David Menninger's Analyst Perspectives

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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/analytics-and-data/data-operations" class="minimal-mobile-menu-link"><span>Data 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/analytics-and-data/data-platforms" class="minimal-mobile-menu-link"><span>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/analytics-and-data/streaming-and-events" class="minimal-mobile-menu-link"><span>Streaming & Events</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/artificial-intelligence" class="minimal-mobile-menu-link"><span>Artificial Intelligence</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/artificial-intelligence/computer-vision" class="minimal-mobile-menu-link"><span>Computer Vision</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.ventanaresearch.com/focus/artificial-intelligence/deep-learning" class="minimal-mobile-menu-link"><span>Deep Learning</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/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/artificial-intelligence/mlops" class="minimal-mobile-menu-link"><span>Machine Learning 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/artificial-intelligence/model-building-and-llm" 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 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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_dnd_area-module-2_" 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://davidmenninger.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 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://davidmenninger.isg-research.net/hubfs/_images/_backgