetOps <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/netsuite">NetSuite <span class="filter-link-count" dir="ltr">(10)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/netsuite-openai">NetSuite OpenAi <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/netsuite-tribehr-hcm-hr-hrms-customer-experience-z">NetSuite, TribeHR, HCM, HR, HRMS, Customer Experie <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/networking">Networking <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/news">News <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/newvoicemedia">NewVoicemedia <span class="filter-link-count" dir="ltr">(7)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/nexidia">Nexidia <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/nexus-7">Nexus 7 <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/nexxphase">Nexxphase <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/nice-systems">NICE Systems <span class="filter-link-count" dir="ltr">(15)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/nist">NIST <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/nlp">NLP <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/noble-systems">Noble Systems <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/nokia">Nokia <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/nomis-solutions">Nomis Solutions <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/nosql">NoSQL <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/nps">NPS <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/nuview-systems">NuView Systems <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/obamacare">Obamacare <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/obiee">OBIEE <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/obifs">OBIFS <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/object-storage">Object storage <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/observability">Observability <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/office-of-finance">Office of Finance <span class="filter-link-count" dir="ltr">(504)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/office-of-revenue">Office of Revenue <span class="filter-link-count" dir="ltr">(39)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/office-of-sales">Office of Sales <span class="filter-link-count" dir="ltr">(15)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/olap">OLAP <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/omnichannel">Omnichannel <span class="filter-link-count" dir="ltr">(8)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/omnichannel-commerce-digital-kdigital">Omnichannel, Commerce, Digital kDigital <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/on24">On24 <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/onboarding">Onboarding <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/onvisource">OnviSource <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/open-ccaas">Open CCaaS <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/open-pages">Open Pages <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/open-source-software">Open Source Software <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/open-world">Open World <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/openconnect">OpenConnect <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/openpages">OpenPages <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/openspan">OpenSpan <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/opentext">OpenText <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/openworld">OpenWorld <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/opera-solutions">Opera Solutions <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/operational-data-platforms">operational data platforms <span class="filter-link-count" dir="ltr">(39)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/operational-intelligence">Operational Intelligence <span class="filter-link-count" dir="ltr">(180)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/operational-management">Operational Management <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/operational-performance">Operational Performance <span class="filter-link-count" dir="ltr">(696)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/operational-performance-management-opm">Operational Performance Management (OPM) <span class="filter-link-count" dir="ltr">(13)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/operational-planning">Operational Planning <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/operations">Operations <span class="filter-link-count" dir="ltr">(5)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/operations-supply-chain">Operations & Supply Chain <span class="filter-link-count" dir="ltr">(90)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/oracle">Oracle <span class="filter-link-count" dir="ltr">(78)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/oracle-cloud">Oracle Cloud <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/oracle-fusion-hcm">Oracle Fusion HCM <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/orcaeyes">OrcaEyes <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/orchestration">Orchestration <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/order-management">Order Management <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/order-to-cash">Order-to-Cash <span class="filter-link-count" dir="ltr">(12)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/overall-operational-leadership">Overall Operational Leadership <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/oversight-systems">Oversight Systems <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/overtone">Overtone <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/paas">PaaS <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/panopticon">Panopticon <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/paraccel">Paraccel <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/pardot">Pardot <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/partner-experience">Partner Experience <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/partner-management">partner management <span class="filter-link-count" dir="ltr">(12)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/patersons">Patersons <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/paxata">Paxata <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/pay-equity">Pay Equity <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/pay-for-performance">Pay for Performance <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/payroll-management">Payroll Management <span class="filter-link-count" dir="ltr">(17)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/payroll-optimization">Payroll Optimization <span class="filter-link-count" dir="ltr">(7)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/pdi">PDI <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/pentaho">Pentaho <span class="filter-link-count" dir="ltr">(9)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/people-analytics">People Analytics <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/people-cloud">People Cloud <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/peoplefluent">Peoplefluent <span class="filter-link-count" dir="ltr">(9)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/peoplesoft">PeopleSoft <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/performance">Performance <span class="filter-link-count" dir="ltr">(30)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/performance-analytics">Performance Analytics <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/performance-management">Performance Management <span class="filter-link-count" dir="ltr">(89)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/perillon">Perillon <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/personalization">Personalization <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/pervasive">Pervasive <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/pim">PIM <span class="filter-link-count" dir="ltr">(5)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/pim-product-information-management-sales-marketing">PIM, Product Information Management, Sales, Market <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/pipkins">Pipkins <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/pitney-bowes">Pitney Bowes <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/pivotal-hd">Pivotal HD <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/pivotlink">PivotLink <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/planmill">PlanMill <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/planning">Planning <span class="filter-link-count" dir="ltr">(88)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/planview">Planview <span class="filter-link-count" dir="ltr">(6)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/plateau">Plateau <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/platfora">Platfora <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/platform-as-a-service">Platform as a service <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/playbook">Playbook <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/plex">Plex <span class="filter-link-count" dir="ltr">(9)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/plunk">Plunk <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/portfolio-management">Portfolio Management <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/portrait-software">Portrait Software <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/potus-department-of-labor-flsa-part-541-overtime-r">POTUS, Department of Labor, FLSA, Part 541, Overti <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/powercenter">PowerCenter <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/predictive">Predictive <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/predictive-analytics">Predictive Analytics <span class="filter-link-count" dir="ltr">(203)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/predictive-hcm">Predictive HCM <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/predictive-planning">Predictive Planning <span class="filter-link-count" dir="ltr">(49)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/predixion">Predixion <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/price-and-revenue-management">Price and Revenue Management <span class="filter-link-count" dir="ltr">(70)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/price-optimization">Price Optimization <span class="filter-link-count" dir="ltr">(9)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/pricing-and-promotion-management">Pricing and Promotion Management <span class="filter-link-count" dir="ltr">(5)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/pricing-management">pricing management <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/pro">PRO <span class="filter-link-count" dir="ltr">(7)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/process-management">process management <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/process-mining">Process Mining <span class="filter-link-count" dir="ltr">(9)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/procure-to-pay">Procure-to-Pay <span class="filter-link-count" dir="ltr">(11)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/procurement">procurement <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/product-experience-management">product experience management <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/product-information-management">Product Information Management <span class="filter-link-count" dir="ltr">(90)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/professional-services">Professional Services <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/professional-services-automation">Professional Services Automation <span class="filter-link-count" dir="ltr">(9)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/profit-velocity">Profit Velocity <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/profitability">Profitability <span class="filter-link-count" dir="ltr">(14)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/profitability-management">profitability management <span class="filter-link-count" dir="ltr">(11)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/project-management">Project Management <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/project-planning">Project Planning <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/project-portfolio-management">Project Portfolio Management <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/projecthelp">ProjectHelp <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/projectops">ProjectOps <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/projector">Projector <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/promptbook">Promptbook <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/property-technology">Property Technology <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/pros">PROS <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/pros-pricing">PROS Pricing <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/psa">PSA <span class="filter-link-count" dir="ltr">(11)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/publishing">Publishing <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/purchasing">purchasing <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/purchasing-sourcing-payments">Purchasing/Sourcing/Payments <span class="filter-link-count" dir="ltr">(16)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/pv-accelerator">PV Accelerator <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/pxm">PXM <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/qad">QAD <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/qlik">Qlik <span class="filter-link-count" dir="ltr">(5)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/qlik-qlik-sense">Qlik Qlik Sense <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/qlik-qonnections">Qlik Qonnections <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/qlik-analytics-qlik-sense-qlik-business-intelligen">Qlik, Analytics, Qlik Sense, Qlik, Business intell <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/qliktech">Qliktech <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/qlikview">QlikView <span class="filter-link-count" dir="ltr">(10)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/qualtrics">Qualtrics <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/quantrix">Quantrix <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/quinterian">Quinterian <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/quotas">Quotas <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/qvidian">Qvidian <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/r">R <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/ransomware">Ransomware <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/rapide">Rapide <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/rapidminer">Rapidminer <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/rdbms">RDBMS <span class="filter-link-count" dir="ltr">(5)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/real-estate">real estate <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/real-time">Real-time <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/reconciliation">Reconciliation <span class="filter-link-count" dir="ltr">(9)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/recovery">Recovery <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/recruiting">Recruiting <span class="filter-link-count" dir="ltr">(41)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/recurring-revenue">Recurring Revenue <span class="filter-link-count" dir="ltr">(19)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/report">report <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/reporting">Reporting <span class="filter-link-count" dir="ltr">(78)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/reporting-xbrl-ixbrl-document-management">Reporting, XBRL, iXBRL, document management <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/research">Research <span class="filter-link-count" dir="ltr">(16)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/resilience">Resilience <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/responsetek">ResponseTek <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/retail-analytics">Retail Analytics <span class="filter-link-count" dir="ltr">(6)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/revenue">Revenue <span class="filter-link-count" dir="ltr">(8)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/revenue-and-lease-accounting">revenue and lease accounting <span class="filter-link-count" dir="ltr">(15)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/revenue-management">Revenue Management <span class="filter-link-count" dir="ltr">(33)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/revenue-performance">Revenue Performance <span class="filter-link-count" dir="ltr">(11)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/revenue-performance-management">Revenue Performance Management <span class="filter-link-count" dir="ltr">(13)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/revenue-recognition">revenue recognition <span class="filter-link-count" dir="ltr">(8)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/revenue-lease-and-tax-accounting">Revenue, Lease and Tax Accounting <span class="filter-link-count" dir="ltr">(9)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/revolution">Revolution <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/revolution-analtyics">Revolution Analtyics <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/rightnow">RightNow <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/rim">RIM <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/risk">Risk <span class="filter-link-count" dir="ltr">(37)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/risk-compliance-grc">Risk & Compliance (GRC) <span class="filter-link-count" dir="ltr">(17)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/risk-analytics">Risk Analytics <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/riversand">Riversand <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/roambi">Roambi <span class="filter-link-count" dir="ltr">(7)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/robotic-automation">robotic automation <span class="filter-link-count" dir="ltr">(10)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/robotic-finance">robotic finance <span class="filter-link-count" dir="ltr">(38)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/robotic-process-automation">Robotic Process Automation <span class="filter-link-count" dir="ltr">(12)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/roi">ROI <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/rpa">RPA <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/rypple">Rypple <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sop">S&OP <span class="filter-link-count" dir="ltr">(13)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/saas">SaaS <span class="filter-link-count" dir="ltr">(27)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sab-miller">SAB Miller <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/saba">Saba <span class="filter-link-count" dir="ltr">(10)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sage">Sage <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sage-software">Sage Software <span class="filter-link-count" dir="ltr">(9)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sales">Sales <span class="filter-link-count" dir="ltr">(169)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sales-and-operations-planning">Sales and Operations Planning <span class="filter-link-count" dir="ltr">(10)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sales-cloud">Sales Cloud <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sales-coaching">Sales Coaching <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sales-commission">Sales Commission <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sales-compensation">Sales Compensation <span class="filter-link-count" dir="ltr">(12)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sales-effectiveness">Sales Effectiveness <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sales-enablement">sales enablement <span class="filter-link-count" dir="ltr">(16)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sales-enablement-and-execution">Sales Enablement and Execution <span class="filter-link-count" dir="ltr">(10)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sales-engagement">Sales Engagement <span class="filter-link-count" dir="ltr">(25)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sales-force-automation">Sales Force Automation <span class="filter-link-count" dir="ltr">(13)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sales-forecasting">Sales Forecasting <span class="filter-link-count" dir="ltr">(11)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sales-operations">Sales Operations <span class="filter-link-count" dir="ltr">(12)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sales-performance">Sales Performance <span class="filter-link-count" dir="ltr">(446)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sales-performance-management">Sales Performance Management <span class="filter-link-count" dir="ltr">(77)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sales-performance-management-spm">Sales Performance Management (SPM) <span class="filter-link-count" dir="ltr">(13)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sales-planning">Sales Planning <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sales-planning-and-analytics">Sales Planning and Analytics <span class="filter-link-count" dir="ltr">(18)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sales-tax">Sales Tax <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sales-marketing-sales-performance-management-marke">Sales, Marketing, Sales Performance Management, Ma <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sales-sfa-spm-sales-performance-management-sales-c">Sales, SFA, SPM, Sales Performance Management, Sal <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/salescloud">SalesCloud <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/salesforce-com">Salesforce.com <span class="filter-link-count" dir="ltr">(58)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sap">SAP <span class="filter-link-count" dir="ltr">(76)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sap-epm">SAP EPM <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sap-hana">SAP HANA <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sap-insider">SAP insider <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sap-lumira">SAP Lumira <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sap-netweaver">SAP NetWeaver <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sap-predictive-analytics">SAP predictive analytics <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sap-teched">SAP TechEd <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sapphire">SAPPHIRE <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sarbanes-oxley">Sarbanes Oxley <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sas">SAS <span class="filter-link-count" dir="ltr">(26)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sas-institute">SAS institute <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sasb">SASB <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sase">SASE <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/savi">Savi <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/scm">SCM <span class="filter-link-count" dir="ltr">(6)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/scm-demand-planning">SCM Demand Planning <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/scout-rfp">Scout RFP <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/scribe-software">Scribe Software <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/search">Search <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sec">SEC <span class="filter-link-count" dir="ltr">(19)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sec-software">SEC Software <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/secfinops">SecFinOps <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/secops">SecOps <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/security">Security <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/security-posture">Security Posture <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sedar">SEDAR <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/self-service">Self-service <span class="filter-link-count" dir="ltr">(41)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/semantic-model">semantic model <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/service-supply-chain">Service & Supply Chain <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/service-cloud">Service Cloud <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/servigistics">Servigistics <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sfa">SFA <span class="filter-link-count" dir="ltr">(24)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sfdc">SFDC <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sharepoint">SharePoint <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/shoretel">ShoreTel <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/siemens-enterprise-communications">Siemens Enterprise Communications <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/signal-demand">Signal Demand <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/silverpop">SilverPop <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/simba">Simba <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/smart-financial-close">Smart Financial Close <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/smart-manufacturing">Smart Manufacturing <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/smart-phones">Smart Phones <span class="filter-link-count" dir="ltr">(5)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/smart-watches">Smart Watches <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/smarter-analytics">Smarter Analytics <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/smarter-workforce">Smarter Workforce <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/smb">SMB <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/snaplogic">SnapLogic <span class="filter-link-count" dir="ltr">(5)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/snowfly">Snowfly <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/soa">SOA <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/soc">SOC <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/social">Social <span class="filter-link-count" dir="ltr">(13)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/social-business">Social Business <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/social-collaboration">Social Collaboration <span class="filter-link-count" dir="ltr">(17)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/social-crm">Social CRM <span class="filter-link-count" dir="ltr">(128)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/social-dynamx">Social Dynamx <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/social-media">Social Media <span class="filter-link-count" dir="ltr">(365)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/social-media-analytics">Social Media Analytics <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/social-media-intelligence">Social Media Intelligence <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/social-recognition">Social Recognition <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/socialtext">SocialText <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sococare">SoCoCare <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/softscape">Softscape <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/software">Software <span class="filter-link-count" dir="ltr">(8)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/software-as-a-service">Software as a Service <span class="filter-link-count" dir="ltr">(5)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sourcing">sourcing <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sovereign-cloud">Sovereign Cloud <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sox">SOX <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/spago-solutions">Spago Solutions <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/spatial-data-quality">Spatial Data Quality <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/speech-analytics">Speech Analytics <span class="filter-link-count" dir="ltr">(177)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/splunk">Splunk <span class="filter-link-count" dir="ltr">(8)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/spm">SPM <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/spotfire">Spotfire <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/spreadsheets">Spreadsheets <span class="filter-link-count" dir="ltr">(10)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/spss">SPSS <span class="filter-link-count" dir="ltr">(9)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sql-h">SQL-H <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sqlstream">SQLstream <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/star-analytics">Star Analytics <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/statistics">Statistics <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/stibo-systems">Stibo Systems <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/stratahadoop">Strata+Hadoop <span class="filter-link-count" dir="ltr">(56)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/streaming-analytics">Streaming Analytics <span class="filter-link-count" dir="ltr">(28)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/streaming-data">Streaming Data <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/streaming-data-events">Streaming Data & Events <span class="filter-link-count" dir="ltr">(32)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/subscriber-experience">Subscriber Experience <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/subscription-billing">Subscription Billing <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/subscription-management">Subscription Management <span class="filter-link-count" dir="ltr">(68)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/subscription-recurring-revenue-zuora">Subscription, Recurring Revenue, Zuora <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/successfactors">SuccessFactors <span class="filter-link-count" dir="ltr">(12)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sumo-logic">Sumo Logic <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sumtotal">SumTotal <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sumtotal-systems">SumTotal Systems <span class="filter-link-count" dir="ltr">(11)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sungard">Sungard <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sunsystems">Sunsystems <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/supplier-relationship-management">supplier relationship management <span class="filter-link-count" dir="ltr">(5)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/supply-chain">Supply Chain <span class="filter-link-count" dir="ltr">(12)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/supply-chain-management">supply chain management <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/supply-chain-performance">Supply Chain Performance <span class="filter-link-count" dir="ltr">(253)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/supply-chain-planning">Supply Chain Planning <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/surface">Surface <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/surveys">Surveys <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sustainability">Sustainability <span class="filter-link-count" dir="ltr">(38)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sustainability-and-esg">Sustainability and ESG <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sustainability-management">Sustainability Management <span class="filter-link-count" dir="ltr">(7)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/swordciboodle">SwordCiboodle <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sybase">Sybase <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sybase-iq">Sybase IQ <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sybase-mobile-industry">Sybase. Mobile Industry <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/syncsort">Syncsort <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/synygy">Synygy <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/tableau">Tableau <span class="filter-link-count" dir="ltr">(13)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/tableau-software">Tableau Software <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/tablets">Tablets <span class="filter-link-count" dir="ltr">(6)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/tagetik">Tagetik <span class="filter-link-count" dir="ltr">(5)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/talemetry">Talemetry <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/talend">Talend <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/talent-acquisition">Talent Acquisition <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/talent-generation">Talent Generation <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/talent-management">Talent Management <span class="filter-link-count" dir="ltr">(133)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/taleo">Taleo <span class="filter-link-count" dir="ltr">(7)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/talx">TALX <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/tamr">Tamr <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/tanner-labs">Tanner Labs <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/tax">Tax <span class="filter-link-count" dir="ltr">(35)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/tax-compliance">tax compliance <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/tax-department">tax department <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/tax-planning">tax planning <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/tax-software">Tax Software <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/tax-datawarehouse">Tax-Datawarehouse <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/tax-tax-provision-transfer-pricing">tax; tax provision; transfer pricing <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/tcm">TCM <span class="filter-link-count" dir="ltr">(6)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/technology">Technology <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/technology-innovation">Technology Innovation <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/technology-innovation-award">Technology Innovation Award <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/technology-spending">Technology Spending <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/technology-vendors">Technology Vendors <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/telephony">Telephony <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/teradata">Teradata <span class="filter-link-count" dir="ltr">(16)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/teradata-aster">Teradata Aster <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/text-analytics">Text Analytics <span class="filter-link-count" dir="ltr">(182)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/tez">Tez <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/thomson-reuters-multinational">Thomson-Reuters multinational <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/threat-landscape">Threat Landscape <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/threat-management">Threat Management <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/tibco">Tibco <span class="filter-link-count" dir="ltr">(5)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/tidemark">Tidemark <span class="filter-link-count" dir="ltr">(5)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/time-attendance">Time & Attendance <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/tm">TM <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/tm1">TM1 <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/topic">Topic <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/total-compensation-management">Total Compensation Management <span class="filter-link-count" dir="ltr">(46)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/total-rewards-management">total rewards management <span class="filter-link-count" dir="ltr">(15)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/towers-watson">Towers Watson <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/towers-watson-talent-management">Towers Watson Talent Management <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/training">Training <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/transcom">Transcom <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/transera">Transera <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/treasury">Treasury <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/tribehr">TribeHR <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/trifacta">Trifacta <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/twitter">Twitter <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/ucaas">UCaaS <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/uda">UDA <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/ui">UI <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/ukg">UKG <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/ultimate">Ultimate <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/ultimate-software">Ultimate Software <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/unanet-technologies">Unanet Technologies <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/uncategorized">Uncategorized <span class="filter-link-count" dir="ltr">(103)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/unica">Unica <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/unicorns">Unicorns <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/unified-communications">Unified Communications <span class="filter-link-count" dir="ltr">(83)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/unit4">Unit4 <span class="filter-link-count" dir="ltr">(11)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/upstream-works">Upstream Works <span class="filter-link-count" dir="ltr">(6)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/us-gaap">US-GAAP <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/us-gaap-accounting">US-GAAP accounting <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/usability">Usability <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/use-cases">Use Cases <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/ux">UX <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/value-index">Value Index <span class="filter-link-count" dir="ltr">(11)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/value-added-tax">Value-Added Tax <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/vantage-platform">Vantage platform <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/varicent">Varicent <span class="filter-link-count" dir="ltr">(5)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/vat">VAT <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/vendavo">Vendavo <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/vendavo-price-pricing-optimization-revenue-custome">Vendavo, price, pricing, optimization, revenue, cu <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li 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"@type": "BlogPosting", "headline": "Vector Search and RAG Improve Trust in Generative AI", "mainEntityOfPage": "https://blog.isg-research.net/vector-search-and-rag-improve-trust-in-generative-ai", "datePublished": "31/10/2023", "dateModified": "31/10/2023", "articleBody": "I previously discussed the trust and accuracy limitations of large language models, suggesting that data and analytics vendors provide guidance about potentially inaccurate results and the risks of creating a misplaced level of trust. In the months that have followed, we are seeing some clarity from these vendors about the approaches organizations can take to increase trust and accuracy when developing applications that incorporate generative AI, including fine-tuning and prompt engineering. It is clear that one of the most important approaches will be the use of vector search to augment generative AI with context from enterprise content and data via a concept known as retrieval-augmented generation. Vector search and RAG have taken the data platform market by storm as providers of both operational and analytic data platforms position products to benefit from the huge surge of interest in generative AI. This focus helps organizations develop applications that combine generative AI and enterprise data. Vendors already supporting vector search have accelerated marketing efforts, while others have fast-tracked capabilities to store and process vectors. Before explaining the technical aspects of vector search and RAG, it is worth recapping some of the previously mentioned limitations of LLMs to understand why vector search and RAG are so important to help overcome them. As my colleague Dave Menninger previously explained, generative AI creates content such as text, digital images, audio, video or even computer programs and models with artificial intelligence. We expect the adoption of generative AI to grow rapidly, asserting that through 2025, one-quarter of organizations will deploy generative AI embedded in one or more software applications. The large language models that enable text-based generative AI can increase productivity by improving natural language processing. However, they are not without fault. LLMs generate content that is grammatically valid rather than factually accurate. As a result, the content generated by LLMs can include factual inaccuracies such as fictitious data and source references. The reason is that foundation models only have “knowledge” of the information they are trained on. This could be enormous amounts of public information, but public LLMS do not have access to an organization’s private data and content. A public LLM can provide accurate responses about generic questions for which there is a large corpus of freely available information, but ask it a question that requires private data that it has not been trained on — for instance, about a particular company’s latest sales figures — and it will generate text that is plausible but has no basis in factual data. A useful analogy for thinking about the limitations of generative AI is human memory. Training and tuning a model’s foundational functionality are akin to creating the implicit memories humans use to carry out functions without conscious thought. An example is learning how to drive a car. Once the functional aspects of operating a vehicle have been embedded in implicit memory, people can drive a car without consciously thinking about how to do so. But, implicit knowledge of how to operate a vehicle is not enough to complete a journey. Knowing how to drive a car does not equate to knowing which routes to avoid when traveling from point A to point B. This is the job of conscious, explicit memory, which provides the context to complete the journey without making a wrong turn. If a driver makes the same journey enough times, knowledge of the route becomes an implicit memory, and they can do so almost without thinking about it. This is equivalent to tuning a foundation model using private data. A model trained on private data can be extremely effective at a specific task, but the result is a model that is finely tuned yet limited in scope. Knowing implicitly how to drive from point A to point B is not much use when your destination is point C. What is required is to augment foundation models with real-life data and context from enterprise information. One way of doing this is via prompt engineering, a process of providing context to the question as it is asked. Prompts can require the model to provide a response that matches a desired format or to provide specific data or information to be used in the response. Although prompt engineering augments the information on which LLMs have been trained, the augmentation is temporary. Since LLMs are stateless, the information contained within the prompt is not retained and needs to be provided every time the question is asked. Prompt engineering provides short-term context within the bounded scope of a single interaction. As such, it can be thought of as the equivalent of short-term working memory – used by the brain to retain information for a short period and soon forgotten if not transferred to long-term, conscious, explicit memory. An analogous example would be a driver remembering where they left their car keys. The equivalent of conscious, explicit memory for generative AI can be provided by augmenting foundation models with real-life data and context from enterprise information via vector search and RAG. Vectors — or vector embeddings — are multi-dimensional mathematical representations of features or attributes of raw data, including text, images, audio or video. Vector search utilizes vector embeddings to perform similarity searches by enabling rapid identification and retrieval of similar or related data. Vector search supports natural language processing and recommendation systems that find and recommend products similar in function or style, either visually or based on written descriptions. Vectors and vector search can also improve accuracy and trust with generative AI via RAG, which is the process of retrieving vector embeddings representing factually accurate and up-to-date information from a database and combining it with text automatically generated by the LLM. RAG provides an LLM with a constantly updated source of private data and information. It can provide the equivalent of knowing how to drive from point A to point B, plus any combination of routes. Whether RAG is best performed using a specialist vector database or a general-purpose database capable of storing and processing vectors is a matter of debate and a subject I will return to in the future. Either way, I assert that through 2026, almost all organizations developing applications based on generative AI will explore vector search and retrieval-augmented generation to complement foundation models with proprietary data and content. It is likely that organizations will use a combination of approaches to improve trust and accuracy with generative AI, depending on the use case. I recommend that all organizations investigate the potential use cases for each approach and seek vendors that can assist in implementing fine-tuning, prompt engineering, vector search and RAG. Different tasks have different levels of reliance on long-term implicit memory, long-term explicit memory or short-term working memory. To complete a journey, a person needs to remember how to drive a car, where they put their keys and the best route to get to their destination. All of these are essential, but each is useless without the others. Regards, Matt Aslett", "image": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/Vector_Search_and_RAG_Improve_Trust_in_Generative_AI.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "Matt Aslett", "url": "https://blog.isg-research.net/author/matt-aslett", "sameAs": "https://www.linkedin.com/in/matt-aslett/" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "blog.isg-research.net", "logo": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/ISG_Software_Research_Logo_Color@2x.png" } } }</script> <script type="application/ld+json"> { "@context": "http://schema.org", "@type": "BlogPosting", "headline": "The Buyers Guide for Data Observability Classifies and Rates Vendors", "mainEntityOfPage": "https://blog.isg-research.net/the-buyers-guide-for-data-observability-classifies-and-rates-vendors", "datePublished": "26/10/2023", "dateModified": "26/10/2023", "articleBody": "I am happy to share insights gleaned from our latest Buyers Guide, an assessment of how well vendors’ offerings meet buyers’ requirements. The Ventana Research 2023 Data Observability Buyers Guide is the distillation of a year of market and product research by Ventana Research. Drawing on our Benchmark Research, we apply a structured methodology built on evaluation categories that reflect the real-world criteria incorporated in a request for proposal to data vendors supporting the spectrum of data observability. Using this methodology, we evaluated vendor submissions in seven categories: five relevant to the product experience ﹘ Adaptability, Capability, Manageability, Reliability and Usability ﹘ and two related to the customer experience ﹘ TCO/ROI and vendor Validation. This research-based index evaluates the full business and information technology value of data observability software offerings. I encourage you to learn more about our Buyers Guide and its effectiveness as a vendor selection and RFI/RFP tool. This Buyers Guide research evaluates the following vendors that offer products for data observability as we define it: Acceldata, Collibra, DataKitchen, IBM, Monte Carlo, Precisely and Stonebranch. Two of these seven suppliers responded positively to our requests for information and provided completed questionnaires and demonstrations to aid our analysis of their data observability products. Along with briefings and information where provided, all online material that was generally available was used for the analysis. This report includes products generally available as of August 30, 2023. The following vendors declined to participate or did not respond to our invitation: Collibra, IBM, Precisely and Stonebranch. To organizations considering products from these vendors, we recommend extra scrutiny as part of the software assessment because they did not make their technology available for the Value Index evaluation process. Unlike many IT analyst firms that rank vendors from an IT-only perspective or focus on a specific type of data observability, Ventana Research has designed the Buyers Guide to provide a balanced perspective, rooted in an understanding of business drivers and needs. This approach not only reduces cost and time but also minimizes the risk of making a decision that is bad for the business. Using the Buyers Guide will enable your organization to achieve levels of efficiency and effectiveness needed to optimize data observability. We urge organizations to do a thorough job of evaluating data observability offerings in this Buyers Guide as both the results of our in-depth analysis of these vendors and as an evaluation methodology. The Value Index can be used to evaluate existing suppliers, plus provides evaluation criteria for new projects. Using it can shorten the cycle time for an RFP. The Buyers Guide for Data Observability in 2023 finds Monte Carlo first on the list, followed by IBM and Acceldata. Companies that rated in the top three of any category ﹘ including the product and customer experience dimensions ﹘ earn the designation of Leader. The Leaders in Product Experience are: Monte Carlo. IBM. Acceldata. The Leaders in Customer Experience are: IBM. Monte Carlo. Collibra. The Leaders across any of the seven categories are: IBM and Monte Carlo, which has achieved this rating in seven of the seven categories. Acceldata in four categories. Collibra in two categories. Precisely in one category. The overall performance chart provides a visual representation of how vendors rate across product and customer experience. Vendors with products scoring higher in a weighted rating of the five product experience categories place farther to the right. The combination of ratings for the two customer experience categories determines their placement on the vertical axis. As a result, vendors that place closer to the upper-right are “exemplary” and rated higher than those closer to the lower-left and identified as vendors of “merit.” Vendors that excelled at customer experience over product experience have an “assurance” rating, and those excelling instead in product experience have an “innovative” rating. Note that close vendor scores should not be taken to imply that the packages evaluated are functionally identical or equally well-suited for use by every organization or process. Although there is a high degree of commonality in how organizations handle data observability, there are many idiosyncrasies and differences that can make one vendor’s offering a better fit than another. After more than a decade of technological advances, all the products we evaluated are feature-rich, but not all the capabilities they offer are equally valuable to users. Moreover, the existence of too many capabilities may be a negative factor for an organization if it introduces unnecessary complexity. Nonetheless, an organization may decide that a larger number of options is a plus, especially if some of them match its established practices or better support a new initiative that is driving the purchase of new software. Features, functions and vendor assessments are not the only deciding factors. For example, an organization may face budget constraints such that the TCO evaluation can tip the scale to one vendor or another. This is where the Value Index methodology and appropriate weighting can be applied to determine the best fit of vendors and products to an organization’s specific needs. Our firm has made every effort to encompass in this Buyers Guide the overall product and customer experience from our data observability blueprint, which we believe reflects what a well-crafted RFP should contain. Even so, there may be additional areas that affect which vendor and products best fit an organization’s particular requirements. Therefore, while this research is complete as it stands, utilizing it in your own organizational context is critical to ensure that products deliver the highest level of support for your projects. You can find more details on our site as well as in the Buyers Guide Report. Regards, Matt Aslett", "image": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/The_Buyers_Guide_for_Data_Observability_Classifies_and_Rates_Vendors.png.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "Matt Aslett", "url": "https://blog.isg-research.net/author/matt-aslett", "sameAs": "https://www.linkedin.com/in/matt-aslett/" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "blog.isg-research.net", "logo": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/ISG_Software_Research_Logo_Color@2x.png" } } }</script> <script type="application/ld+json"> { "@context": "http://schema.org", "@type": "BlogPosting", "headline": "Data Observability Buyers Guide: Market Observations", "mainEntityOfPage": "https://blog.isg-research.net/data-observability-buyers-guide-market-observations", "datePublished": "25/10/2023", "dateModified": "25/10/2023", "articleBody": "The 2023 Ventana Research Buyers Guide for Data Observability research enables me to provide observations about how the market has advanced. The need to monitor the pipelines and processes in data-processing and analytics environments has driven the emergence of a new category of software: data observability. Inspired by the observability platforms that provide an environment for monitoring metrics, traces and logs to track application and infrastructure performance, data observability software provides an environment for monitoring the quality and reliability of data used for analytics and governance projects. There has been a Cambrian explosion of data observability software vendors in recent years and while each is slightly different, they also have fundamental capabilities in common. To monitor and measure anything, it must first be instrumented, so a baseline requirement for data observability software is that it collects and measures metrics from data pipelines, data warehouses, data lakes and other data-processing platforms. Data observability software also collects, monitors and measures information on data lineage (dependencies between data), metadata (describing the attributes of the data, such as its age, volume, format, schema), and logs of human- or machine-based interaction with the data. In addition to collecting and monitoring this information, some data observability software also enables the creation of models that can be applied to the various metrics, logs, dependencies and attributes to automate the detection of anomalies. Data observability software may also offer root cause analysis and the provision of alerts, explanations and recommendations to enable data engineers and data architects to accelerate the correction of issues. Data observability addresses one of the most significant impediments to generating value from data. Maintaining data quality and trust is a perennial data management challenge, often preventing organizations from operating at the speed of business. Almost two-thirds (64%) of participants in Ventana Research’s Analytics and Data Benchmark Research cite reviewing data for quality and consistency issues as the most time-consuming task in analyzing data. The importance of trust in data has arguably never been greater. As organizations aspire to be more data-driven, it is critical to trust the data used to make those decisions. Without data quality processes and tools, organizations may make decisions based on old, incomplete, incorrect or poorly organized data. Assessing the quality of data used to make business decisions is not only more important than ever but also increasingly difficult given the growing range of data sources and the volume of data that needs to be evaluated. Poor data quality processes can result in security and privacy risks as well as unnecessary data storage and processing costs due to data duplication. Monitoring the quality and reliability of data used for analytics and governance projects is not a new challenge. Data quality software has been extant for decades. Organizations that have made investments in data quality might reasonably ask whether they need data observability, while those that have invested in data observability might wonder whether they can eschew traditional data quality tools. To understand the difference between data quality and data observability it is important to recognize data quality is both a discipline and a product category. As a discipline, data quality refers to the processes, methods and tools used to measure the suitability of a dataset for a specific purpose. The precise measure of suitability will depend on the individual use case, but important characteristics include accuracy, completeness, consistency, timeliness and validity. The data quality product category is comprised of the tools used to evaluate data in relation to these characteristics. Data observability, meanwhile, has emerged as a separate product category. It includes software focused on automating the monitoring of data to assess its health based on key attributes including freshness, distribution, volume, schema and lineage. The use of automation expands the volume of data that can be monitored while also improving efficiency compared to manual data monitoring and management by automating data quality checks and recommended remediation actions. As such, automation is often cited as a distinction between data observability and data quality software. Focusing on automation as a distinction, however, relies on an outdated view of data quality software. Although data quality software has historically provided users with an environment to manually check and correct data quality issues, the use of machine learning (ML) to automate the monitoring of data is also being integrated into data quality tools and platforms. Automating data monitoring ensures it is complete, valid and consistent as well as relevant and free from duplication. Automation using ML is not, therefore, a defining characteristic that separates data quality from data observability. A clearer distinction can be drawn from the scope and focus of the functionality. Data quality software is concerned with the suitability of the data to a given task. In comparison, data observability is concerned with the reliability and health of the overall data environment. Data observability tools monitor not just the data in an individual environment for a specific purpose at a given point in time, but also the associated upstream and downstream data pipelines. In doing so, data observability software ensures that data is available and up to date, avoiding downtime caused by lost or inaccurate data due to schema changes, system failures or broken data pipelines. To put it another way, while data quality software is designed to help users identify and resolve data quality problems, data observability software is designed to automate the detection and identification of the causes of data quality problems, potentially enabling users to prevent data quality issues before they occur. The two are largely complementary. For example, when the data being assessed remains consistent, data quality tools might not detect a failed pipeline until the data has become out of date. Data observability tools could detect the failure long before the data quality issue arises. Conversely, a change in address might not be identified by data observability tools if the new information adhered to the correct schema. It could be detected — and remediated — using data quality tools. The reciprocal nature of data quality and data observability software products is supported by the fact that some vendors offer products in both categories while others offer products that could be said to offer functionality associated with both data observability and data quality. In addition to the emergence of standalone data observability software specialists, we also see this functionality being included in wider DataOps platforms. This is a trend we expect to continue. Through 2025, data observability will continue to be a priority for the evolution of data operations products as vendors deliver more automated approaches to data engineering and improving trust in enterprise data. The relative immaturity of the market for data observability software means that it is difficult for organizations today to evaluate potential suppliers. Only a handful of vendors met the inclusion criteria for this Buyer’s Guide, while there is a very long list of Vendors of Note that were considered. Many of the emerging vendors are likely to be acquired while a few will fall by the wayside. That should not stop organizations from evaluating the potential benefits of data observability, however. It has a critical role to play in evaluating the performance and reliability of data pipelines, as well as the quality and validity of data, to deliver the benefits of investment in data and analytics. The evolution of data observability is still in its early stages. Potential adopters of data observability are advised to pay close attention and evaluate purchases carefully. Some data observability products offer quality resolution and remediation functionality traditionally associated with data quality software, albeit not to the same depth and breadth. Additionally, some vendors previously associated with data quality have adopted the term data observability but may lack the depth and breadth of pipeline monitoring and error detection capabilities. This research evaluates the following vendors that offer products that address key elements of data observability as we define it: Acceldata, Collibra, DataKitchen, IBM, Monte Carlo, Precisely and Stonebranch. You can find more details on our site as well as in the Buyers Guide Market Report. Regards, Matt Aslett", "image": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/Data_Observability_Buyers_Guide_Market_Observations.png.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "Matt Aslett", "url": "https://blog.isg-research.net/author/matt-aslett", "sameAs": "https://www.linkedin.com/in/matt-aslett/" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "blog.isg-research.net", "logo": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/ISG_Software_Research_Logo_Color@2x.png" } } }</script> <script type="application/ld+json"> { "@context": "http://schema.org", "@type": "BlogPosting", "headline": "NICE Puts Knowledge at the Heart of Its AI Strategy", "mainEntityOfPage": "https://blog.isg-research.net/nice-puts-knowledge-at-the-heart-of-its-ai-strategy", "datePublished": "25/10/2023", "dateModified": "25/10/2023", "articleBody": "NICE held its annual Analyst Summit this month in the magnificent high-altitude mountains of Peru, a fantastic environment in which to hear top executives share insights into the changing company and industry. NICE has been very successful in navigating the industry transition from voice-centric interactions to a digital-first posture. Consumer behavior is one of the underlying causes of this shift, forcing many organizations to rethink customer experience strategies. Every aspect of the technology stack is affected by the shift to digital, along with the transformative effects of artificial intelligence, automation and cloud applications. From a practical perspective, organizations with contact centers are looking to vendors like NICE for guidance on outfitting those centers with new tools for new problems: managing remote workforces, for example, or making knowledge resources available to agents and self-service systems. NICE’s leadership believes that new technologies are demonstrating a transformative impact on enterprise operations. In particular, the emergence of AI has significantly accelerated conversations about modernizing customer service activities and processes. NICE highlights three elements that the company brings to the table to help guide organizations in building (or rebuilding) an advanced customer experience program focused on the contact center. According to NICE, an advanced CX program requires an “interaction-centric” cloud platform. This platform seamlessly connects interactions across all channels and includes a unified data and application framework. An open ecosystem that operates across departments, the platform primarily supports the contact center but also includes relevant, customer-adjacent people and processes to create broad, integrated CX. The ideal platform integrates all of the CX resources within an organization: media channels, data sources, applications and knowledge from existing silos. The role of the platform is to maintain awareness of the customer’s intent and the surrounding context of the interaction. CX programs also need to maximize AI, specifically machine learning and generative AI that is purpose-built for CX applications and trained on data sources relevant to the CX domain. Programs must be secure, built with guardrails and able to align with the branding needs of the organization deploying it. NICE submits that CXone is a platform capable of fulfilling these needs. The market clearly supports this premise, as demonstrated by NICE’s strong growth in recent years, both financially and in terms of the number of agent seats and interactions under its control. And NICE’s offering ranked first in the industry in Ventana Research’s own Buyers Guides for both Contact Center platforms (i.e., tools with an automatic call distributor) and for broader Contact Center Suites. NICE has dedicated resources to develop its AI solution, Enlighten AI, building out applications that address specific use cases for different types of interactions. Enlighten Copilot, for example, supports live agents by giving them real-time assistance during interactions and summarizes the content of interactions afterward. This one-two punch provides better experiences by boosting the agent’s capabilities and controls costs by reducing agent work time between interactions. For self-service, Enlighten Autopilot powers self-learning virtual agents. The inputs for both applications are managed by the unified platform with models that are built from NICE’s store of domain-specific industry interactions. All of Enlighten’s activities are integrated across the CXone suite, so its insights are consistently applied to all interactions throughout a customer’s journey. NICE’s progression with AI appears to be moving from the broadest notion of how it will be used – essentially as part of an analytics and data strategy – to one more focused on finding specific problems that it can solve and applying it to those use cases, like supervisor desktop applications and the management of core company knowledge resources. NICE also demonstrates a commitment to knowledge management as a key element of its strategy going forward. One speaker asserted that knowledge management will change from being a “backwater” to being the area that fundamentally controls an organization’s AI. That’s a strong assertion, but one we believe is fundamentally correct and insightful. Another executive noted that for most companies, the corpus of business knowledge available is diffuse and unfocused. More than that, it is stuck in an outdated mode where much of the information useful in service contexts is locked away in sources like CMS tools, which are optimized for marketing content rather than service. Bridging this gap should be a priority for organizations and is one of the most compelling arguments for applying AI to the knowledge problem. Our findings echo this. Ventana Research asserts that by 2025, one-third of small and mid-sized organizations will deploy AI-based knowledge management systems for the dual purpose of assisting agents in real-time and boosting customers’ use of self-service. NICE offers Expert, a knowledge product that unifies desktop and cloud operations by incorporating Enlighten AI. The company has devoted considerable resources to building Expert since it was brought in-house through the acquisition of MindTouch several years ago. NICE appears to see knowledge management as an essential complement to its operational AI tools, and it is correct: Without robust and timely access to knowledge, it is not possible to get the benefits of advanced self-service tools like conversational chatbots or intent-driven agent guidance. These systems work in tandem, which means an organization can’t just patch one AI application onto its existing processes without working through the entire cycle of where knowledge resides and how it is created, parceled out and updated. We agree that knowledge management has been a backwater, but also that the AI revolution is a once-in-a-generation opportunity for organizations to tackle this knotty problem and turn KM into a key asset. NICE’s customers who spoke at the event appeared to be wrestling with some of these issues as well, describing an approach to AI as embryonic so far. They appear to be looking for ways to do proof-of-concept tests with many of the newer NICE components, looking for use cases that provide realistic value and allow users to become comfortable with the controls, guardrails and unique characteristics of tools that are very different from the traditional mainstream standard. When we step back and ask why NICE has been so successful during a tumultuous period of drastic change in technology and market dynamics, there appear to be two key reasons. First, the company’s leadership clearly understands the challenges posed by external circumstances – changes in consumer behavior, possible economic headwinds and the sudden emergence of transformational technology (e.g., AI, automation, the cloud and omnichannel communications). CEO Barak Eilam has, since the acquisition of inContact in 2016, guided a consistent shift away from NICE’s roots in agent optimization towards something better thought of as interaction optimization or relationship optimization. NICE can be technologically agnostic to the extent that it can add or subtract elements of the core, so long as it focuses on the big picture of providing companies with an overall platform that incorporates the best available components addressing the widest spectrum of problems and use cases. Second, since the acquisition of inContact, NICE has acquired companies with talent and intellectual property to fill portfolio gaps as they emerge. MindTouch was one example in KM; ContactEngine was another that brought in significant conversational AI. This past month, NICE also announced the intent to acquire LiveVox, largely because of its strong capabilities in outbound, proactive outreach and messaging. NICE has a strong enough financial position to be able to consider filling gaps as they arise in a timely way. NICE is a contact center vendor well-equipped to navigate the complexities of the broad customer experience, even well beyond the contact center. Like the Peruvian alpaca, NICE has evolved to be amazingly alert to its environment, capable of thriving as that environment changes and a key part of a balanced and varied ecosystem. As the landscape continues to change, buyers should view NICE’s Expert as one of the industry’s strongest KM tools and the CXone platform as a set of tools that satisfy both contact center and broader CX needs. For more information on related subjects, see the recent Analyst Perspective on Optimizing Customer Communication Channels, or visit our Customer Experience Expertise Area. Regards, Keith Dawson", "image": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/NICE_Puts_Knowledge_at_the_Heart_of_Its_AI_Strategy.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "Keith Dawson", "url": "https://blog.isg-research.net/author/keith-dawson", "sameAs": "www.linkedin.com/in/keith-dawson-b17b71" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "blog.isg-research.net", "logo": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/ISG_Software_Research_Logo_Color@2x.png" } } }</script> <script type="application/ld+json"> { "@context": "http://schema.org", "@type": "BlogPosting", "headline": "Stream and Event Processing Require Real-Time Analytics", "mainEntityOfPage": "https://blog.isg-research.net/stream-and-event-processing-require-real-time-analytics", "datePublished": "25/10/2023", "dateModified": "25/10/2023", "articleBody": "In my past perspectives, I’ve written about the evolution from data at rest to data in motion and the fact that you can’t rely on dashboards for real-time analytics. Organizations are becoming more and more event-driven and operating based on streaming data. As well, analytics are becoming more and more intertwined with operations. More than one-fifth of organizations (22%) describe their analytics workloads as real time in our Data and Analytics Benchmark Research and nearly half (47%) of organizations report their IoT workloads require seconds or sub-second latency. If organizations can’t rely on dashboards for real-time analytics, what should they consider? Let’s start with a consideration of the requirements for real-time analytics. Clearly, latency is one consideration. The analysis must be completed quickly enough to meet the requirements of the specific use case. Some use cases require very low latency, such as digital ad servers. Other use cases, such as social media monitoring, are less demanding. But in either case, one of the key requirements is automation. Manually monitoring continuous streams of data is not a very effective or efficient way to analyze and respond to events as they occur. Another question is how much context is needed to provide an accurate analysis. In some cases, a single event might be sufficient, for instance if you are monitoring temperature and need to know if any single reading is outside normal operating limits. In other cases, you may need a window of events, such as social media monitoring, to see if sentiment has shifted over a period of time. And in still other situations, you may need large amounts of historical context, for example a customer’s past purchase history, so you can make product recommendations as they are browsing your site or speaking with a customer service agent. The options to handle these requirements fall into two broad categories: data platforms and stream-processing frameworks. My colleague, Matt Aslett, has written about and evaluated operational data platforms. Data platform vendors, both relational and non-relational (NoSQL), have been reducing the latency and increasing the throughput of their systems. In many use cases, these capabilities may be sufficient. In other cases, organizations may need to consider a stream-processing framework, or they may have already adopted a streaming data platform and wish to enrich the data as it streams through their organization. There are a variety of Apache open-source projects associated with processing streaming data including Beam, Calcite, Flink, Kafka, Spark, Storm and others. Among these, Kafka and Spark have been widely adopted. Kafka has emerged as a de facto standard for streaming data, with many vendors supporting it in their data platforms or offering it as a managed service. However, while Kafka provides a robust data streaming platform, it is limited in the types of transformations and analyses it supports. Perhaps as a result of these limitations, Apache Flink is on a growth trajectory that matches Kafka’s earlier growth trajectory. Flink provides stateful computations over data streams, including an SQL processing layer. Apache Spark has also emerged as a de facto standard for processing large amounts of data and is the backbone of many data lake implementations. Within Spark are projects to support SQL as well as a project for Structured Streaming which applies SQL to streaming data. For those that require more sophisticated analyses, Spark also includes a machine learning (ML) library. Basically, organizations have three categories to choose from for real-time analytics over streaming data. First, they can rely on their data platform provider. Most are offering some support for stream processing and, depending on the use case and the relationship with the vendor, that may be sufficient. For those organizations that have made big commitments to a data lake architecture, Spark is likely to be a good alternative, again depending on the use case. However, for those organizations that are heavily invested in Kafka or one of its variants, Flink may be the best option, enabling the analyses to be associated with the streams independent of where they land and what data platforms are used. Some operations don’t require real time. Some require near real time and still others will require nearly instantaneous responses. Explore your options so you can be prepared to meet your organization’s needs. Regards, David Menninger", "image": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/Stream_and_Event_Processing_Require_Real-Time_Analytics.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "David Menninger", "url": "https://blog.isg-research.net/author/david-menninger", "sameAs": "http://www.linkedin.com/in/davidmenninger" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "blog.isg-research.net", "logo": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/ISG_Software_Research_Logo_Color@2x.png" } } }</script> <script type="application/ld+json"> { "@context": "http://schema.org", "@type": "BlogPosting", "headline": "The Buyers Guide for Data Orchestration Classifies and Rates Vendors", "mainEntityOfPage": "https://blog.isg-research.net/the-buyers-guide-for-data-orchestration-classifies-and-rates-vendors", "datePublished": "24/10/2023", "dateModified": "24/10/2023", "articleBody": "I am happy to share insights gleaned from our latest Buyers Guide, an assessment of how well vendors’ offerings meet buyers’ requirements. The Ventana Research 2023 Data Orchestration Buyers Guide is the distillation of a year of market and product research by Ventana Research. Drawing on our Benchmark Research, we apply a structured methodology built on evaluation categories that reflect the real-world criteria incorporated in a request for proposal to data vendors supporting the spectrum of data orchestration. Using this methodology, we evaluated vendor submissions in seven categories: five relevant to the product experience ﹘ Adaptability, Capability, Manageability, Reliability and Usability ﹘ and two related to the customer experience ﹘ TCO/ROI and vendor Validation. This research-based index evaluates the full business and information technology value of data orchestration software offerings. I encourage you to learn more about our Buyers Guide and its effectiveness as a vendor selection and RFI/RFP tool. This Buyers Guide research evaluates the following vendors that offer products for Data Orchestration as we define it: Alteryx, AWS, Astronomer, BMC, Databricks, DataKitchen, Google, Hitachi Vantara, IBM, Infoworks.io, Matillion, Microsoft, Prefect, Rivery, Saagie, SAP, Stonebranch, StreamSets and Y42. Three of these 19 suppliers responded positively to our requests for information and provided completed questionnaires and demonstrations to aid our analysis of their data orchestration products. Along with briefings and information where provided, all online material that was generally available was used for the analysis. This report includes products generally available as of August 30, 2023. The following vendors declined to participate or did not respond to our invitation: AWS, Astronomer, Databricks, Google, Hitachi Vantara, IBM, Infoworks.io, Matillion, Microsoft, Prefect, Rivery, Saagie, SAP, Stonebranch, StreamSets, Y42. To organizations considering products from these vendors, we recommend extra scrutiny as part of the software assessment because they did not make their technology available for the Value Index evaluation process. Unlike many IT analyst firms that rank vendors from an IT-only perspective or focus on a specific type of data orchestration, Ventana Research has designed the Buyers Guide to provide a balanced perspective, rooted in an understanding of business drivers and needs. This approach not only reduces cost and time but also minimizes the risk of making a decision that is bad for the business. Using the Buyers Guide will enable your organization to achieve levels of efficiency and effectiveness needed to optimize data orchestration. We urge organizations to do a thorough job of evaluating data orchestration offerings in this Buyers Guide as both the results of our in-depth analysis of these vendors and as an evaluation methodology. The Value Index can be used to evaluate existing suppliers, plus provides evaluation criteria for new projects. Using it can shorten the cycle time for an RFP. The Buyers Guide for Data Orchestration in 2023 finds IBM first on the list, followed by Microsoft and Alteryx. Companies that rated in the top three of any category ﹘ including the product and customer experience dimensions ﹘ earn the designation of Leader. The Leaders in Product Experience are: Alteryx. IBM. Microsoft. The Leaders in Customer Experience are: Microsoft. IBM. AWS. The Leaders across any of the seven categories are: Alteryx, IBM and Microsoft, which has achieved this rating in four of the seven categories. Databricks in three categories. Google in two categories. AWS, BMC, Hitachi Vantara and SAP in one category. The overall performance chart provides a visual representation of how vendors rate across product and customer experience. Vendors with products scoring higher in a weighted rating of the five product experience categories place farther to the right. The combination of ratings for the two customer experience categories determines their placement on the vertical axis. As a result, vendors that place closer to the upper-right are “exemplary” and rated higher than those closer to the lower-left and identified as vendors of “merit.” Vendors that excelled at customer experience over product experience have an “assurance” rating, and those excelling instead in product experience have an “innovative” rating. Note that close vendor scores should not be taken to imply that the packages evaluated are functionally identical or equally well-suited for use by every organization or process. Although there is a high degree of commonality in how organizations handle data orchestration, there are many idiosyncrasies and differences that can make one vendor’s offering a better fit than another. After more than a decade of technological advances, all the products we evaluated are feature-rich, but not all the capabilities they offer are equally valuable to users. Moreover, the existence of too many capabilities may be a negative factor for an organization if it introduces unnecessary complexity. Nonetheless, an organization may decide that a larger number of options is a plus, especially if some of them match its established practices or better support a new initiative that is driving the purchase of new software. Features, functions and vendor assessments are not the only deciding factors. For example, an organization may face budget constraints such that the TCO evaluation can tip the scale to one vendor or another. This is where the Value Index methodology and appropriate weighting can be applied to determine the best fit of vendors and products to an organization’s specific needs. Our firm has made every effort to encompass in this Buyers Guide the overall product and customer experience from our data orchestration blueprint, which we believe reflects what a well-crafted RFP should contain. Even so, there may be additional areas that affect which vendor and products best fit an organization’s particular requirements. Therefore, while this research is complete as it stands, utilizing it in your own organizational context is critical to ensure that products deliver the highest level of support for your projects. You can find more details on our site as well as in the Buyers Guide Report. Regards, Matt Aslett", "image": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/The_Buyers_Guide_for_Data_Orchestration_Classifies_and_Rates_Vendors.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "Matt Aslett", "url": "https://blog.isg-research.net/author/matt-aslett", "sameAs": "https://www.linkedin.com/in/matt-aslett/" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "blog.isg-research.net", "logo": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/ISG_Software_Research_Logo_Color@2x.png" } } }</script> <script type="application/ld+json"> { "@context": "http://schema.org", "@type": "BlogPosting", "headline": "Data Orchestration Buyers Guide: Market Observations", "mainEntityOfPage": "https://blog.isg-research.net/data-orchestration-buyers-guide-market-observations", "datePublished": "23/10/2023", "dateModified": "23/10/2023", "articleBody": "The 2023 Ventana Research Buyers Guide for Data Orchestration research enables me to provide observations about how the market has advanced. Data orchestration is a concept that has been growing in popularity in the past five years amid the rise of DataOps, which describes more agile approaches to data integration and data management. Data orchestration provides the capabilities to automate and accelerate the flow of data from multiple sources to support operational and analytics initiatives and drive business value. At the highest level of abstraction, data orchestration covers three key capabilities: collection (including data ingestion, preparation and cleansing); transformation (additionally including integration and enrichment); and activation (making the results available to compute engines, analytics and data science tools, or operational applications). This may sound very much like the tasks that data management practitioners have been undertaking for decades. As such, it is fair to ask what separates data orchestration from traditional approaches to data management. Key to understanding why data orchestration is different, and necessary, is viewing data management challenges through the lens of modern data-processing requirements. Data-driven organizations stand to gain competitive advantage, responding faster to worker and customer demands for more innovative, data-rich applications and personalized experiences. Being data-driven requires a combination of people, processes, information and technology improvements involving data culture, data literacy, data democracy, and data curiosity. Encouraging employees to discover and experiment with data is a key aspect of being data-driven that requires new, agile approaches to data management. Meanwhile, the increasing reliance on real-time data processing is driving requirements for more agile, continuous data processing. Additionally, the rapid adoption of cloud computing has fragmented where data is accessed or consolidated, with data increasingly spread across multiple data centers and cloud providers. Traditional approaches to data management are rooted in point-to-point batch data processing, whereby data is extracted from its source, transformed for a specific purpose, and loaded into a target environment for analysis. These approaches are unsuitable for the demands of modern analytics environments, which instead require agile data pipelines that can traverse multiple data-processing locations and can evolve in response to changing data sources and business requirements. Given the increasing complexity of evolving data sources and requirements, there is a need to enable the flow of data across the organization through new approaches to the creation, scheduling, automation and monitoring of workflows. This is the realm of data orchestration, although the key capabilities of data orchestration will be familiar to existing data practitioners. Specific tasks related to these capabilities have traditionally been addressed with a variety of tools as well as manual effort, hand-coded scripts and expertise. In comparison, data orchestration tools are designed to automate and coordinate the sequential or parallel execution of a complete set of tasks via data pipelines, typically based on directed acyclic graphs (DAGs) that represent the relationships and dependencies between the tasks. The capabilities delivered by data orchestration fall under three categories: pipeline monitoring, pipeline management, and workflow management. As is often the case with new approaches to data and analytics, the requirements for data orchestration were first experienced by digital-native brands at the forefront of data-driven business strategies. One of the most prominent data orchestration tools, Apache Airflow, began as an internal development project within Airbnb, becoming an Apache Software Foundation project in 2016; workflow automation platform Flyte was originally created and subsequently open-sourced by Lyft; and Metaflow was developed and open-sourced by Netflix. Data orchestration is not just for digital natives, however, and a variety of vendors have sprung up with offerings based around these open-source projects, as well as other development initiatives, to bring the benefits of data orchestration to the masses. In addition to stand-alone data orchestration software products and cloud services, data orchestration capabilities are also being built into larger data-engineering platforms addressing broader data management requirements, including data observability, often in the context of data fabric and data mesh. Whether stand-alone or embedded in larger data-engineering platforms, data orchestration has the potential to drive improved efficiency and agility in data and analytics projects. Data orchestration addresses one of the most significant impediments to generating value from data. More than two-thirds (69%) of participants in Ventana Research’s Analytics and Data Benchmark Research cite preparing data for analysis as the most time-consuming task in analyzing data. Adoption of data orchestration is still in the early stages and is closely linked to larger data transformation efforts that introduce greater agility and flexibility. However, by 2026, more than one-half of organizations will adopt data orchestration technologies to automate and coordinate data workflows and increase efficiency and agility in data and analytics projects. If an organization’s data processes and skills remain rooted in traditional products and manual intervention, then data orchestration is not likely to be a quick fix. However, alongside the cultural and organizational changes involved in people, processes, and information improvements, data orchestration has the potential to play a key role in the technological improvement involved in becoming more data-driven. All organizations are recommended to investigate the potential advantages of data orchestration with a view to improving their use of data and analytics. This research evaluates the following vendors that offer products that address key elements of data orchestration as we define it: Alteryx, AWS, Astronomer, BMC, Databricks, DataKitchen, Google, Hitachi Vantara, IBM, Infoworks.io, Matillion, Microsoft, Prefect, Rivery, Saagie, SAP, Stonebranch, StreamSets and Y42. You can find more details on our site as well as in the Buyers Guide Market Report. Regards, Matt Aslett", "image": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/Data_Orchestration_Buyers_Guide_Market_Observations.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "Matt Aslett", "url": "https://blog.isg-research.net/author/matt-aslett", "sameAs": "https://www.linkedin.com/in/matt-aslett/" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "blog.isg-research.net", "logo": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/ISG_Software_Research_Logo_Color@2x.png" } } }</script> <script type="application/ld+json"> { "@context": "http://schema.org", "@type": "BlogPosting", "headline": "The Buyers Guide for Data Pipelines Classifies and Rates Vendors", "mainEntityOfPage": "https://blog.isg-research.net/the-buyers-guide-for-data-pipelines-classifies-and-rates-vendors", "datePublished": "20/10/2023", "dateModified": "20/10/2023", "articleBody": "I am happy to share insights gleaned from our latest Buyers Guide, an assessment of how well vendors’ offerings meet buyers’ requirements. The Ventana Research 2023 Data Pipelines Buyers Guide is the distillation of a year of market and product research by Ventana Research. Drawing on our Benchmark Research, we apply a structured methodology built on evaluation categories that reflect the real-world criteria incorporated in a request for proposal to Data vendors supporting the spectrum of Data Pipelines. Using this methodology, we evaluated vendor submissions in seven categories: five relevant to the product experience ﹘ Adaptability, Capability, Manageability, Reliability and Usability ﹘ and two related to the customer experience ﹘ TCO/ROI and vendor Validation. This research-based index evaluates the full business and information technology value of Data Pipelines software offerings. I encourage you to learn more about our Buyers Guide and its effectiveness as a vendor selection and RFI/RFP tool. This Buyers Guide research evaluates the following vendors that offer products for Data Pipelines as we define it: Alteryx, AWS, Astronomer, BMC, Databricks, DataKitchen, dbt Labs, Google, Hitachi Vantara, IBM, Infoworks.io, Matillion, Microsoft, Prefect, Rivery, SAP, StreamSets and Y42. Five of these 18 suppliers responded positively to our requests for information and provided completed questionnaires and demonstrations to aid our analysis of their data pipelines products. Along with briefings and information where provided, all online material that was generally available was used for the analysis. This report includes products generally available as of August 30, 2023. The following vendors declined to participate or did not respond to our invitation: AWS, Astronomer, Databricks, dbt Labs, Google, Hitachi Vantara, IBM, Infoworks.io, Matillion, Microsoft, Precisely, Prefect, Rivery, SAP, StreamSets and Y42. To organizations considering products from these vendors, we recommend extra scrutiny as part of the software assessment because they did not make their technology available for the Value Index evaluation process. Unlike many IT analyst firms that rank vendors from an IT-only perspective or focus on a specific type of TOPIC, Ventana Research has designed the Buyers Guide to provide a balanced perspective, rooted in an understanding of business drivers and needs. This approach not only reduces cost and time but also minimizes the risk of making a decision that is bad for the business. Using the Buyers Guide will enable your organization to achieve levels of efficiency and effectiveness needed to optimize Data Pipelines. We urge organizations to do a thorough job of evaluating Data Pipelines offerings in this Buyers Guide as both the results of our in-depth analysis of these vendors and as an evaluation methodology. The Value Index can be used to evaluate existing suppliers, plus provides evaluation criteria for new projects. Using it can shorten the cycle time for an RFP. The Buyers Guide for Data Pipelines in 2023 finds IBM first on the list, followed by Microsoft and Alteryx. Companies that rated in the top three of any category ﹘ including the product and customer experience dimensions ﹘ earn the designation of Leader. The Leaders in Product Experience are: IBM. Microsoft. Alteryx. The Leaders in Customer Experience are: Microsoft. IBM. AWS. The Leaders across any of the seven categories are: IBM and Microsoft, which has achieved this rating in four of the seven categories. Alteryx and Databricks in three categories. Google in two categories. AWS, DataKitchen, dbt Labs, Hitachi Vantara and SAP in one category. The overall performance chart provides a visual representation of how vendors rate across product and customer experience. Vendors with products scoring higher in a weighted rating of the five product experience categories place farther to the right. The combination of ratings for the two customer experience categories determines their placement on the vertical axis. As a result, vendors that place closer to the upper-right are “exemplary” and rated higher than those closer to the lower-left and identified as vendors of “merit.” Vendors that excelled at customer experience over product experience have an “assurance” rating, and those excelling instead in product experience have an “innovative” rating. Note that close vendor scores should not be taken to imply that the packages evaluated are functionally identical or equally well-suited for use by every organization or process. Although there is a high degree of commonality in how organizations handle Data Pipelines, there are many idiosyncrasies and differences that can make one vendor’s offering a better fit than another. After more than a decade of technological advances, all the products we evaluated are feature-rich, but not all the capabilities they offer are equally valuable to users. Moreover, the existence of too many capabilities may be a negative factor for an organization if it introduces unnecessary complexity. Nonetheless, an organization may decide that a larger number of options is a plus, especially if some of them match its established practices or better support a new initiative that is driving the purchase of new software. Features, functions and vendor assessments are not the only deciding factors. For example, an organization may face budget constraints such that the TCO evaluation can tip the scale to one vendor or another. This is where the Value Index methodology and appropriate weighting can be applied to determine the best fit of vendors and products to an organization’s specific needs. Our firm has made every effort to encompass in this Buyers Guide the overall product and customer experience from our Data Pipelines blueprint, which we believe reflects what a well-crafted RFP should contain. Even so, there may be additional areas that affect which vendor and products best fit an organization’s particular requirements. Therefore, while this research is complete as it stands, utilizing it in your own organizational context is critical to ensure that products deliver the highest level of support for your projects. You can find more details on our site as well as in the Buyers Guide Report. Regards, Matt Aslett", "image": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/The_Buyers_Guide_for_Data_Pipelines_Classifies_and_Rates_Vendors.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "Matt Aslett", "url": "https://blog.isg-research.net/author/matt-aslett", "sameAs": "https://www.linkedin.com/in/matt-aslett/" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "blog.isg-research.net", "logo": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/ISG_Software_Research_Logo_Color@2x.png" } } }</script> <script type="application/ld+json"> { "@context": "http://schema.org", "@type": "BlogPosting", "headline": "The Buyers Guide for Business Planning Classifies and Rates Vendors", "mainEntityOfPage": "https://blog.isg-research.net/the-buyers-guide-for-business-planning-classifies-and-rates-vendors", "datePublished": "19/10/2023", "dateModified": "19/10/2023", "articleBody": "I am happy to share insights gleaned from our latest Buyers Guide, an assessment of how well vendors’ offerings meet buyers’ requirements. The Ventana Research 2023 Business Planning Buyers Guide is the distillation of a year of market and product research by Ventana Research. Drawing on our Benchmark Research, we apply a structured methodology built on evaluation categories that reflect the real-world criteria incorporated in a request for proposal to Office of Finance vendors supporting the spectrum of Business Planning. Using this methodology, we evaluated vendor submissions in seven categories: five relevant to the product experience ﹘ Adaptability, Capability, Manageability, Reliability and Usability ﹘ and two related to the customer experience ﹘ TCO/ROI and vendor Validation. This research-based index evaluates the full business and information technology value of Business Planning software offerings. I encourage you to learn more about our Buyers Guide and its effectiveness as a vendor selection and RFI/RFP tool. This Buyers Guide research evaluates the following vendors that offer products for Business Planning as we define it: Anaplan, Board, IBM, Infor, insightsoftware, OneStream Software, Oracle, Planful, Prophix Software, SAP, Vena Solutions Wolters Kluwer and Workday. 12 of these 13 suppliers responded positively to our requests for information and provided completed questionnaires and demonstrations to aid our analysis of their Business Planning products. Along with briefings and information where provided, all online material that was generally available was used for the analysis. This report includes products generally available as of August 31, 2023. The following vendors declined to participate or did not respond to our invitation: insightsoftware. To organizations considering products from these vendors, we recommend extra scrutiny as part of the software assessment because they did not make their technology available for the Value Index evaluation process. Unlike many IT analyst firms that rank vendors from an IT-only perspective or focus on a specific type of Business Planning, Ventana Research has designed the Buyers Guide to provide a balanced perspective, rooted in an understanding of business drivers and needs. This approach not only reduces cost and time but also minimizes the risk of making a decision that is bad for the business. Using the Buyers Guide will enable your organization to achieve levels of efficiency and effectiveness needed to optimize Business Planning. We urge organizations to do a thorough job of evaluating Business Planning offerings in this Buyers Guide as both the results of our in-depth analysis of these vendors and as an evaluation methodology. The Value Index can be used to evaluate existing suppliers, plus provides evaluation criteria for new projects. Using it can shorten the cycle time for an RFP. The Buyers Guide for Business Planning in 2023 finds Oracle first on the list, followed by SAP and Anaplan. Companies that rated in the top three of any category ﹘ including the product and customer experience dimensions ﹘ earn the designation of Leader. The Leaders in Product Experience are: Oracle. SAP. IBM. The Leaders in Customer Experience are: Oracle. SAP. Anaplan. The Leaders across any of the seven categories are: Oracle, which has achieved this rating in five of the seven categories. Anaplan and SAP in four categories. Board, IBM and OneStream in two categories. Planful and Workday in one category. The overall performance chart provides a visual representation of how vendors rate across product and customer experience. Vendors with products scoring higher in a weighted rating of the five product experience categories place farther to the right. The combination of ratings for the two customer experience categories determines their placement on the vertical axis. As a result, vendors that place closer to the upper-right are “exemplary” and rated higher than those closer to the lower-left and identified as vendors of “merit.” Vendors that excelled at customer experience over product experience have an “assurance” rating, and those excelling instead in product experience have an “innovative” rating. Note that close vendor scores should not be taken to imply that the packages evaluated are functionally identical or equally well-suited for use by every organization or process. Although there is a high degree of commonality in how organizations handle Business Planning, there are many idiosyncrasies and differences that can make one vendor’s offering a better fit than another. After more than a decade of technological advances, all the products we evaluated are feature-rich, but not all the capabilities they offer are equally valuable to users. Moreover, the existence of too many capabilities may be a negative factor for an organization if it introduces unnecessary complexity. Nonetheless, an organization may decide that a larger number of options is a plus, especially if some of them match its established practices or better support a new initiative that is driving the purchase of new software. Features, functions and vendor assessments are not the only deciding factors. For example, an organization may face budget constraints such that the TCO evaluation can tip the scale to one vendor or another. This is where the Value Index methodology and appropriate weighting can be applied to determine the best fit of vendors and products to an organization’s specific needs. Our firm has made every effort to encompass in this Buyers Guide the overall product and customer experience from our Business Planning blueprint, which we believe reflects what a well-crafted RFP should contain. Even so, there may be additional areas that affect which vendor and products best fit an organization’s particular requirements. Therefore, while this research is complete as it stands, utilizing it in your own organizational context is critical to ensure that products deliver the highest level of support for your projects. You can find more details on our site as well as in the Buyers Guide Report. Regards, Robert Kugel", "image": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/The_Buyers_Guide_for_Business_Planning_Classifies_and_Rates_Vendors.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "Robert Kugel", "url": "https://blog.isg-research.net/author/robert-kugel", "sameAs": "https://www.linkedin.com/in/robertkugel" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "blog.isg-research.net", "logo": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/ISG_Software_Research_Logo_Color@2x.png" } } }</script> <script type="application/ld+json"> { "@context": "http://schema.org", "@type": "BlogPosting", "headline": "Data Pipelines Buyers Guide: Market Observations", "mainEntityOfPage": "https://blog.isg-research.net/data-pipelines-buyers-guide-market-observations", "datePublished": "19/10/2023", "dateModified": "19/10/2023", "articleBody": "The 2023 Ventana Research Buyers Guide for Data Pipelines research enables me to provide observations about how the market has advanced. The development, testing and deployment of data pipelines is essential to generating intelligence from data. Just as a physical pipeline is used to transport water between turbines, generators and transformers in the generation of hydroelectric power, so data pipelines are used to transport data between the stages involved in data processing and analytics to generate business insight. Healthy data pipelines are necessary to ensure data is integrated and processed in the sequence required to generate business intelligence (BI). The concept of the data pipeline is nothing new, but it is becoming increasingly important as organizations adapt data management processes to be more data driven. Data pipelines have traditionally involved batch extract, transform and load processes, but data-driven processes require more agile, continuous data processing as part of a DataOps approach to data management, with an increased focus on extract, load and transform (ELT) processes, as well as change data capture and automation and orchestration. Data-driven organizations are increasingly thinking of the steps involved in extracting, integrating, aggregating, preparing, transforming and loading data as a continual process that is orchestrated to facilitate data-driven analytics. By 2026, three-quarters of organizations will adopt data engineering processes that span data integration, transformation and preparation, producing repeatable data pipelines that create more agile information architectures. The need for more agile data pipelines is driven by the need for real-time data processing. Almost a quarter (22%) of organizations who participated in Ventana Research’s Analytics and Data Benchmark Research are currently analyzing data in real time, with an additional 10% analyzing data every hour. More frequent data analysis requires data to be integrated, cleansed, enriched, transformed and processed for analysis in a continuous and agile process. Traditional batch extract, transform and load data pipelines are ill-suited to continuous and agile processes. These pipelines were designed to extract data from a source (typically a database supporting an operational application), transform it in a dedicated staging area, and then load it into a target environment (typically a data warehouse or data lake) for analysis. Exact, transfer and load (ETL) pipelines can be automated and orchestrated to reduce manual intervention. However, since they are designed for a specific data transformation task, ETL pipelines are rigid and difficult to adapt. As data and business requirements change, ETL pipelines need to be rewritten accordingly. The need for greater agility and flexibility to meet the demands of real-time data processing is one reason we have seen increased interest in ELT pipelines. These pipelines involve the use of a more lightweight staging tier, which is required simply to extract data from the source and load it into the target data platform. Rather than a separate transformation stage prior to loading as with an ETL pipeline, ELT pipelines make use of pushdown optimization, leveraging the data processing functionality and processing power of the target data platform to transform the data. Pushing data transformation execution to the target data platform results in a more agile data extraction and loading phase, which is more adaptable to changing data sources. This approach is well-aligned with the application of schema-on-read applied in data lake environments, as opposed to the schema-on-write approach in which a schema is applied as it is loaded into a data warehouse. Since the data is not transformed before being loaded into the target data platform, data sources can change and evolve without delaying data loading. This potentially enables data analysts to transform data to meet their requirements rather than have dedicated data integration professionals perform the task. As such, many ELT offerings are positioned for use by data analysts and developers rather than IT professionals. This can also result in reduced delays in deploying business intelligence projects by avoiding the need to wait for data transformation specialists to (re)configure pipelines in response to evolving BI requirements and new data sources. Like ETL pipelines, ELT pipelines may also be batch processes. Both can be accelerated by using change data capture techniques. Change data capture (CDC) is similarly not new but has come into greater focus given the increasing need for real-time data processing. As the name suggests, CDC is the process of capturing data changes. Specifically, in the context of data pipelines, CDC identifies and tracks changes to tables in the source database as they are inserted, updated or deleted. CDC reduces complexity and increases agility by only synchronizing changed data rather than the entire dataset. The data changes can be synchronized incrementally or in a continuous stream. The development, testing and deployment of both ETL and ELT pipelines can be automated and orchestrated to provide further agility by reducing the need for manual intervention. Specifically, the batch extraction of data can be scheduled to occur at regular intervals of a set number of minutes or hours, while the various stages in a data pipeline can be managed as orchestrated workflows using data engineering workflow management platforms. Data observability also has a complementary role to play in monitoring the health of data pipelines and associated workflows as well as the quality of the data itself. Many products for data pipeline development, testing and deployment also offer functionality for monitoring and managing pipelines and are integrated with data orchestration and/or observability functionality. There remains a need for batch ETL pipelines, not least of which to support existing data integration and analytic processes. However, ELT and CDC approaches have a role to play alongside automation and orchestration in increasing data agility, and all organizations are recommended to consider the potential advantages of more agile data pipelines driving BI and transformational change. This research evaluates the following vendors that offer products that address key elements of data pipelines as we define it: Alteryx, AWS, Astronomer, BMC, Databricks, DataKitchen, dbt Labs, Google, Hitachi Vantara, IBM, Infoworks.io, Matillion, Microsoft, Prefect, Rivery, SAP, StreamSets and Y42. You can find more details on our site as well as in the Buyers Guide Market Report. Regards, Matt Aslett", "image": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/Data_Pipelines_Buyers_Guide_Market_Observations.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "Matt Aslett", "url": "https://blog.isg-research.net/author/matt-aslett", "sameAs": "https://www.linkedin.com/in/matt-aslett/" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "blog.isg-research.net", "logo": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/ISG_Software_Research_Logo_Color@2x.png" } } }</script> <script type="application/ld+json"> { "@context": "http://schema.org", "@type": "BlogPosting", "headline": "Business Planning Buyers Guide: Market Observations", "mainEntityOfPage": "https://blog.isg-research.net/business-planning-buyers-guide-market-observations", "datePublished": "18/10/2023", "dateModified": "18/10/2023", "articleBody": "The 2023 Ventana Research Buyers Guide for Business Planning research enables me to provide observations about how the market has advanced. The purpose of business planning is not simply to create a plan — it is to make better decisions. Planning and budgeting software should make the process faster, more agile, less burdensome and more intelligent so that managers can make better decisions more consistently. Since the early 2000s, Ventana Research has advocated using a dedicated application to increase the business value of planning and budgeting. In 2008, we first articulated a new vision of using a planning software platform to improve the business value of corporate budgeting. This approach allows business units to plan the way that best serves their needs while enabling the financial planning and analysis (FP&A) group to integrate their plans to construct a unified operating plan and budget. This idea has since been widely embraced by software vendors and increasingly adopted by organizations. Our Value Index methodology assesses the ability of specific applications to achieve this vision.  Organizations do a lot of planning. Some of this planning is formal, but in practice, much of it is informal and almost always in business silos. People plan sales and how to produce products and deliver services. They plan for the headcount they will need and how to organize distribution and their supply chain. They also produce a budget, which is a financial plan. When business managers are asked to prepare a budget, they first create a business plan, often informally, and then translate that into financial terms.   Although planning and budgeting are similar and connected, they have different aims. Budgeting sets limits while planning seeks opportunities. Planning is about determining the best path to success while budgeting imposes financial controls to prevent an organization from failing, though not failing is not the same as succeeding. Planning is about the things that departments do and the things they need to do them. Budgeting summarizes the monetary consequences of a plan in income statements, pro forma balance sheets and cash flow projections. Successful organizations understand that planning for success while respecting financial objectives and constraints is more productive and strategic than just budgeting. Planning software enables organizations to do both faster and more effectively than using stand-alone spreadsheets, which still remains the most popular software for planning and budgeting.  The enduring popularity of the stand-alone spreadsheet is understandable because it is a familiar tool that is easy to use for creating financial models and collecting data. Spreadsheets are the tool of choice for personal productivity, but their essential design makes them the wrong choice for supporting a repetitive, collaborative, enterprise-wide process. Moreover, a spreadsheet is a two-dimensional grid, adequate for accounting, but inherently flawed in modeling and reporting because business is inherently multidimensional.    Many organizations are in a planning rut, the result of inertia, the power of vested interests and the perceived risk of change. Although the annual budget provokes grumbling, there is little impetus for change, especially from the top. For those in FP&A who manage the budgeting process, it is easy to lose sight of an objective when a job is wrapped up in a complex business process, especially if the individual in charge of that job is spending a considerable amount of time handling its mechanics and dealing with the inefficiencies of spreadsheets. Technology allows organizations to streamline and redefine the process so that those doing the planning and budgeting can concentrate on its essential purpose.  Planning should be a structured dialog between executives and managers about objectives and the resources and tactics that people need to achieve them, not just a financial plan. Dialog is considered “structured” when it is grounded in measurable objectives rather than vague goals. For example, “Our objective is to grow sales by 8%,” not “We plan to increase sales.”   In practice, planning is an inconsistent, fragmented and siloed process. Mostly, this stems from using legacy technologies. Stand-alone spreadsheet-based planning reinforces silos because they compartmentalize plans. Each department plans their part of the business differently because they have different needs. Creating an integrated or connected business plan with spreadsheets is difficult because, while spreadsheets can handle the financial aspects of planning an organization, they are not good at simultaneously handling monetary elements and “things” like headcount, hours, or supplies or materials needed to fulfill sales orders or contracts.   For years, people have been talking about finance transformation; that is, the remaking of the finance department into an organization that functions as a strategic asset to the corporation. Such a department is strategic because it provides greater visibility and insight into how the company and each of its business units is performing and how to optimize that performance going forward. Its focus is on what happens next and not merely on what just happened. It does not only explain past results but uses that context to provide insight into choices among next-step options and their ramifications. A strategic finance organization has the capability to provide data-driven advice to executives. Building technology competence does not just benefit planning and budgeting; it raises the performance of every facet of the department.   Technology is essential to making planning and budgeting more strategic, more productive, and more consequential to an organization’s success. Planning software increases the business value of planning and budgeting because it serves the needs of the senior leadership team, business managers and the office of finance, thereby increasing an organization’s agility.   This research evaluates the following vendors that offer products that address key elements of business planning as we define it: Anaplan, Board, IBM, Infor, insightsoftware, OneStream Software, Oracle, Planful, Prophix Software, SAP, Vena Solutions Wolters Kluwer and Workday. You can find more details on our site as well as in the Buyers Guide Market Report. Regards, Robert Kugel", "image": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/Business_Planning_Buyers_Guide_Market_Observations.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "Robert Kugel", "url": "https://blog.isg-research.net/author/robert-kugel", "sameAs": "https://www.linkedin.com/in/robertkugel" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "blog.isg-research.net", "logo": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/ISG_Software_Research_Logo_Color@2x.png" } } }</script> <script type="application/ld+json"> { "@context": "http://schema.org", "@type": "BlogPosting", "headline": "The Buyers Guide for DataOps Classifies and Rates Vendors", "mainEntityOfPage": "https://blog.isg-research.net/the-buyers-guide-for-dataops-classifies-and-rates-vendors", "datePublished": "18/10/2023", "dateModified": "18/10/2023", "articleBody": "I am happy to share insights gleaned from our latest Buyers Guide, an assessment of how well vendors’ offerings meet buyers’ requirements. The Ventana Research 2023 DataOps Buyers Guide is the distillation of a year of market and product research by Ventana Research. Drawing on our Benchmark Research, we apply a structured methodology built on evaluation categories that reflect the real-world criteria incorporated in a request for proposal to Data vendors supporting the spectrum of DataOps. Using this methodology, we evaluated vendor submissions in seven categories: five relevant to the product experience ﹘ Adaptability, Capability, Manageability, Reliability and Usability ﹘ and two related to the customer experience ﹘ TCO/ROI and vendor Validation. This research-based index evaluates the full business and information technology value of DataOps software offerings. I encourage you to learn more about our Buyers Guide and its effectiveness as a vendor selection and RFI/RFP tool. This Buyers Guide research evaluates the following vendors that offer products for DataOps as we define it: Alteryx, AWS, Astronomer, BMC, Databricks, DataKitchen, Google, Hitachi Vantara, IBM, Infoworks, Matillion, Prefect, Rivery, SAP, Stonebranch, StreamSets and Y42. Three of these 18 suppliers responded positively to our requests for information and provided completed questionnaires and demonstrations to aid our analysis of their DataOps products. Along with briefings and information where provided, all online material that was generally available was used for the analysis. This report includes products generally available as of August 30, 2023. The following vendors declined to participate or did not respond to our invitation: AWS, Astronomer, Databricks, Google, Hitachi Vantara, IBM, Infoworks.io, Matillion, Microsoft, Prefect, Rivery, SAP, Stonebranch, StreamSets and Y42. To organizations considering products from these vendors, we recommend extra scrutiny as part of the software assessment because they did not make their technology available for the Value Index evaluation process. Unlike many IT analyst firms that rank vendors from an IT-only perspective or focus on a specific type of DataOps, Ventana Research has designed the Buyers Guide to provide a balanced perspective, rooted in an understanding of business drivers and needs. This approach not only reduces cost and time but also minimizes the risk of making a decision that is bad for the business. Using the Buyers Guide will enable your organization to achieve levels of efficiency and effectiveness needed to optimize DataOps. We urge organizations to do a thorough job of evaluating DataOps offerings in this Buyers Guide as both the results of our in-depth analysis of these vendors and as an evaluation methodology. The Value Index can be used to evaluate existing suppliers, plus provides evaluation criteria for new projects. Using it can shorten the cycle time for an RFP. The Buyers Guide for DataOps in 2023 finds IBM first on the list, followed by DataKitchen and Microsoft. Companies that rated in the top three of any category ﹘ including the product and customer experience dimensions ﹘ earn the designation of Leader. The Leaders in Product Experience are: IBM. DataKitchen. Microsoft. The Leaders in Customer Experience are: Microsoft. IBM. AWS. The Leaders across any of the seven categories are: IBM, which has achieved this rating in five of the seven categories. Microsoft in four categories. Alteryx and Databricks in three categories. Google in two categories. AWS, DataKitchen, Hitachi Vantara and SAP in one category. The overall performance chart provides a visual representation of how vendors rate across product and customer experience. Vendors with products scoring higher in a weighted rating of the five product experience categories place farther to the right. The combination of ratings for the two customer experience categories determines their placement on the vertical axis. As a result, vendors that place closer to the upper-right are “exemplary” and rated higher than those closer to the lower-left and identified as vendors of “merit.” Vendors that excelled at customer experience over product experience have an “assurance” rating, and those excelling instead in product experience have an “innovative” rating. Note that close vendor scores should not be taken to imply that the packages evaluated are functionally identical or equally well-suited for use by every organization or process. Although there is a high degree of commonality in how organizations handle DataOps, there are many idiosyncrasies and differences that can make one vendor’s offering a better fit than another. After more than a decade of technological advances, all the products we evaluated are feature-rich, but not all the capabilities they offer are equally valuable to users. Moreover, the existence of too many capabilities may be a negative factor for an organization if it introduces unnecessary complexity. Nonetheless, an organization may decide that a larger number of options is a plus, especially if some of them match its established practices or better support a new initiative that is driving the purchase of new software. Features, functions and vendor assessments are not the only deciding factors. For example, an organization may face budget constraints such that the TCO evaluation can tip the scale to one vendor or another. This is where the Value Index methodology and appropriate weighting can be applied to determine the best fit of vendors and products to an organization’s specific needs. Our firm has made every effort to encompass in this Buyers Guide the overall product and customer experience from our DataOps blueprint, which we believe reflects what a well-crafted RFP should contain. Even so, there may be additional areas that affect which vendor and products best fit an organization’s particular requirements. Therefore, while this research is complete as it stands, utilizing it in your own organizational context is critical to ensure that products deliver the highest level of support for your projects. You can find more details on our site as well as in the Buyers Guide Report. Regards, Matt Aslett", "image": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/The_Buyers_Guide_for_DataOps_Classifies_and_Rates_Vendors.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "Matt Aslett", "url": "https://blog.isg-research.net/author/matt-aslett", "sameAs": "https://www.linkedin.com/in/matt-aslett/" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "blog.isg-research.net", "logo": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/ISG_Software_Research_Logo_Color@2x.png" } } }</script> <script type="application/ld+json"> { "@context": "http://schema.org", "@type": "BlogPosting", "headline": "Databricks Utilizes Unity Catalog to Support Generative AI Development", "mainEntityOfPage": "https://blog.isg-research.net/databricks-utilizes-unity-catalog-to-support-generative-ai-development", "datePublished": "17/10/2023", "dateModified": "17/10/2023", "articleBody": "I previously described how Databricks had positioned its Lakehouse Platform as the basis for data engineering, data science and data warehousing. The lakehouse design pattern provides a flexible environment for storing and processing data from multiple enterprise applications and workloads for multiple use cases. I assert that by 2025, 8 in 10 current data lake adopters will invest in data lakehouse architecture to improve the business value generated from the accumulated data. Databricks is perhaps still best known for the Apache Spark open-source distributed data processing framework, which is a core component of its Databricks’ Lakehouse Platform. The company’s structured data management and unified governance functionality are just as important to the Lakehouse Platform, as reinforced by the company’s recent announcements related to the expansion of its workloads to include generative AI. Databricks’ Unity Catalog is a core enabler of the Lakehouse AI announcements and the launch of LakehouseIQ. Databricks was founded in 2013 and initially built a business providing a managed service enabling data engineers, data scientists and developers to create and maintain data engineering and machine-learning workloads using Apache Spark. It is compatible with other open-source projects, including TensorFlow, MLflow and PyTorch. Databricks expanded its focus to data warehousing with dashboarding and visualization functionality provided by the 2020 acquisition of Redash. The functionality is supported by the Delta Lake table storage project, the Delta Engine high-performance query engine and Unity Catalog, which provides unified governance for files, tables, dashboards and machine learning models. Almost one-half (46%) of participants in Ventana Research’s Data Lake Dynamic Insights research use a data catalog to manage data in the data lake. Databricks’ combined functionality is offered as Databricks Lakehouse Platform on Amazon Web Services and Google Cloud, and Azure Databricks on Microsoft Azure. It is used by more than 10,000 customers, providing the company with a revenue run rate of over $1.5 billion and a valuation of $43 billion, according to its recent $500 million Series I funding round. Databricks has expanded its focus to address generative AI through internal research and development and through the acquisition of generative AI platform provider MosaicML. Best known for developing and training its large language models, MosaicML also provides a platform for use by enterprises to build, train and deploy LLMs. The MosaicML platform remains available on a standalone basis while Databricks is working on enabling closer integration with the Databricks Lakehouse Platform. Announcements from the company’s recent Data + AI Summit user conference focused on facilitating the combination of generative AI and Databricks Lakehouse Platform. This includes the Databricks Lakehouse Platform as a foundation for developing generative AI-based applications and the introduction of generative AI-based services to facilitate the generation of insight from data in the Databricks Lakehouse Platform. Lakehouse AI is not a new product offering but a set of new functionality announcements that facilitate the use of Databricks Lakehouse Platform for the development and training of generative AI. The new features introduced at Data + AI Summit include support for fine-tuning LLMs using Databricks AutoML, optimizing Databricks model serving for a curated list of open-source LLMs available within Databricks Marketplace and enhancements to the MLflow machine learning operationalization platform to support LLMs. Enhancements to MLflow include the addition of MLflow AI Gateway for the management of cloud models and application programming interfaces and MLflow prompt tools for comparing the output of multiple models. Databricks also announced the addition of vector search, providing the ability to create and manage vector embeddings from files in Unity Catalog, improving the accuracy of generative AI responses with proprietary data and content. Unity Catalog now performs as the backend of Databricks Feature Store, serving data to models in training and production as well as providing governance and lineage capabilities to support model monitoring with Databricks Lakehouse Monitoring. This expands the company’s data and AI monitoring capabilities with the monitoring and management of all data and AI assets within the Lakehouse. Unity Catalog also provides governance capabilities to support LakehouseIQ, which is described as a knowledge engine. It provides a natural language interface for users to discover, understand and query data in the Lakehouse Platform, including Unity Catalog, in addition to dashboards, notebooks, data pipelines and documents. LakehouseIQ provides enhanced search by adding business context and supports automated query generation in the Assistant capability in Databricks’ SQL editor and notebooks software. It is also used to debug jobs, data pipelines and Spark and SQL queries. In addition to building on Unity Catalog, Databricks also used Data + AI Summit to announce some enhancements to the product. This included the addition of Lakehouse Federation capabilities to better enable Lakehouse Platform to be used as part of a data mesh environment. The Lakehouse Federation capabilities include query federation, enabling users to query data in data platforms outside Databricks Lakehouse Platform, such as MySQL, PostgreSQL, Amazon Redshift, Snowflake, Azure SQL Database, Azure Synapse and Google BigQuery. Additionally, Unity Catalog users can now define data access and governance policies in Unity Catalog and apply those policies to data stored in external data warehouses. Databricks previously added a hive Metastore interface to Unity Catalog, enabling it to connect to any software compatible with Apache Hive, including Amazon EMR, open-source Apache Spark, Amazon Athena, Presto and Trino. Databricks also recently announced version 3.0 of the open-source Delta Lake table storage project – including the Universal Format (or UniForm) – providing compatibility with tools designed for alternative table formats by allowing data stored in Delta tables to be read as if it were Apache Iceberg or Apache Hudi. Multiple competing table formats had the potential to divide the various data lakehouse platform and tool providers. Databricks continues to develop differentiating capabilities. Its investment in developing UniForm is indicative of the company’s focus on higher-level business value differentiation via capabilities such as Unity Catalog rather than lower-level technical differentiators like table formats. Either way, Databricks is now well-established as an analytic data platform provider, and I recommend that organizations include Databricks in evaluations for data platforms to support data engineering, data science, data governance, data warehousing and generative AI. Regards, Matt Aslett", "image": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/Databricks_Utilizes_Unity_Catalog_to_Support_Generative_AI_Development.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "Matt Aslett", "url": "https://blog.isg-research.net/author/matt-aslett", "sameAs": "https://www.linkedin.com/in/matt-aslett/" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "blog.isg-research.net", "logo": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/ISG_Software_Research_Logo_Color@2x.png" } } }</script> <script type="application/ld+json"> { "@context": "http://schema.org", "@type": "BlogPosting", "headline": "The Buyers Guide for Mobile Analytics Classifies and Rates Vendors", "mainEntityOfPage": "https://blog.isg-research.net/the-buyers-guide-for-mobile-analytics-classifies-and-rates-vendors", "datePublished": "17/10/2023", "dateModified": "17/10/2023", "articleBody": "I am happy to share insights gleaned from our latest Buyers Guide, an assessment of how well vendors’ offerings meet buyers’ requirements. The Ventana Research 2023 Mobile Analytics Buyers Guide is the distillation of a year of market and product research by Ventana Research. Drawing on our Benchmark Research, we apply a structured methodology built on evaluation categories that reflect the real-world criteria incorporated in a request for proposal to Analytics vendors supporting the spectrum of Mobile Analytics. Using this methodology, we evaluated vendor submissions in seven categories: five relevant to the product experience ﹘ Adaptability, Capability, Manageability, Reliability and Usability ﹘ and two related to the customer experience ﹘ TCO/ROI and vendor Validation. This research-based index evaluates the full business and information technology value of Mobile Analytics software offerings. I encourage you to learn more about our Buyers Guide and its effectiveness as a vendor selection and RFI/RFP tool. This Buyers Guide research evaluates the following vendors that offer products for Mobile Analytics as we define it: AWS, Cloud Software Group, Domo, GoodData, Google, IBM, Idera, Incorta, Infor, insightsoftware, Microsoft, MicroStrategy, Oracle, Pyramid Analytics, Qlik, SAP, SAS, Sigma Computing, Sisense, Tableau (Salesforce), ThoughtSpot and Zoho. 11 of these 22 suppliers responded positively to our requests for information and provided completed questionnaires and demonstrations to aid our analysis of their Mobile Analytics products. Along with briefings and information where provided, all online material that was generally available was used for the analysis. This report includes products generally available as of August 1, 2023. The following vendors declined to participate or did not respond to our invitation: AWS, Cloud Software Group, Google, Idera, Microsoft, SAS, Sigma Computing and ThoughtSpot. To organizations considering products from these vendors, we recommend extra scrutiny as part of the software assessment because they did not make their technology available for the Value Index evaluation process. Unlike many IT analyst firms that rank vendors from an IT-only perspective or focus on a specific type of Mobile Analytics, Ventana Research has designed the Buyers Guide to provide a balanced perspective, rooted in an understanding of business drivers and needs. This approach not only reduces cost and time but also minimizes the risk of making a decision that is bad for the business. Using the Buyers Guide will enable your organization to achieve levels of efficiency and effectiveness needed to optimize Mobile Analytics. We urge organizations to do a thorough job of evaluating Mobile Analytics offerings in this Buyers Guide as both the results of our in-depth analysis of these vendors and as an evaluation methodology. The Value Index can be used to evaluate existing suppliers, plus provides evaluation criteria for new projects. Using it can shorten the cycle time for an RFP. The Buyers Guide for Mobile Analytics in 2023 finds Oracle first on the list, followed by SAP and Qlik. Companies that rated in the top three of any category ﹘ including the product and customer experience dimensions ﹘ earn the designation of Leader. The Leaders in Product Experience are: Oracle. SAP. IBM. The Leaders in Customer Experience are: Oracle. SAP. Qlik. The Leaders across any of the seven categories are: Oracle, which has achieved this rating in six of the seven categories. SAP in four categories. IBM in three categories. Domo and Qlik in two categories. Microsoft, MicroStrategy, Sisense and Tableau in one category. The overall performance chart provides a visual representation of how vendors rate across product and customer experience. Vendors with products scoring higher in a weighted rating of the five product experience categories place farther to the right. The combination of ratings for the two customer experience categories determines their placement on the vertical axis. As a result, vendors that place closer to the upper-right are “exemplary” and rated higher than those closer to the lower-left and identified as vendors of “merit.” Vendors that excelled at customer experience over product experience have an “assurance” rating, and those excelling instead in product experience have an “innovative” rating. Note that close vendor scores should not be taken to imply that the packages evaluated are functionally identical or equally well-suited for use by every organization or process. Although there is a high degree of commonality in how organizations handle mobile analytics there are many idiosyncrasies and differences that can make one vendor’s offering a better fit than another. After more than a decade of technological advances, all the products we evaluated are feature-rich, but not all the capabilities they offer are equally valuable to users. Moreover, the existence of too many capabilities may be a negative factor for an organization if it introduces unnecessary complexity. Nonetheless, an organization may decide that a larger number of options is a plus, especially if some of them match its established practices or better support a new initiative that is driving the purchase of new software. Features, functions and vendor assessments are not the only deciding factors. For example, an organization may face budget constraints such that the TCO evaluation can tip the scale to one vendor or another. This is where the Value Index methodology and appropriate weighting can be applied to determine the best fit of vendors and products to an organization’s specific needs. Our firm has made every effort to encompass in this Buyers Guide the overall product and customer experience from our Mobile Analytics blueprint, which we believe reflects what a well-crafted RFP should contain. Even so, there may be additional areas that affect which vendor and products best fit an organization’s particular requirements. Therefore, while this research is complete as it stands, utilizing it in your own organizational context is critical to ensure that products deliver the highest level of support for your projects. You can find more details on our site as well as in the Buyers Guide Report. Regards, David Menninger", "image": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/The_Buyers_Guide_for_Mobile_Analytics_Classifies_and_Rates_Vendors.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "David Menninger", "url": "https://blog.isg-research.net/author/david-menninger", "sameAs": "http://www.linkedin.com/in/davidmenninger" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "blog.isg-research.net", "logo": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/ISG_Software_Research_Logo_Color@2x.png" } } }</script> <script type="application/ld+json"> { "@context": "http://schema.org", "@type": "BlogPosting", "headline": "Visier Extends People Analytics Beyond the Data Analyst and Scientist", "mainEntityOfPage": "https://blog.isg-research.net/visier-extends-people-analytics-beyond-the-data-analyst-and-scientist", "datePublished": "17/10/2023", "dateModified": "17/10/2023", "articleBody": "Human resources has long been stereotyped as the department that relies on intuition and experience in decision-making. Enterprises have evolved to understand the importance of their workforce to achieving business outcomes and not just as a line item in the expense column of a balance sheet, earning HR executives a seat in Lin Manuel Miranda’s eponymous “room where it happens.” In doing so, those very executives must be armed with the data that ties worker performance to business outcomes; thus, people analytics has become an industry of its own. Like any data analytics enterprise, the strength of people analytics vendors lies in their inherent ability to process and interpret vast amounts of business and people data to generate insights; in today’s world, data is the linchpin of decision-making. Think about the avalanche of data a typical enterprise generates from its employees: demographic information, performance metrics, engagement surveys, feedback loops, compensation trends and much more. While this data is valuable on its own, it truly shines when combined with business and external data sources, like market compensation trends, industry benchmarks, and evolving skill-demand matrices, which is then interpreted in ways that are meaningful to end users who don’t happen to be data scientists. A people analytics provider can integrate this ocean of data to provide actionable insights, allowing companies to stay ahead in their unique competitive landscapes. Visier is a premier people analytics platform provider that specializes in offering comprehensive people analytics solutions, helping enterprises harness the power of data to make informed decisions about their workforce. And while theoretically any data analytics platform is capable of providing the same level of analytics capability, there are a variety of reasons why enterprises should choose to invest in people-specific analytics capabilities. Visier offers comprehensive domain expertise, with deep knowledge of HR-related metrics tied to business outcomes, and an understanding of the context, challenges, and best practices of HR-related issues. Further, its platform provides pre-configured HR metrics, reports, and dashboards specifically designed for workforce management and insights, unlike general analytics platforms which would require customization. And because it is specifically intended for use in workforce-related analytics, integration options with other HR software like applicant tracking, performance management and core HR systems are readily available and require less work and data mapping than with non-people analytics systems. As an advanced analytics platform, Visier offers all the same capabilities of any non-HR analytics platform that data analysts are accustomed to. But it is also designed to be used by those who may not have advanced data analytics skills; the user interface and experience are tailored for ease of use by non-technical users. That level of usability is important, as I assert that by 2027, two-thirds of enterprises evaluating people analytics offerings will require prescriptive guidance with actionable insights that can be read and heard rather than just visualized in dashboards. Recently, Visier significantly advanced its capabilities in that arena with the release of Vee, a generative AI (GenAI)-based digital assistant that uses a conversational interface for people leaders to get immediate and accurate insights about their workforce simply by asking questions, rather than having to create pivot tables and advanced queries. This type of interface puts the power of advanced people analytics in the hands of those who need that information readily accessible to make informed decisions that will impact their workforce and their business. In addition to Vee, Visier has also entered the field of compensation management with the release of Visier Smart Compensation. Extending the themes of accessibility and usability by business users, this system provides managers, not just HR and compensation teams, the ability to understand and apply the enterprise's compensation philosophy, see how different compensation allocation decisions will impact their team and how those decisions relate to rest of the enterprise, and apply advanced analytics to help remove bias and ensure pay equity. And with the use of prescriptive analytics and guided insights, Visier helps managers hold better compensation-related conversations with their employees, something that has long been expected of them but for which they have never been trained. People analytics are commonly used in compensation management all the time, and with their level of expertise in that arena, Visier is well-positioned to quickly make an impact in the category. Visier's expertise and capabilities in people analytics can make it an indispensable partner for organizations looking to make the power of people analytics and insights more accessible to non-data scientists. This is particularly true now with the addition of Vee and Smart Compensation. Visier’s data-driven insights, derived from a rich and diverse set of data points, provide the kind of foundational knowledge that can drive not just incremental improvements but transformative changes. In a world where data is king, partnering with a people analytics provider like Visier is akin to securing a royal advisor, providing the wisdom and insights needed for ruling wisely and effectively. I strongly recommend that enterprises consider Visier as part of their HCM technology stack. Regards, Mark Smith", "image": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/Visier_Extends_People_Analytics.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "ISG Software Research", "url": "https://blog.isg-research.net/author/isg-software-research", "sameAs": "https://www.linkedin.com/company/ventana-research" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "blog.isg-research.net", "logo": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/ISG_Software_Research_Logo_Color@2x.png" } } }</script> <script type="application/ld+json"> { "@context": "http://schema.org", "@type": "BlogPosting", "headline": "DataOps Buyers Guide: Market Observations", "mainEntityOfPage": "https://blog.isg-research.net/dataops-buyers-guide-market-observations", "datePublished": "16/10/2023", "dateModified": "16/10/2023", "articleBody": "The 2023 Ventana Research Buyers Guide for DataOps research enables me to provide observations about how the market has advanced. Data Operations (DataOps) is a methodology focused on the delivery of agile business intelligence (BI) and data science through the automation and orchestration of data integration and processing pipelines, incorporating improved data reliability and integrity via data monitoring and observability. DataOps has been part of the lexicon of the data market for almost a decade and takes inspiration from DevOps, which describes a set of tools, practices and philosophy used to support the continuous delivery of software applications in the face of constant changes. Interest in DataOps is growing. Ventana Research asserts that by 2025, one-half of organizations will have adopted a DataOps approach to their data engineering processes, enabling them to be more flexible and agile. A variety of products, practices and processes enable DataOps, including products that support agile and continuous delivery of data analytics and continuous measurable improvement. An emphasis on agility, collaboration and automation separates DataOps from traditional approaches to data management, which were typically based on tools and practices that were batch-based, manual and rigid. This distinction between DataOps and traditional data management tools is clearer in theory than it is in practice. There is a level of opacity as traditional data management vendors have, in recent years, incorporated capabilities that make their products more automated, collaborative and agile. There is no industry-wide consensus on the level of agility, collaboration and automation that must be provided for products be to be considered part of the DataOps category. While traditional data management vendors have also adopted the term DataOps, many have adopted a broader definition that describes DataOps as the combination of people, process and technology needed to automate the delivery of data to users in an organization and enable collaboration to facilitate data-driven decisions. This definition is broad enough that it could be interpreted to encompass all products and services that address data management and data governance, including many traditional batch-based, manual products that do not support agile and continuous delivery and continuous measurable improvement. A narrower definition of DataOps focuses on the practical application of agile development, DevOps and lean manufacturing to the tasks and skills employed by data engineering professionals in support of data analytics development and operations. This definition emphasizes specific capabilities such as continuous delivery of analytic insight, process simplification, code generation, automation to avoid repeated errors and reduce repetitive tasks, the incorporation of stakeholder feedback and advancement, and measurable improvement in the efficient generation of insight from data. As such, the narrow definition of DataOps provides a set of criteria for agile and collaborative practices that products and services can be measured against. Ventana Research’s perspective, based on our interaction with the vendor and user communities, aligns with the narrow definition. While traditional data management and data governance are complementary, our DataOps coverage focuses specifically on the delivery of agile BI and data science through the automation and orchestration of data integration and processing pipelines, incorporating improved data reliability and integrity via data monitoring and observability. To be more specific, we believe that DataOps products and services provide functionality that addresses a particular set of capabilities: agile and collaborative data operations; the development, testing and deployment of data and analytics pipelines; data orchestration and data observability. These are the key criteria that we used to assess DataOps products and services as part of this Buyer’s Guide. This research is comprised of parallel evaluations of products addressing each of the three core areas of functionality: data pipelines, data orchestration and data observability. Vendors with products that address at least two of these three core areas were deemed to provide a superset of functionality to address DataOps overall. Additionally, we evaluated all products in all categories in relation to their support for agile and collaborative practices. The development, testing and deployment of data pipelines is essential to generating intelligence from data, ensuring that data is integrated and processed in the correct sequence to generate the required intelligence. Just as a physical pipeline is used to transport water between stages in the generation of hydroelectric power, data pipelines are used to transport data between the stages involved in data processing and analytics to generate business insight. The transportation of data has traditionally been a batch process that has moved data from one environment to another. However, data-driven organizations are increasingly thinking of the steps involved in extracting, integrating, aggregating, preparing, transforming and loading data as a continual process that is orchestrated to facilitate data-driven analytics. We assert that by 2026, three-quarters of organizations will adopt data engineering processes that span data integration, transformation and preparation, producing repeatable data pipelines that create more agile information architectures. Data orchestration provides the capabilities to automate and accelerate the flow of data from multiple sources to support analytics initiatives and drive business value. At the highest level of abstraction, data orchestration covers three key capabilities: collection (including data ingestion, preparation and cleansing); transformation (additionally including integration and enrichment); and activation (making the results available to compute engines, analytics and data science tools or operational applications). By 2026, more than one-half of organizations will adopt data orchestration technologies to automate and coordinate data workflows and increase efficiency and agility in data and analytics projects. Meanwhile, the need to monitor the pipelines and processes in data processing and analytics environments has driven the emergence of a new category of software: data observability. Monitoring the quality and reliability of data used for analytics and governance projects is not new, but data pipeline observability utilizes machine learning (ML) to automate the monitoring of data to ensure that it is complete, valid and consistent, as well as relevant and free from duplication. Data pipeline observability also addresses monitoring not just the data stored in an individual data warehouse or data lake, but also the associated upstream and downstream data pipelines. Through 2025, data observability will continue to be a priority for the evolution of data operations products as vendors deliver more automated approaches to data engineering and improving trust in enterprise data. In combination, data orchestration and data observability products address two of the most significant impediments to generating value from data. Participants in Ventana Research’s Analytics and Data Benchmark Research cite preparing data for analysis (69%) and reviewing data for quality and consistency issues (64%) as the two most time-consuming tasks in analyzing data. As always, however, products are only one aspect of delivering on the promise of DataOps. New approaches to people, process and information are also required to deliver agile and collaborative development, testing and deployment of data and analytics workloads, as well as data operations. To improve the value that they are generating from their analytics and data initiatives, organizations need to investigate the potential benefits of data pipeline development, data orchestration and data observability products alongside processes and methodologies that support rapid innovation and experimentation, automation, collaboration, measurement and monitoring, and high data quality. This research evaluates the following vendors that offer products that address at least two of the three core areas of DataOps functionality (data pipeline development, testing and deployment; data pipeline orchestration; and data pipeline observability): Alteryx, AWS, Astronomer, BMC, Databricks, DataKitchen, Google, Hitachi Vantara, IBM, Infoworks, Matillion, Prefect, Rivery, SAP, Stonebranch, StreamSets and Y42. You can find more details on our site as well as in the Buyers Guide Market Report. Regards, Matt Aslett", "image": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/DataOps_Buyers_Guide_Market_Observations.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "Matt Aslett", "url": "https://blog.isg-research.net/author/matt-aslett", "sameAs": "https://www.linkedin.com/in/matt-aslett/" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "blog.isg-research.net", "logo": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/ISG_Software_Research_Logo_Color@2x.png" } } }</script> <script type="application/ld+json"> { "@context": "http://schema.org", "@type": "BlogPosting", "headline": "Mobile Analytics Buyers Guide: Market Observations", "mainEntityOfPage": "https://blog.isg-research.net/mobile-analytics-buyers-guide-market-observations", "datePublished": "16/10/2023", "dateModified": "16/10/2023", "articleBody": "The 2023 Ventana Research Buyers Guide for Mobile Analytics research enables me to provide observations about how the market has advanced. The processes and technology of the mobile analytics software industry play an instrumental role in enabling an organization’s business units and IT to optimally utilize data in both tactical and strategic ways. To accomplish this, organizations must provide technology that can access the data, generate and apply insights from analytics, communicate the results and support collaboration as needed. For analytics to be effective, they need to be available to line-of-business personnel as needed in their normal course of conducting business, which today means providing rich mobile access to analytics to support a mobile workforce seeking to conduct business in any location at any time. Workers today expect these mobile capabilities, so organizations must make choices to provide analytics platforms that can deliver. Many of these workers switch between desktop/laptop devices when in the office to mobile devices when outside the office or even when attending an internal meeting, so integration between these two modes is important as well. This Buyers Guide assessment focuses on the challenge of delivering analytics and business intelligence (BI) on a variety of mobile devices including smartphones and tablets. When it comes to smaller devices such as smartphones, it may not be realistic to expect full-blown analyses due to the small form factor, but screen resolution on larger devices such as tablets can support nearly any type of functionality that mobile personnel may need. These needs are substantial: two-thirds of organizations (67%) consider it important to access analytics from mobile devices and more than two-fifths (45%) of organizations report that it is important or very important to be able to address data preparation tasks on mobile devices. Analytics vendors are divided in their approach to providing mobile capabilities. Many vendors have invested in developing applications that are native to the two platforms that dominate the mobile landscape: Apple’s iOS and Google’s Android operating system. Other vendors have opted to invest in an HTML5 approach, often in combination with a helper application that provides some of the native capabilities such as participation in the store or marketplace where applications are distributed. Our research suggests that both approaches can be effective. Vendors also differ in their approach to designing the mobile applications. Some take a design once, deploy anywhere approach where every visualization is available on all devices. This approach requires some interpretation on what the best mobile layout might be for each analysis. For instance, a dashboard may consist of four individual visualizations each occupying one quadrant of the display. On a small mobile device such as a smartphone, these are often presented with each visualization occupying the entire screen and vertical scrolling enabled to move through all four. Other vendors offer a mobile design view that offers the opportunity not only to preview how the devices will display information but to design visualizations specifically for mobile devices. The former approach makes it easier to build and manage the applications. The latter allows for more control over the use of limited screen real estate for mobile devices. The small form factor of these mobile devices, especially smartphones, has helped push the boundaries on ease of use. Users can’t be expected to make multiple selections and accomplish complex interactions using a small display, so vendors have simplified how tasks are performed. In addition, mobile devices offer capabilities such as voice interaction, location information and cameras that can enhance the user experience. Voice interaction can be used for natural language query and natural language responses, and we are starting to see multilingual natural language capabilities. As support for wearables becomes more prevalent, voice interaction could make those devices much more interactive. In some cases, mobile device information can be used to initiate queries or charts based on a customer or vendor’s location information. Cameras can identify products on retail store shelves and their location to help drive sales analyses and projections. We also see a growing interest in — and a growing set of capabilities for — collaboration in conjunction with analytics. Two-thirds of organizations report they are using or plan to use collaboration with analytics. Collaboration provides a way for organizations to derive more value from their analytics by sharing results and interacting with others who can put the information to use in the operations of the business. Collaboration also helps make it easier for individuals to find the information or analyses they need more quickly. Mobile technology is critical for successful collaboration because it reaches so far into the community of potential collaborators. They can be informed via their mobile devices that a topic of interest requires their input or that new information is available. This research evaluates the following vendors that offer products that address key elements of mobile data as we define it: AWS, Cloud Software Group, Domo, GoodData, Google, IBM, Idera, Incorta, Infor, insightsoftware, Microsoft, MicroStrategy, Oracle, Pyramid Analytics, Qlik, SAP, SAS, Sigma Computing, Sisense, Tableau (Salesforce), ThoughtSpot and Zoho. You can find more details on our site as well as in the Buyers Guide Market Report. Regards, David Menninger", "image": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/Mobile_Analytics_Buyers_Guide_Market_Observations.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "David Menninger", "url": "https://blog.isg-research.net/author/david-menninger", "sameAs": "http://www.linkedin.com/in/davidmenninger" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "blog.isg-research.net", "logo": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/ISG_Software_Research_Logo_Color@2x.png" } } }</script> <script type="application/ld+json"> { "@context": "http://schema.org", "@type": "BlogPosting", "headline": "The Buyers Guide for Collaborative Analytics Classifies and Rates Vendors", "mainEntityOfPage": "https://blog.isg-research.net/the-buyers-guide-for-collaborative-analytics-classifies-and-rates-vendors", "datePublished": "13/10/2023", "dateModified": "13/10/2023", "articleBody": "I am happy to share insights gleaned from our latest Buyers Guide, an assessment of how well vendors’ offerings meet buyers’ requirements. The Ventana Research 2023 Collaborative Analytics Buyers Guide is the distillation of a year of market and product research by Ventana Research. Drawing on our Benchmark Research, we apply a structured methodology built on evaluation categories that reflect the real-world criteria incorporated in a request for proposal to Analytics vendors supporting the spectrum of Collaborative Analytics. Using this methodology, we evaluated vendor submissions in seven categories: five relevant to the product experience ﹘ Adaptability, Capability, Manageability, Reliability and Usability ﹘ and two related to the customer experience ﹘ TCO/ROI and vendor Validation. This research-based index evaluates the full business and information technology value of collaborative analytics software offerings. I encourage you to learn more about our Buyers Guide and its effectiveness as a vendor selection and RFI/RFP tool. This Buyers Guide research evaluates the following vendors that offer products for Collaborative Analytics as we define it: AWS, Cloud Software Systems, Domo, GoodData, Google, IBM, Idera, Incorta, Infor, insightsoftware, Microsoft, MicroStrategy, Oracle, Pyramid Analytics, Qlik, SAP, SAS, Sigma Computing, Sisense, Tableau, ThoughtSpot and Zoho. 11 of these 22 suppliers responded positively to our requests for information and provided completed questionnaires and demonstrations to aid our analysis of their Collaborative Analytics products. Along with briefings and information where provided, all online material that was generally available was used for the analysis. This report includes products generally available as of August 1, 2023. The following vendors declined to participate or did not respond to our invitation: AWS, Cloud Software Group, Google, Idera, Microsoft, SAS, Sigma Computing and ThoughtSpot. To organizations considering products from these vendors, we recommend extra scrutiny as part of the software assessment because they did not make their technology available for the Value Index evaluation process. Unlike many IT analyst firms that rank vendors from an IT-only perspective or focus on a specific type of Collaborative Analytics, Ventana Research has designed the Buyers Guide to provide a balanced perspective, rooted in an understanding of business drivers and needs. This approach not only reduces cost and time but also minimizes the risk of making a decision that is bad for the business. Using the Buyers Guide will enable your organization to achieve levels of efficiency and effectiveness needed to optimize Collaborative Analytics. We urge organizations to do a thorough job of evaluating Collaborative Analytics offerings in this Buyers Guide as both the results of our in-depth analysis of these vendors and as an evaluation methodology. The Value Index can be used to evaluate existing suppliers, plus provides evaluation criteria for new projects. Using it can shorten the cycle time for an RFP. The Buyers Guide for Collaborative Analytics in 2023 finds SAP first on the list, followed by IBM and Domo. Companies that rated in the top three of any category ﹘ including the product and customer experience dimensions ﹘ earn the designation of Leader. The Leaders in Product Experience are: SAP. IBM. Domo. The Leaders in Customer Experience are: Oracle. SAP. Qlik. The Leaders across any of the seven categories are: Oracle, which has achieved this rating in six of the seven categories. SAP in five categories. IBM in four categories. Domo and Qlik in two categories. Sisense and Tableau in one category. The overall performance chart provides a visual representation of how vendors rate across product and customer experience. Vendors with products scoring higher in a weighted rating of the five product experience categories place farther to the right. The combination of ratings for the two customer experience categories determines their placement on the vertical axis. As a result, vendors that place closer to the upper-right are “exemplary” and rated higher than those closer to the lower-left and identified as vendors of “merit.” Vendors that excelled at customer experience over product experience have an “assurance” rating, and those excelling instead in product experience have an “innovative” rating. Note that close vendor scores should not be taken to imply that the packages evaluated are functionally identical or equally well-suited for use by every organization or process. Although there is a high degree of commonality in how organizations handle Collaborative Analytics, there are many idiosyncrasies and differences that can make one vendor’s offering a better fit than another. After more than a decade of technological advances, all the products we evaluated are feature-rich, but not all the capabilities they offer are equally valuable to users. Moreover, the existence of too many capabilities may be a negative factor for an organization if it introduces unnecessary complexity. Nonetheless, an organization may decide that a larger number of options is a plus, especially if some of them match its established practices or better support a new initiative that is driving the purchase of new software. Features, functions and vendor assessments are not the only deciding factors. For example, an organization may face budget constraints such that the TCO evaluation can tip the scale to one vendor or another. This is where the Value Index methodology and appropriate weighting can be applied to determine the best fit of vendors and products to an organization’s specific needs. Our firm has made every effort to encompass in this Buyers Guide the overall product and customer experience from our Collaborative Analytics blueprint, which we believe reflects what a well-crafted RFP should contain. Even so, there may be additional areas that affect which vendor and products best fit an organization’s particular requirements. Therefore, while this research is complete as it stands, utilizing it in your own organizational context is critical to ensure that products deliver the highest level of support for your projects. You can find more details on our site as well as in the Buyers Guide Report. Regards, David Menninger", "image": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/The_Buyers_Guide_for_Collaborative_Analytics_Classifies_and_Rates_Vendors.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "David Menninger", "url": "https://blog.isg-research.net/author/david-menninger", "sameAs": "http://www.linkedin.com/in/davidmenninger" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "blog.isg-research.net", "logo": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/ISG_Software_Research_Logo_Color@2x.png" } } }</script> <script type="application/ld+json"> { "@context": "http://schema.org", "@type": "BlogPosting", "headline": "Collaborative Analytics Buyers Guide: Market Observations", "mainEntityOfPage": "https://blog.isg-research.net/collaborative-analytics-buyers-guide-market-observations", "datePublished": "12/10/2023", "dateModified": "12/10/2023", "articleBody": "The 2023 Ventana Research Buyers Guide for Collaborative Analytics research enables me to provide observations about how the market has advanced. Analytics almost universally involves collaboration among a team of individuals. Interpreting the results of the analyses, choosing a course of action and tracking the implementation of those actions spans multiple roles and departments within an organization. As a result, most of today’s analytics and business intelligence (BI) products support some form of collaboration. Capabilities that support the sharing of — and communication about — output from analytical processes help organizations maximize the value of their analytics investments. Facilitating communication and collaboration among those involved in the decision-making process leads to more informed and better decisions. Our research shows that nearly four in 10 organizations are using collaboration to support analytics processes, and more than one-half said they expect to use these capabilities in the future. Analytics and BI vendors have recognized the value of collaboration and have increasingly been incorporating these capabilities into their products. More than a decade ago, social media tools like Facebook, Twitter and LinkedIn brought on a wave of collaborative analytics and BI capabilities. We saw chat streams associated with specific analyses that users could like or endorse. The number of contributions a user made to the community was part of his or her profile so others could accordingly weigh the importance of the input. However, after an initial surge of interest, these efforts failed to gain traction and waned. Collaboration requires a large community of active individuals, and there simply were not enough people regularly engaged in using the analytic products. Those early efforts also required users to participate in the dialog from within the analytics and BI products. Rather than working solely with those products, line-of-business personnel spend their days using a variety of business applications. Two major changes now provide the glue to pull a community of collaborators together: mobile devices and enterprise collaboration tools. The significant expansion of mobile analytics and BI has made it easier for users to get involved. Perhaps more importantly, in the same way the social media users get notifications of activity via mobile devices, collaborative analytics and BI vendors use mobile notifications to engage participants in the analytics process. Organizations have also adopted more widespread use of enterprise collaboration technologies in place of or in addition to email and is more widely available. The analytics process typically involves multiple people with differing areas of expertise and responsibilities. Collaborative tools can enable this diverse group of participants to coordinate their activities and share knowledge. To be most effective, collaborative capabilities should cover the entire data and analytics process; only this way can participants understand the provenance of data as it is analyzed. With this approach, it is easy to identify subject matter experts to engage in the dialog. The team can discuss and document decision-making for compliance purposes, and the actions resulting from those decisions can be assigned and tracked to completion. We expect that by 2025, 8 in 10 BI software platforms will include collaborative capabilities designed to support organizations’ decision-making, task management and compliance requirements associated with analytics. These types of capabilities should be a standard part of analytic processes in much the same way that visualization is now a standard part of data and analytic processes. As organizations embrace more sophisticated analytics such as artificial intelligence (AI) and machine learning (ML), and as analytics become more easily accessible via technologies such as natural language processing (NLP), collaboration capabilities will become even more important. However, strong collaboration capabilities alone are insufficient. These capabilities also must include strong analytics. Our Value Index assessment methodology takes all these factors into account. This research evaluates the following vendors that offer products that address key elements of collaborative analytics as we define it: AWS, Cloud Software Systems, Domo, GoodData, Google, IBM, Idera, Incorta, Infor, insightsoftware, Microsoft, MicroStrategy, Oracle, Pyramid Analytics, Qlik, SAP, SAS, Sigma Computing, Sisense, Tableau, ThoughtSpot and Zoho. You can find more details on our site as well as in the Buyers Guide Market Report. Regards, David Menninger", "image": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/Collaborative_Analytics_Buyers_Guide_Market_Observations.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "David Menninger", "url": "https://blog.isg-research.net/author/david-menninger", "sameAs": "http://www.linkedin.com/in/davidmenninger" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "blog.isg-research.net", "logo": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/ISG_Software_Research_Logo_Color@2x.png" } } }</script> <script type="application/ld+json"> { "@context": "http://schema.org", "@type": "BlogPosting", "headline": "Cultivating Contact Center, Customer Experience Thinking for Success", "mainEntityOfPage": "https://blog.isg-research.net/cultivating-contact-center-customer-experience-thinking-for-success", "datePublished": "12/10/2023", "dateModified": "12/10/2023", "articleBody": "The environment around customer experience is being disrupted by a series of technical and organizational shifts ranging from artificial intelligence to work-from-home to deep customer analytics. When times change, it’s helpful for people to have a framework to organize their thinking and actions. I believe there are two dominant frameworks used to organize business approaches to customers. One is rooted in the efficiency of the contact center (let’s call it “CC thinking”), and the more marketing-centric framework is based on maximizing customer value (for argument’s sake, “CX thinking”). Managing customer experience is not the same thing as running a contact center. Being good at one doesn’t mean success at the other. They’re connected, but look at similar problems through different frames of reference. The confusion creates a disconnect, pitting workers who should be striving toward the same goals to work at cross-purposes, compete for resources and speak different languages. Consider the idea of a contact center. It is an organization’s figurative front door. It is primarily reactive to customer behavior and one of the only places in an organization where workers are dedicated to responding to customers. Now, broaden the scope of concern beyond the interaction: CX controls important things that happen before and after the interaction. It looks to quantify and maximize the relationship, extracting maximum value from customers. Unlike contact center management, it is often the responsibility of people in many different roles with different experiences and backgrounds. Where the contact center works toward efficiency as the goal, customer experience revolves around revenue. People in and around centers focus on interactions and cases. Speed is their goal. They are alert to customer requests and driven to close the matter as inexpensively as possible. This is the default mode of agents, supervisors and many managers. CX professionals, by contrast, are driven by analytics and data in pursuit of outcomes like value, growth, longevity and revenue. They focus on longer-term journeys and shaping relationships by orchestrating interactions and behavior. Each mode of thinking has pros and cons. Contact center thinking has created a miraculously fine-tuned engine of interaction productivity. It establishes an environment in which everyone knows what they need to do, using technology that is well-understood and mature. On the downside, it takes so much effort to manage that it is harder for decision-makers to focus on the bigger picture. CC thinking emphasizes problems rather than opportunities and often views customers through that lens. It has a hard time anticipating and dealing with the unexpected: complex, extra-long interactions or customer volatility. It fits customers and their problems into rigid templates to handle them at scale, and as a result, isn’t as good at managing outlier events. CX thinking solves different problems. First, it takes what the contact center does and applies it to the broader problem of influencing customers to do what you want them to do. It uses vaster data resources to build pictures of customers as individuals and groups likely to behave in certain ways. CX thinking finds and acts on moments of influence that emerge. It does this by encouraging multiple departments and processes to complement each other. For example, it uses the contact center as an instrument for achieving business goals rather than as a way of triaging customer issues. The primary downside of CX thinking is that it’s rare that someone is directly in charge of it. And when someone is, it’s not clear whether they have the authority to make decisions and changes to support their goals or course correct. With broad CX goals, it is often less clear what steps need to be taken (and by whom) to execute a plan. Does the service department answer to the needs of sales and marketing? Does a marketer understand the nuances of different types of service (field, self-service, agented, etc.)? And how can the CX quest for leads and sales affects costs and behavior downstream? It also brings a wider set of tools that aren’t familiar across business units, particularly those that are more deeply analytic and predictive. And it doesn’t always operate with a familiarity of the specific resource requirements of the contact center (i.e., agents). Organizations should care about reconciling these two frameworks because, if not, experiences will degrade, and opportunities will be lost. For example, in a declining (or static) economy, the contact center is one of the first places organizations look to cut costs via headcount. Senior management might know perfectly well that staffing reductions cause longer hold times, agent burnout and attrition. Their attitude may be to say, “Okay, we can live with these CC-focused consequences because they aren’t experienced directly in terms of customer effects.” But those effects are still there, downstream, in the form of lower revenue, customer churn or dissatisfaction. Senior managers need to ask the key question: Do existing cost models assess the downstream customer impacts of cost-cutting on things like loyalty, long-term customer value and so forth? People with a CX focus are more likely to use customer analytics, often with a predictive component. They’re more likely to consider customers as part of larger groups with common qualities. A person with a CC focus will be more likely to look at the same technologies through the lens of making agents more productive and efficient. They’re more likely to see self-service as a mode of deflection than as a way of capturing relevant info about the customer for relationship-building efforts. Not to oversimplify, but from the perspective of the contact center, the goal is to maximize performance metrics such as call volume, response time or first-call resolution. Agents prioritize meeting these key performance indicators to ensure they hit performance targets and earn incentives. However, from the perspective of the organization as a whole, the goal may be to prioritize customer satisfaction and experience. The organization may want to measure metrics such as customer retention rates or customer lifetime value, which reflect overall value and loyalty of customers. If the organization prioritizes customer satisfaction and experience, it may require agents to take more time to fully understand and address customer needs. This may result in longer handling times but higher customer satisfaction rates and potentially increase long-term customer loyalty and revenue. With two distinct ways of looking at customers, individuals need to know when to lean into one or the other. When there are sudden shifts in external conditions, CC thinking excels because you can rely on its ability to operate consistently at scale. However, when launching a new product or service, the organization may need to focus more on CX thinking to ensure that customers understand the value proposition and are excited about the offering. This may involve investing more time and resources to train agents on the new product or service and developing scripts and materials that focus on the customer's needs and preferences. To prosper in the coming age of complexity, consider that this is not a binary choice. You can’t and shouldn’t be either a CX thinker or a CC thinker. You have to be both. You have to act from multiple points of view when making plans about technology deployments, including platforms, AI, workflow automation, analytics and knowledge resources. From inside the contact center, you have to put yourself into the mindset of the CX actors in the rest of the organization. Both modes encourage different types of outcomes and can lead to success in different ways. Within a few years, we predict that organizations will come face-to-face with the disconnect created by this duality. Many will see how important it is to focus on customer value and loyalty to measure success in customer relationships. That’s the growth of CX thinking. At the same time, only a portion of firms will recognize how important good experiences are to building a brand and attracting and keeping customers. That’s the persistence of CC thinking. These predictions suggest a need to collaborate more, to use more common technology platforms and share more sources of data in the quest for a whole-organization approach to customers. Regards, Keith Dawson", "image": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/Cultivating_Contact_Center_Customer_Experience_Thinking_for_Success.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "Keith Dawson", "url": "https://blog.isg-research.net/author/keith-dawson", "sameAs": "www.linkedin.com/in/keith-dawson-b17b71" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "blog.isg-research.net", "logo": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/ISG_Software_Research_Logo_Color@2x.png" } } }</script> <script type="application/ld+json"> { "@context": "http://schema.org", "@type": "BlogPosting", "headline": "Align Early-Career HCM Technology with Digital-First Generation", "mainEntityOfPage": "https://blog.isg-research.net/align-early-career-hcm-technology-with-digital-first-generation", "datePublished": "11/10/2023", "dateModified": "11/10/2023", "articleBody": "When my father entered the workforce in the 1950s, his path was relatively clear. He graduated high school, went to work in his chosen trade, did a stint in the Navy Reserves, and continued in the same or adjacent career until he retired. The same was true for my uncle, who chose to go to college but joined a firm working in his field, worked hard to be promoted, and changed companies one time before retiring as an executive. But the nature of work and job-hunting has undergone a radical transformation over the decades, driven largely by technological advancements and the globalization of the job market. As a result, early career workers — those just entering the workforce or within their first few years — find themselves navigating a vastly different landscape than their predecessors. To effectively tap into their potential and ensure they’re positioned for success, there’s a real need for specialized technology for unique sourcing, engagement, and career pathing to ensure a return on monetary and time investment for both the organization and job seeker. The younger generation just entering the workforce are the first to be digital natives. They’ve grown up with technology and are accustomed to utilizing digital tools in nearly every aspect of their lives. Using generalized or outdated methods to attract, hire or develop these workers is like trying to fit a square peg in a round hole. Organizations must engage with this population using their language, which resonates with their experiences and capitalizes on their natural affinity for the digital world. Moreover, the digital fluency of early career workers means they aren’t just passive job seekers — they're active participants in their own career development, and for tech professionals, that provides insights and connections that were previously unavailable. By leveraging technology tailored to social platforms and the unique ways in which young professionals use them, employers can more effectively reach this group, while early career workers can more easily identify and pursue fitting opportunities. The traditional model of talent sourcing — posting a job and waiting for applicants to come — is becoming obsolete. Instead, with the availability of vast amounts of data and advanced analytical tools, proactive sourcing is the way forward. Organizations can now predict potential fit and success by analyzing a candidate’s digital footprint, online engagement, and more, and communicate with personalized messaging via the mediums best suited for each individual. This proactive approach is not only efficient but also necessary when targeting early career workers who might not even know they're a perfect fit for a role or company. Specialized technology also addresses the unique developmental needs of early career workers. Traditional training modules or one-size-fits-all onboarding processes often fail to engage or educate younger employees effectively. Instead, they crave interactive, dynamic and digitally driven developmental tools. Platforms that incorporate gamification, real-time feedback or even virtual reality can significantly enhance the learning experience for these individuals. Driven by the need to evolve to meet the needs and preferences of the new workforce, we assert that by 2026, one-half of all organizations exploring a new LMS/LXP will expect it to leverage mixed-reality technologies to “job explore” or simulate the experience of working in a new role, leading to better retention, internal mobility, and productivity. Early career workers often enter the job market with a diverse range of skills that don’t fit neatly into traditional job descriptions. From coding bootcamps to online course certifications, the paths to skill acquisition have diversified, and sourcing methods need to adapt. It's not just about where one has earned their degree anymore; it’s about the portfolio they’ve built, the projects they've tackled, or the impact of their online presence. Modern technology, designed to capture and interpret these non-traditional indicators of skill and potential, is crucial for both employers looking for the right talent and early career professionals eager to showcase their unique strengths. Culturally, there’s also been a shift in the priorities of the modern worker. For many early career professionals, values alignment, work-life balance, and opportunities for personal and professional growth are as important — if not more so — than salary or job title. Sophisticated sourcing and engagement tools can match individuals with companies that align with their values and aspirations, creating a more fulfilling work experience and reducing turnover. The early career worker of today isn’t just looking for a job; they're seeking an experience, a growth opportunity, and a chance to make a meaningful impact. As these young professionals become an increasingly significant portion of the workforce, the necessity for specialized technology and modern sourcing methods becomes undeniably clear. To harness the potential of these dynamic, tech-savvy individuals, employers and recruiters must evolve, adapting tools and strategies to reflect the changing realities of the job market. The future of work depends on it. Regards, Quincy Valencia", "image": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/Align_Early-Career_HCM_Technology.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "ISG Software Research", "url": "https://blog.isg-research.net/author/isg-software-research", "sameAs": "https://www.linkedin.com/company/ventana-research" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "blog.isg-research.net", "logo": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/ISG_Software_Research_Logo_Color@2x.png" } } }</script> <script type="application/ld+json"> { "@context": "http://schema.org", "@type": "BlogPosting", "headline": "The Buyers Guide for Embedded Analytics Classifies and Rates Vendors", "mainEntityOfPage": "https://blog.isg-research.net/the-buyers-guide-for-embedded-analytics-classifies-and-rates-vendors", "datePublished": "11/10/2023", "dateModified": "11/10/2023", "articleBody": "I am happy to share insights gleaned from our latest Buyers Guide, an assessment of how well vendors’ offerings meet buyers’ requirements. The Ventana Research 2023 Embedded Analytics Buyers Guide is the distillation of a year of market and product research by Ventana Research. Drawing on our Benchmark Research, we apply a structured methodology built on evaluation categories that reflect the real-world criteria incorporated in a request for proposal to Analytics vendors supporting the spectrum of Embedded Analytics. Using this methodology, we evaluated vendor submissions in seven categories: five relevant to the product experience ﹘ Adaptability, Capability, Manageability, Reliability and Usability ﹘ and two related to the customer experience ﹘ TCO/ROI and vendor Validation. This research-based index evaluates the full business and information technology value of Embedded Analytics software offerings. I encourage you to learn more about our Buyers Guide and its effectiveness as a vendor selection and RFI/RFP tool. This Buyers Guide research evaluates the following vendors that offer products for Embedded Analytics as we define it: AWS, Cloud Software Group, Domo, GoodData, Google, IBM, Idera, Incorta, Infor, insightsoftware, Microsoft, MicroStrategy, Oracle, Pyramid Analytics, Qlik, SAP, SAS, Sigma Computing, Sisense, Tableau Software, ThoughtSpot and Zoho. 11 of these 22 suppliers responded positively to our requests for information and provided completed questionnaires and demonstrations to aid our analysis of their embedded analytics products. Along with briefings and information where provided, all online material that was generally available was used for the analysis. This report includes products generally available as of August 1, 2023. The following vendors declined to participate or did not respond to our invitation: AWS, Cloud Software Group, Google, Idera, Microsoft, SAS, Sigma Computing and ThoughtSpot. To organizations considering products from these vendors, we recommend extra scrutiny as part of the software assessment because they did not make their technology available for the Value Index evaluation process. Unlike many IT analyst firms that rank vendors from an IT-only perspective or focus on a specific type of Embedded Analytics, Ventana Research has designed the Buyers Guide to provide a balanced perspective, rooted in an understanding of business drivers and needs. This approach not only reduces cost and time but also minimizes the risk of making a decision that is bad for the business. Using the Buyers Guide will enable your organization to achieve levels of efficiency and effectiveness needed to optimize Embedded Analytics. We urge organizations to do a thorough job of evaluating Embedded Analytics offerings in this Buyers Guide as both the results of our in-depth analysis of these vendors and as an evaluation methodology. The Value Index can be used to evaluate existing suppliers, plus provides evaluation criteria for new projects. Using it can shorten the cycle time for an RFP. The Buyers Guide for Embedded Analytics in 2023 finds IBM first on the list, followed by SAP and Qlik. Companies that rated in the top three of any category ﹘ including the product and customer experience dimensions ﹘ earn the designation of Leader. The Leaders in Product Experience are: IBM. SAP. Qlik. The Leaders in Customer Experience are: Oracle. SAP. Qlik. The Leaders across any of the seven categories are: IBM, Oracle and SAP, which has achieved this rating in five of the seven categories. Qlik, in three categories. Domo, in two categories. Sisense, in one category. The overall performance chart provides a visual representation of how vendors rate across product and customer experience. Vendors with products scoring higher in a weighted rating of the five product experience categories place farther to the right. The combination of ratings for the two customer experience categories determines their placement on the vertical axis. As a result, vendors that place closer to the upper-right are “exemplary” and rated higher than those closer to the lower-left and identified as vendors of “merit.” Vendors that excelled at customer experience over product experience have an “assurance” rating, and those excelling instead in product experience have an “innovative” rating. Note that close vendor scores should not be taken to imply that the packages evaluated are functionally identical or equally well-suited for use by every organization or process. Although there is a high degree of commonality in how organizations handle Embedded Analytics, there are many idiosyncrasies and differences that can make one vendor’s offering a better fit than another. After more than a decade of technological advances, all the products we evaluated are feature-rich, but not all the capabilities they offer are equally valuable to users. Moreover, the existence of too many capabilities may be a negative factor for an organization if it introduces unnecessary complexity. Nonetheless, an organization may decide that a larger number of options is a plus, especially if some of them match its established practices or better support a new initiative that is driving the purchase of new software. Features, functions and vendor assessments are not the only deciding factors. For example, an organization may face budget constraints such that the TCO evaluation can tip the scale to one vendor or another. This is where the Value Index methodology and appropriate weighting can be applied to determine the best fit of vendors and products to an organization’s specific needs. Our firm has made every effort to encompass in this Buyers Guide the overall product and customer experience from our Embedded Analytics blueprint, which we believe reflects what a well-crafted RFP should contain. Even so, there may be additional areas that affect which vendor and products best fit an organization’s particular requirements. Therefore, while this research is complete as it stands, utilizing it in your own organizational context is critical to ensure that products deliver the highest level of support for your projects. You can find more details on our site as well as in the Buyers Guide Report. Regards, David Menninger", "image": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/The_Buyers_Guide_for_Embedded_Analytics_Classifies_and_Rates_Vendors.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "David Menninger", "url": "https://blog.isg-research.net/author/david-menninger", "sameAs": "http://www.linkedin.com/in/davidmenninger" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "blog.isg-research.net", "logo": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/ISG_Software_Research_Logo_Color@2x.png" } } }</script> <script type="application/ld+json"> { "@context": "http://schema.org", "@type": "BlogPosting", "headline": "Embedded Analytics Buyers Guide: Market Observations", "mainEntityOfPage": "https://blog.isg-research.net/embedded-analytics-buyers-guide-market-observations", "datePublished": "10/10/2023", "dateModified": "10/10/2023", "articleBody": "The 2023 Ventana Research Buyers Guide for Embedded Analytics research enables me to provide observations about how the market has advanced. The processes and technology of the analytics and data software industry play an instrumental role in enabling an organization’s business units and IT to optimally utilize data in both tactical and strategic ways. To accomplish this, organizations must provide their workers technology that can access the data, generate and apply insights from analytics, communicate the results and support collaboration as needed. When analytics are embedded in business processes and applications, analyses are easier to perform and more accessible to line-of-business personnel. The analyses are easier to perform in part because the application collects and assembles data. Our research shows that data preparation can be the most time-consuming step in the analytical process; embedded analytics can dramatically reduce or eliminate this step. Analysis is also easier to consume because there is no need to switch context between the business application and an analysis tool. Perhaps more importantly, analytics embedded within applications can more easily lead to action. For instance, if an analysis suggests a change in pricing, the appropriate logic can be included in the application to make those changes. If an analysis suggests a marketing campaign for a specific customer segment, that segmentation can be performed and the campaign launched. For these reasons, we expect that by 2026, more than two-thirds of line-of-business personnel will have immediate access to cross-functional analytics embedded in their activities and processes, helping to make operational decision-making more efficient and effective. This Buyers Guide focuses on the challenge of delivering analytics and business intelligence in the context of business processes and applications. These needs are substantial — more than half of organizations (53%) report their users of analytics tools cannot perform their own analyses without IT involvement. Our research on cloud-based analytics, predictive analytics, data preparation and big data analytics all show strong interest in embedded delivery of those capabilities. Analytics vendors understand the need to deliver embedded analytics; most have been developing and enhancing APIs and other mechanisms to integrate analytics more tightly into business processes and applications. However, it can be challenging for analytics and business intelligence vendors to package and deliver their capabilities in a way that makes them easy to embed into other applications and processes. Vendors are working to overcome these challenges because organizations recognize the importance of embedded analytics. Nearly three-quarters of participants in our Analytics and Data Benchmark Research said they considered embedded analytics important. Vendors must provide tools for defining data models and accessing data from applications and other data sources. These tools will likely be used by a different audience than typical analytics tools. An application development team, for instance, would create the framework for line-of-business personnel to perform analyses. In addition, the tools must provide mechanisms to integrate both inputs to and outputs from the analytical processes with other applications. Embedded analytics also requires programmatic access to the management and administration functions to minimize installation and maintenance of the system for line-of-business personnel. Organizations are aware of the value of embedded analytics. The next step is to embrace embedded analytics and determine what capabilities their vendor currently offers. They should also be comparing their current offering with what other vendors have to offer. In evaluating embedded analytics, one must also consider the underlying analytics capabilities. Great APIs and SDKs can only do so much if the foundation of underlying analytics is weak. Consequently, this Buyers Guide combines an assessment of embedded analytics capabilities with core analytics capabilities to determine the vendor’s overall rankings. Organizations can then use this report not only to help guide purchasing decisions, but to guide conversations with vendors about their roadmap for embedded analytics. The market continues to evolve, but organizations can realize value today that will improve their analytics processes. This research evaluates the following vendors that offer products that address key elements of embedded analytics as we define it: AWS, Cloud Software Group, Domo, GoodData, Google, IBM, Idera, Incorta, Infor, insightsoftware, Microsoft, MicroStrategy, Oracle, Pyramid Analytics, Qlik, SAP, SAS, Sigma Computing, Sisense, Tableau Software, ThoughtSpot and Zoho. You can find more details on our site as well as in the Buyers Guide Market Report. Regards, David Menninger", "image": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/Embedded_Analytics_Buyers_Guide_Market_Observations.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "David Menninger", "url": "https://blog.isg-research.net/author/david-menninger", "sameAs": "http://www.linkedin.com/in/davidmenninger" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "blog.isg-research.net", "logo": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/ISG_Software_Research_Logo_Color@2x.png" } } }</script> <script type="application/ld+json"> { "@context": "http://schema.org", "@type": "BlogPosting", "headline": "The Buyers Guide for Augmented Analytics Classifies and Rates Vendors", "mainEntityOfPage": "https://blog.isg-research.net/the-buyers-guide-for-augmented-analytics-classifies-and-rates-vendors", "datePublished": "06/10/2023", "dateModified": "06/10/2023", "articleBody": "I am happy to share insights gleaned from our latest Buyers Guide, an assessment of how well vendors’ offerings meet buyers’ requirements. The Ventana Research 2023 Augmented Analytics Buyers Guide is the distillation of a year of market and product research by Ventana Research. Drawing on our Benchmark Research, we apply a structured methodology built on evaluation categories that reflect the real-world criteria incorporated in a request for proposal to Analytics and Data vendors supporting the spectrum of Augmented Analytics. Using this methodology, we evaluated vendor submissions in seven categories: five relevant to the product experience ﹘ Adaptability, Capability, Manageability, Reliability and Usability ﹘ and two related to the customer experience ﹘ TCO/ROI and vendor Validation. This research-based index evaluates the full business and information technology value of Augmented Analytics software offerings. I encourage you to learn more about our Buyers Guide and its effectiveness as a vendor selection and RFI/RFP tool. This Buyers Guide research evaluates the following vendors that offer products for Augmented Analytics as we define it: AWS, Cloud Software Group, Domo, GoodData, Google, IBM, Idera, Incorta, Infor, insightsoftware, Microsoft, MicroStrategy, Oracle, Pyramid Analytics, Qlik, SAP, SAS, Sigma Computing, Sisense, Tableau Software, ThoughtSpot and Zoho. 11 of these 22 suppliers responded positively to our requests for information and provided completed questionnaires and demonstrations to aid our analysis of their augmented analytics products. Along with briefings and information where provided, all online material that was generally available was used for the analysis. This report includes products generally available as of August 1, 2023. The following vendors declined to participate or did not respond to our invitation: AWS, Cloud Software Group, Google, Idera, Microsoft, SAS, Sigma Computing and ThoughtSpot. To organizations considering products from these vendors, we recommend extra scrutiny as part of the software assessment because they did not make their technology available for the Value Index evaluation process. Unlike many IT analyst firms that rank vendors from an IT-only perspective or focus on a specific type of Augmented Analytics, Ventana Research has designed the Buyers Guide to provide a balanced perspective, rooted in an understanding of business drivers and needs. This approach not only reduces cost and time but also minimizes the risk of making a decision that is bad for the business. Using the Buyers Guide will enable your organization to achieve levels of efficiency and effectiveness needed to optimize Augmented Analytics. We urge organizations to do a thorough job of evaluating Augmented Analytics offerings in this Buyers Guide as both the results of our in-depth analysis of these vendors and as an evaluation methodology. The Value Index can be used to evaluate existing suppliers, plus provides evaluation criteria for new projects. Using it can shorten the cycle time for an RFP. The Buyers Guide for Augmented Analytics in 2023 finds Oracle first on the list, followed by SAP and IBM. Companies that rated in the top three of any category ﹘ including the product and customer experience dimensions ﹘ earn the designation of Leader. The Leaders in Product Experience are: Oracle. SAP. IBM. The Leaders in Customer Experience are: Oracle. SAP. Qlik. The Leaders across any of the seven categories are: Oracle, which has achieved this rating in six of the seven categories. SAP, in five categories. IBM, in four categories. Domo and Qlik, in two categories. Sisense and Tableau, in one category. The overall performance chart provides a visual representation of how vendors rate across product and customer experience. Vendors with products scoring higher in a weighted rating of the five product experience categories place farther to the right. The combination of ratings for the two customer experience categories determines their placement on the vertical axis. As a result, vendors that place closer to the upper-right are “exemplary” and rated higher than those closer to the lower-left and identified as vendors of “merit.” Vendors that excelled at customer experience over product experience have an “assurance” rating, and those excelling instead in product experience have an “innovative” rating. Note that close vendor scores should not be taken to imply that the packages evaluated are functionally identical or equally well-suited for use by every organization or process. Although there is a high degree of commonality in how organizations handle Augmented Analytics, there are many idiosyncrasies and differences that can make one vendor’s offering a better fit than another. After more than a decade of technological advances, all the products we evaluated are feature-rich, but not all the capabilities they offer are equally valuable to users. Moreover, the existence of too many capabilities may be a negative factor for an organization if it introduces unnecessary complexity. Nonetheless, an organization may decide that a larger number of options is a plus, especially if some of them match its established practices or better support a new initiative that is driving the purchase of new software. Features, functions and vendor assessments are not the only deciding factors. For example, an organization may face budget constraints such that the TCO evaluation can tip the scale to one vendor or another. This is where the Value Index methodology and appropriate weighting can be applied to determine the best fit of vendors and products to an organization’s specific needs. Our firm has made every effort to encompass in this Buyers Guide the overall product and customer experience from our Augmented Analytics blueprint, which we believe reflects what a well-crafted RFP should contain. Even so, there may be additional areas that affect which vendor and products best fit an organization’s particular requirements. Therefore, while this research is complete as it stands, utilizing it in your own organizational context is critical to ensure that products deliver the highest level of support for your projects. Regards, David Menninger", "image": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/The_Buyers_Guide_for_Augmented_Analytics_Classifies_and_Rates_Vendors.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "David Menninger", "url": "https://blog.isg-research.net/author/david-menninger", "sameAs": "http://www.linkedin.com/in/davidmenninger" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "blog.isg-research.net", "logo": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/ISG_Software_Research_Logo_Color@2x.png" } } }</script> <script type="application/ld+json"> { "@context": "http://schema.org", "@type": "BlogPosting", "headline": "Augmented Analytics Buyers Guide 2023: Market Observations", "mainEntityOfPage": "https://blog.isg-research.net/augmented-analytics-buyers-guide-2023-market-observations", "datePublished": "05/10/2023", "dateModified": "05/10/2023", "articleBody": "The 2023 Ventana Research Buyers Guide for Augmented Analytics research enables me to provide observations about how the market has advanced. For decades, organizations have been improving and expanding the way they use analytics and data software, commonly referred to as business intelligence (BI), to improve their operations. Vendors have made dramatic improvements to BI products with highly interactive visualizations and the ability to process and display very large volumes of data quickly. However, organizations quest for more. Artificial intelligence and machine learning (AI/ML) has gained in popularity with 87% of participants in our Analytics and Data Benchmark Research indicating they have adopted or plan to adopt AI/ML. Adopting AI/ML has proven more complicated than many had expected. Ideally, BI software products could simply be extended to include a full set of AI/ML capabilities, but that has not yet happened. AI/ML requires skills that are beyond the reach of many analysts, and organizations have had difficulty finding skilled resources. As a result, we expect through 2025, AI and ML approaches will remain largely independent of BI approaches, requiring three-quarters of organizations to maintain multiple, separate skill sets. Faced with this separation, BI software vendors have invested in ways to make AI/ML more accessible by augmenting the capabilities in their products. These combined capabilities have been referred to as augmented analytics or augmented intelligence. This report uses the term augmented analytics. AI/ML can augment analytics in a variety of ways. Natural language processing (NLP) utilizes AI/ML to parse and understand language to support conversational analytics with natural language queries and narrative responses. Automated machine learning (AutoML) automates the process of creating ML models, making more sophisticated analytics such as customer segmentation using clustering techniques accessible to more individuals. And, AI/ML can be applied to many tasks in analytics and data processes to make those tasks easier to perform. Perhaps the most common area to apply AI/ML assistance is in data preparation. Data preparation continues to be the area where organizations spend the most time in their analytics processes. AI/ML can be used to suggest which tables of data to combine and how to combine those tables. It can be used to automatically construct a logical data model from a physical data model. AI/ML can augment data quality processes, identifying outliers and anomalies in the data, even recommending potential corrections for those data points. While efforts to apply AI/ML have been underway for some time, the sudden explosion of generative AI capabilities has fueled more interest in how to augment BI. Generative AI is being used to generate SQL to access data sources, and to enhance NLP and conversational analytics. In some cases, generative AI is being used to produce documentation of data pipelines used in analytics processes, enhancing the understanding and lineage of the data. In some ways, it is the wild, wild west, with vendors racing to outdo each other in the ways in which they can apply generative AI. The technology holds much promise, and we expect it will have a significant impact on the analytics market, but it is early days still. Augmented analytics will continue to evolve. Generative AI is likely to make conversational analytics more common and more capable than it is today. It may enable better support for multilingual capabilities currently lacking in most analytics products and will likely lead to increased automation in data preparation processes and in creating initial analyses, making analysts much more productive. More products will offer AutoML capabilities. Among the vendors we evaluated, AutoML is most often used to generate forecasts and to perform customer segmentation analyses. Over time, AutoML capabilities will expand to support more types of analyses and will produce models with improved accuracy. The exact intersection between AutoML in augmented analytics products and the models produced from more sophisticated AI/ML products remains to be seen. Today some of the augmented analytics products can work with these models, but it still a loosely coupled process. Organizations should be aware of the changes going on in the market, and should understand what capabilities their vendor currently offers. They should also be comparing their current offering with what other vendors have to offer. In evaluating augmented analytics, one must consider the underlying analytics capabilities. Great augmentation can only do so much if the foundation of underlying analytics is weak. Consequently, this Buyers Guide combines an assessment of augmented analytics capabilities with core analytics capabilities to determine the vendor’s overall rankings. Organizations can then use this report not only to help guide purchasing decisions, but to guide conversations with vendors about their roadmap for augmented analytics. The market is still evolving rapidly, but organizations can realize value today that will improve their analytics processes. This research evaluates the following analytics and data vendors that offer products that include key elements of augmented analytics as we define it: AWS, Cloud Software Group, Domo, GoodData, Google, IBM, Idera, Incorta, Infor, insightsoftware, Microsoft, MicroStrategy, Oracle, Pyramid Analytics, Qlik, SAP, SAS, Sigma Computing, Sisense, Tableau Software, ThoughtSpot and Zoho. You can find more details on our site as well as in the Buyers Guide Market Report. Regards, David Menninger", "image": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/Augmented_Analytics_Buyers_Guide_2023_Market_Observations.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "David Menninger", "url": "https://blog.isg-research.net/author/david-menninger", "sameAs": "http://www.linkedin.com/in/davidmenninger" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "blog.isg-research.net", "logo": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/ISG_Software_Research_Logo_Color@2x.png" } } }</script> <script type="application/ld+json"> { "@context": "http://schema.org", "@type": "BlogPosting", "headline": "Infoblox DDI Boosts Network Security Resilience", "mainEntityOfPage": "https://blog.isg-research.net/infoblox-ddi-boosts-network-security-resilience", "datePublished": "05/10/2023", "dateModified": "05/10/2023", "articleBody": "In today’s rapidly evolving digital security landscape, organizations face the challenge of maintaining robust network security resilience. Chief information officers battling increasingly sophisticated cyber threats and the growing complexity of enterprise networks must enhance network security posture with advanced technologies to stay ahead of potential threats. One such technology is DDI – the integration of DNS, DHCP and IPAM. DDI integrates three critical security processes: domain name system, dynamic host configuration protocol and internet protocol address management. DNS translates human-readable domain names into IP addresses, allowing devices to communicate with each other over a network. DHCP automatically assigns IP addresses to devices on a network, simplifying network management. IP address management keeps track of IP address usage and allocation, ensuring efficient use of network resources. DDI improves security by automating management of the interactions between DNS, DHCP and IPAM. Providing real-time visibility and control over who and what connects to the network is foundational to network security resilience. Without an IP address, there is no network access. Infoblox DDI management products and services unite networking and security by integrating these components so organizations can connect all devices, applications and digital resources across the business. With a market approach focused on network security resilience, Infoblox offers a range of capabilities designed to help organizations protect against cyber threats. Infoblox DNS enables organizations to deliver DNS services across physical, virtual and cloud environments at scale. From a digital security perspective, this helps protect the network against inherent vulnerabilities in DNS, including distributed denial-of-service attacks, malware and botnets. Monitoring DNS usage assists with flagging suspect devices and applications to predict threats. Infoblox IPAM and DHCP discover and capture all network assets – including those beyond the walls of the facility – in one database to establish a single source of truth. By automating DHCP and IPAM, organizations can manage hybrid and multi-cloud infrastructure and mobile devices. A history of DNS entries allows Infoblox to track changes in campaigns over time. Essentially, a bad actor from the past cannot reuse a domain without the breadcrumb trail left behind, which accelerates the correlation between the domain and the threat actor. Infoblox Threat Intelligence interrogates threat actor networks and blocks suspicious networks by domain. BloxOne DDI is the vendor’s cloud-managed product that centrally controls and automates an organization’s DDI management for hybrid and multi-cloud networks. BloxOne DDI is built on the Infoblox BloxOne Platform and offered as a subscription service. Network landscapes are rapidly evolving, driven by trends in hybrid and multi-cloud transformation, the internet of things and distributed workforces. Security professionals understand how malware exploits DNS and how DNS detection and response can often thwart these attacks. Collaboration between IT and security teams is essential for network security resilience. Through 2025, ineffective relationships between the IT and security teams will contribute to 3 in 5 organizations experiencing access and authentication vulnerabilities. From DNS products to centralized IPAM management tools, Infoblox assists organizations with increased network resilience. Looking ahead, the field of DDI is poised for further advancements. Emerging technologies and trends will shape the future of network resilience through digital security. Organizations examining ways to include cloud architecture into technology governance, for example, have a path forward with DDI services. By staying up to date with these developments and maximizing DDI approaches, organizations can continue to enhance network security and stay ahead of potential threats. DDI is an essential technology for organizations looking to increase network resilience. It protects against DNS-based threats such as DDoS attacks, prevents rogue DHCP servers from disrupting network resources and avoids IP address conflicts and unauthorized access to network resources. Automating routine tasks and centralizing control over DNS, DHCP and IPAM enables organizations to improve security while reducing the risk of human error. In the simplest of terms, organizations must find ways to protect business outcomes, such as uniting network and security resources to effectively manage DNS, DHCP and IPAM. Software providers such as Infoblox and a focus on automation and centralized management help CIOs maximize DDI to stay ahead of emerging and persistent threats. I recommend organizations looking to obtain value from using DNS services in digital security architecture include Infoblox in evaluations. Regards, Jeff Orr", "image": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/Infoblox_DDI_Boosts_Network_Security_Resilience.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "Jeff Orr", "url": "https://blog.isg-research.net/author/jeff-orr", "sameAs": "https://www.linkedin.com/in/jefforr" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "blog.isg-research.net", "logo": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/ISG_Software_Research_Logo_Color@2x.png" } } }</script> <script type="application/ld+json"> { "@context": "http://schema.org", "@type": "BlogPosting", "headline": "The Buyers Guide for Analytics and Data Classifies and Rates Vendors", "mainEntityOfPage": "https://blog.isg-research.net/the-buyers-guide-for-analytics-and-data-classifies-and-rates-vendors", "datePublished": "04/10/2023", "dateModified": "04/10/2023", "articleBody": "I am happy to share insights gleaned from our latest Buyers Guide, an assessment of how well vendors’ offerings meet buyers’ requirements. The Ventana Research 2023 Analytics and Data Buyers Guide is the distillation of a year of market and product research by Ventana Research. Drawing on our Benchmark Research, we apply a structured methodology built on evaluation categories that reflect the real-world criteria incorporated in a request for proposal to Analytics vendors supporting the spectrum of Analytics and Data. Using this methodology, we evaluated vendor submissions in seven categories: five relevant to the product experience ﹘ Adaptability, Capability, Manageability, Reliability and Usability ﹘ and two related to the customer experience ﹘ TCO/ROI and vendor Validation. This research-based index evaluates the full business and information technology value of analytics and data software offerings. I encourage you to learn more about our Buyers Guide and its effectiveness as a vendor selection and RFI/RFP tool. This Buyers Guide research evaluates the following vendors that offer products for Analytics and Data as we define it: AWS, Cloud Software Group, Domo, GoodData, Google, IBM, Idera, Incorta, Infor, insightsoftware, Microsoft, MicroStrategy, Oracle, Pyramid Analytics, Qlik, SAP, SAS, Sigma Computing, Sisense, Tableau (Salesforce), ThoughtSpot and Zoho. 11 of these 22 suppliers responded positively to our requests for information and provided completed questionnaires and demonstrations to aid our analysis of their analytics and data products. Along with briefings and information where provided, all online material that was generally available was used for the analysis. This report includes products generally available as of August 1, 2023. The following vendors declined to participate or did not respond to our invitation: AWS, Cloud Software Group, Google, Idera, Microsoft, SAS, Sigma Computing and ThoughtSpot. To organizations considering products from these vendors, we recommend extra scrutiny as part of the software assessment because they did not make their technology available for the Value Index evaluation process. Unlike many IT analyst firms that rank vendors from an IT-only perspective or focus on a specific type of Analytics and Data, Ventana Research has designed the Buyers Guide to provide a balanced perspective, rooted in an understanding of business drivers and needs. This approach not only reduces cost and time but also minimizes the risk of making a decision that is bad for the business. Using the Buyers Guide will enable your organization to achieve levels of efficiency and effectiveness needed to optimize Analytics and Data. We urge organizations to do a thorough job of evaluating Analytics and Data offerings in this Buyers Guide as both the results of our in-depth analysis of these vendors and as an evaluation methodology. The Value Index can be used to evaluate existing suppliers, plus provides evaluation criteria for new projects. Using it can shorten the cycle time for an RFP. The Buyers Guide for Analytics and Data in 2023 finds SAP first on the list, followed by IBM and Oracle. Companies that rated in the top three of any category ﹘ including the product and customer experience dimensions ﹘ earn the designation of Leader. The Leaders in Product Experience are: IBM. SAP. Oracle. The Leaders in Customer Experience are: Oracle. SAP. Qlik. The Leaders across any of the seven categories are: Oracle and SAP, which has achieved this rating in five of the seven categories. IBM, in four categories. Qlik and Domo, in two categories. Sisense and Tableau, in one category. The overall performance chart provides a visual representation of how vendors rate across product and customer experience. Vendors with products scoring higher in a weighted rating of the five product experience categories place farther to the right. The combination of ratings for the two customer experience categories determines their placement on the vertical axis. As a result, vendors that place closer to the upper-right are “exemplary” and rated higher than those closer to the lower-left and identified as vendors of “merit.” Vendors that excelled at customer experience over product experience have an “assurance” rating, and those excelling instead in product experience have an “innovative” rating. Note that close vendor scores should not be taken to imply that the packages evaluated are functionally identical or equally well-suited for use by every organization or process. Although there is a high degree of commonality in how organizations handle Analytics and Data, there are many idiosyncrasies and differences that can make one vendor’s offering a better fit than another. After more than a decade of technological advances, all the products we evaluated are feature-rich, but not all the capabilities they offer are equally valuable to users. Moreover, the existence of too many capabilities may be a negative factor for an organization if it introduces unnecessary complexity. Nonetheless, an organization may decide that a larger number of options is a plus, especially if some of them match its established practices or better support a new initiative that is driving the purchase of new software. Features, functions and vendor assessments are not the only deciding factors. For example, an organization may face budget constraints such that the TCO evaluation can tip the scale to one vendor or another. This is where the Value Index methodology and appropriate weighting can be applied to determine the best fit of vendors and products to an organization’s specific needs. Our firm has made every effort to encompass in this Buyers Guide the overall product and customer experience from our Analytics and Data blueprint, which we believe reflects what a well-crafted RFP should contain. Even so, there may be additional areas that affect which vendor and products best fit an organization’s particular requirements. Therefore, while this research is complete as it stands, utilizing it in your own organizational context is critical to ensure that products deliver the highest level of support for your projects. Regards, David Menninger", "image": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/The_Buyers_Guide_for_Analytics_and_Data_Classifies_and_Rates_Vendors.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "David Menninger", "url": "https://blog.isg-research.net/author/david-menninger", "sameAs": "http://www.linkedin.com/in/davidmenninger" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "blog.isg-research.net", "logo": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/ISG_Software_Research_Logo_Color@2x.png" } } }</script> <script type="application/ld+json"> { "@context": "http://schema.org", "@type": "BlogPosting", "headline": "Analytics and Data Buyers Guide 2023: Market Observations", "mainEntityOfPage": "https://blog.isg-research.net/analytics-and-data-buyers-guide-2023-market-observations", "datePublished": "03/10/2023", "dateModified": "03/10/2023", "articleBody": "The 2023 Ventana Research Buyers Guide for Analytics and Data research enables me to provide observations about how the market has advanced. The processes and technology of the analytics and data software industry continue to play an instrumental role in enabling an organization’s business units and IT to optimally utilize data in both tactical and strategic ways. To accomplish this, organizations must provide technology that can access the data, generate and apply insights from analytics, communicate the results and support collaboration as needed. Analytics utilizes mathematics to create measurements and metrics that enable data to be evaluated in whatever form, in whatever tool or application is needed to provide insights and guide decision-making. In today’s data-driven world, organizations must use analytics to understand and plan the details of their operations across every department and across the lines of business and IT. Organizations use analytics to track costs, create staffing plans, assess employee and supplier performance, identify variances and plan corrective actions. Analytics also helps inform employees and facilitates communication throughout the organization to coordinate actions toward a common mission and specific objectives. Operating without analytics would be like flying a plane without an instrument panel. While analytics as a modern business tool dates back more than three decades, the elements of business intelligence (BI) today have expanded well beyond query, reporting, analysis and publishing. The tools now include the sourcing and integration of data and the use of analytics for planning and forecasting, as well as dashboards that present analytics in a variety of visualizations. Analytics today also enables presentations in the form of natural-language narratives with some vendors able to support multiple languages. The collaborative sharing of insights is helping to reduce the time to take action and make decisions. Artificial intelligence and machine learning (AI/ML) extend analytics, enabling it to classify, predict and suggest behaviors that will help improve business operations. Vendors have also begun using ML to analyze product usage data to enhance and streamline interactions, anticipating the best next step in the analytical process and then performing or recommending that step. Advanced analytics, which incorporate AI and ML, has become a staple in analytic processes. Organizations that analyze their data using machine-learning technology state that they gain a competitive advantage, improve customer experiences, increase sales and respond faster to opportunities. In light of these benefits, it is no surprise that nearly two-thirds of organizations report using machine learning today, and three-quarters of organizations in our research said they plan to increase their use of machine learning. The ultimate goal of analytics is to help organizations make and implement decisions that improve their operations and bottom line. The range of analytics capabilities has become known as decision intelligence. Decision intelligence involves analyzing historical performance, determining potential courses of action, evaluating the results of those actions and then identifying the best path forward. Ideally, these processes can be conducted on a self-service basis. Self-service analytics continues to be a goal for most organizations, and those that can achieve it report greater satisfaction with their use of analytics. Organizations that can access analytics without the assistance of IT are more satisfied than those who require the assistance of IT. Organizations seeking to provide self-service analytics also need to provide self-service data preparation. This is one of the areas where we have seen significant additional capabilities from multiple vendors in this evaluation. In many of our research studies, preparing data is reported as the most time-consuming part of the analytics process, and our research finds that a majority of organizations are not comfortable allowing business users to work with data that has not been integrated or prepared for them by IT. We expect self-service data preparation capabilities will reduce the often-necessary involvement of IT. While AI and ML still require highly specialized skills, vendors have used elements of AI and ML to provide augmented intelligence capabilities such as automated insights and key driver analyses that require little or no input from the line-of-business personnel using the tools. These augmented intelligence capabilities make it easier for a larger portion of the workforce to gain insights they might not see otherwise. Augmented intelligence also ensures more consistency in an organization’s analytics discipline because many of the analyses are automated ensuring the same types of information are available to all. For analytics to be effective, they must be accessible. Many vendors now use natural language processing (NLP) to make it easier to access and find information via search and to understand information through narratives explaining the analyses. Analytics also need to be available to line-of-business personnel as needed in their normal course of conducting business, which means providing rich mobile access to analytics to support a workforce seeking to conduct business in any location at any time. Workers today expect these mobile capabilities, so organizations must make choices to provide analytics and data platforms that can deliver. Collaboration in conjunction with analytics has finally become much more commonplace. Two-thirds of organizations report they are using or plan to use collaboration with analytics. Vendors now provide many ways to enable collaboration ranging from commenting on analyses to rating data sources. Others provide ways for organizations to assign tasks and track them to completion, helping to ensure that the value of analytics results in specific actions taken by the organization. Analytics should also lead to action. Organizations use a variety of operational applications where the decisions resulting from analyses are implemented. Embedding analytics directly into these systems makes it easier for line-of-business workers to access the information they need without having to use a different system, reducing the need for additional training. As vendors continue to build out rich APIs that provide access to nearly all the functionality of their products, some vendors are also providing prebuilt connections delivering analytical outputs into operational systems. Ventana Research asserts that by 2026, more than two-thirds of line-of-business personnel will have immediate access to cross-functional analytics embedded in their activities and processes, helping to make operational decision-making more efficient and effective. Analytics must also be timely. Organizations often operate 24/7. Information streams into business operations from a rapidly growing number of devices and sources. Without the ability to analyze this information as it occurs, organizations risk missing the opportunity to respond in the moment. Our research shows that one-half of organizations consider it essential to process streaming data and event information in seconds or milliseconds. As organizations seek expand the spectrum of their analytic requirements, a transition to enterprise-class analytics is an essential step forward. Vendors have responded to these broadening needs with additional capabilities. In some cases, those vendors have invested in developing additional capabilities themselves. In others, they have acquired software vendors that offer complementary capabilities to their existing portfolio. Not only have the capabilities expanded, but the number of vendors has proliferated as well. Despite this expansion, there are still few vendors that attempt to provide the entire spectrum of capabilities we evaluate in this assessment. You will likely need more than one vendor to meet all your analytic needs. This research evaluates the following vendors that offer products that address key elements of analytics and data as we define it: AWS, Cloud Software Group, Domo, GoodData, Google, IBM, Idera, Incorta, Infor, insightsoftware, Microsoft, MicroStrategy, Oracle, Pyramid Analytics, Qlik, SAP, SAS, Sigma Computing, Sisense, Tableau (Salesforce), ThoughtSpot and Zoho. You can find more details on our site as well as in the Buyers Guide Market Report. Regards, David Menninger", "image": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/Analytics_and_Data_Buyers_Guide_2023_Market_Observations.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "David Menninger", "url": "https://blog.isg-research.net/author/david-menninger", "sameAs": "http://www.linkedin.com/in/davidmenninger" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "blog.isg-research.net", "logo": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/ISG_Software_Research_Logo_Color@2x.png" } } }</script> <script type="application/ld+json"> { "@context": "http://schema.org", "@type": "BlogPosting", "headline": "Build Network Security Resilience with DDI: Benefits and Challenges", "mainEntityOfPage": "https://blog.isg-research.net/build-network-security-resilience-with-ddi-benefits-and-challenges", "datePublished": "03/10/2023", "dateModified": "03/10/2023", "articleBody": "Network security resilience is the ability to withstand and recover from cyber-attacks. To achieve this, organizations need to use DDI technology. DDI stands for DNS, DHCP and IPAM. These are three services that help devices connect and communicate on a network. Domain name system (DNS) converts human-readable domain names into IP addresses. For example, when you type www.VentanaResearch.com, DNS tells your device the IP address of Ventana Research’s server. DHCP (dynamic host configuration protocol) assigns IP addresses to devices on a network. For example, when you connect your laptop to Wi-Fi, DHCP gives your computer an IP address. IPAM (IP address management) keeps track of IP addresses on a network. For example, IPAM can tell you which IP addresses are being in use, available or reserved. DDI can improve network security in many ways. DNS security can stop malware from using DNS to communicate with attackers or access malicious websites. DDI can also prevent IP address conflicts and unauthorized access to network resources. DDI management requires automation and centralization. Automation means using software to control and monitor DDI services. Centralization means containing allocation and utilization of network resources to effectively adapt to changing business needs. Automation and centralization can make DDI more efficient and secure, while reducing human error. Network landscapes are changing fast because of new trends such as hybrid and multi-cloud transformation, the internet of things (IoT) and distributed workforces. These trends create new security challenges for organizations. For example, malware can exploit DNS to bypass firewalls or steal data. Ventana Research asserts that through 2025, ineffective relationships between the IT and security teams will contribute to 3 in 5 organizations experiencing access and authentication vulnerabilities. To cope with these challenges, organizations need to adopt a proactive and holistic approach to network security resilience. Not only should DDI technology be used, but also integrated with other digital security approaches and best practices. Organizations can use DDI to enforce policies and compliance across different network environments, such as cloud, on-premises and hybrid. Organizations can also use DDI to enhance visibility and intelligence over their network assets and activities. By doing so, organizations can gain several benefits from DDI technology. First, they can improve their network performance and reliability by guaranteeing optimal configuration and allocation of IP addresses. Second, they can reduce their operational costs and complexity by automating and simplifying DDI management. Third, they can strengthen their network security posture by detecting and preventing DNS-based attacks. However, implementing and managing DDI technology is not without its challenges. Some of the common challenges include: Choosing the right DDI approach that meets the organization’s needs and budget. Migrating from legacy or disparate DDI systems to a unified and modern one. Training and educating staff on how to use and maintain the DDI platform. Monitoring and troubleshooting DDI issues across different network domains. Updating and patching the DDI platform to keep up with the latest security standards and functionality. To overcome these challenges, organizations need to follow some best practices for DDI implementation and management. Some of these best practices include: Conducting a thorough assessment of the current network environment and identifying the gaps and opportunities for improvement. Developing a clear strategy and roadmap for DDI deployment and integration with other network components. Choosing a reliable and reputable DDI vendor that offers comprehensive support and guidance throughout the project life cycle. Testing and validating the DDI approach before rolling it out to the production environment. Establishing clear roles and responsibilities for the DDI team and confirming effective communication and collaboration among stakeholders. DDI technology is a vital component of network security resilience, which is the ability to withstand and recover from cyber-attacks. Improving network security can prevent malware from exploiting DNS, avoiding IP address conflicts and unauthorized access, while also helping to enforce policies across various network environments. By adopting a DDI approach to digital security, organizations can protect themselves from the ever-changing security threats in today’s fast-evolving network landscape. Regards, Jeff Orr", "image": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/Build_Network_Security_Resilience_with_DDI_Benefits_and_Challenges.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "Jeff Orr", "url": "https://blog.isg-research.net/author/jeff-orr", "sameAs": "https://www.linkedin.com/in/jefforr" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "blog.isg-research.net", "logo": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/ISG_Software_Research_Logo_Color@2x.png" } } }</script> <script type="application/ld+json"> { "@context": "http://schema.org", "@type": "BlogPosting", "headline": "Cloudera Facilitates Versatility with Enterprise Data and AI", "mainEntityOfPage": "https://blog.isg-research.net/cloudera-facilitates-versatility-with-enterprise-data-and-ai", "datePublished": "03/10/2023", "dateModified": "03/10/2023", "articleBody": "The data platforms market may appear to have little or nothing to do with haute couture, but it is one of the data sectors most strongly influenced by the fickle finger of fashion. In recent years, various architectural approaches to data storage and processing have enjoyed a phase in the limelight, including data warehouse, data mart, data hub, data lake, cloud data warehouse, object storage, data lakehouse, data fabric and data mesh. These approaches are often heralded as the next big thing, set to overshadow those that came before. In practice, they are typically complementary, with most organizations employing a combination of products with different architectures to achieve business goals. Data platform vendors such as Cloudera need to build on trusted customer relationships to sustain business, even as fashions come and go. Cloudera was founded in 2008 to build a business around the Apache Hadoop data-processing framework. It enjoyed a rapid rise thanks to high levels of interest in the Hadoop project and big data, establishing itself as a primary data platform provider for Fortune 500 companies in financial services, retail, healthcare, telecommunications, manufacturing and energy/utilities along with government. Cloudera merged with its primary rival, Hortonworks, in 2019 amid market consolidation and a shift of trends toward cloud-based data processing and support for cloud object storage. It made its debut on the New York Stock Exchange in 2017. Cloudera was acquired by investment firms Clayton, Dubilier & Rice and KKR for $5.3 billion in June 2021, providing the privacy to transition its customer base to public and private cloud offerings outside the glare of public markets. The company has also highlighted the hybrid nature of its Cloudera Data Platform offering, providing versatility to support multiple workloads, deployment locations and architectural approaches. CDP can serve as an operational and analytic data platform, with functionality to address data engineering, streaming data and analytics and machine learning and artificial intelligence, including generative AI. It is available on-premises, in the cloud and across hybrid infrastructure. And while Cloudera might be typically deployed as a data lakehouse, the company is keen to highlight to existing and potential customers that its security, governance, and management capabilities provide a unified data fabric that facilitates the adoption of CDP as part of a data mesh. As one of the key vendors associated with Apache Hadoop and big data, Cloudera was undeniably a beneficiary of fashion in the data platform market during its early years. The trend toward big data enabled the company to establish itself as a primary data provider for numerous customers, especially large enterprises in financial services, healthcare, telecommunication, government, retail and utilities. Most of the company’s early deployments were on-premises on physical server architecture. The shift to the cloud had a fundamental impact on Cloudera’s product and customer base: Today, most of Cloudera’s customers – and the vast majority of its recurring revenue – are associated with the CDP offering, which is available on private and public cloud infrastructure. CDP Private Cloud is available for deployment on virtual private cloud infrastructure, and is comprised of the CDP Private Cloud Base foundation layer and data services to address data engineering, data warehouse and machine learning workloads. CDP Public Cloud is available on Amazon Web Service, Microsoft Azure and Google Cloud Platform and offers additional services to address data movement, stream processing, data hub and operational database workloads, in addition to engineering, data warehouse and machine learning. Support for hybrid cloud architecture is important for Cloudera, with many of its rivals offering only public cloud services. More than one-half (52%) of participants in Ventana Research’s Analytics and Data Benchmark Research have a hybrid architecture involving on-premises and cloud infrastructure. Earlier this year, Cloudera enhanced CDP’s functionality for monitoring the management of data, applications and infrastructure across public and private clouds with the general availability of Cloudera Observability, which provides functionality for system and service monitoring as well as workload optimization and financial governance. CDP is associated with the lakehouse architecture thanks to its support for object storage, data governance, table formats and query engine functionality. However, Cloudera’s underlying SDX security and governance technologies provide capabilities associated with a unified data fabric for managing and governing data across distributed environments, while the combination of SDX with data movement and data catalog capabilities supports the data mesh concept to facilitate the sharing, discovery and self-service access to data products. I have described the similarities and differences between data fabric and data mesh and see growing interest in both. I assert that by 2026, more than one-half of organizations will adopt technologies to facilitate the delivery of data as a product while adapting cultural and organizational approaches to data ownership in the context of data mesh. Cloudera has also recently taken steps to enhance CDP’s support for generative AI – in particular, the role enterprise data plays in improving trust in the output of large language models. The company has outlined its vision of layering support for generative AI models and applications on CDP for enhanced customer support. It recently launched LLM Chatbot Augmented with Enterprise Data as an addition to its catalog of Applied Machine Learning Prototypes, providing a framework for accelerating the development, deployment and monitoring of enterprise ML applications. Designer Coco Chanel is credited with saying, Fashion changes, but style endures. It’s a concept that Cloudera and other data platform vendors are no doubt conscious of while navigating the multiple trends influencing the data sector. The fashion for Hadoop and big data established Cloudera as a major player in the data platform sector, but thanks to its embrace of the cloud and investment in management, security and governance technologies, CDP is well-placed to play an integral part in data fabric and data mesh. Cloudera has been less vocal to date than some of its competitors about generative AI, but the company is taking a mature and considered approach to the enterprise applicability of LLMs. I recommend that any organization looking for a trusted partner to develop a strategy to take advantage of generative AI include Cloudera in its evaluations. 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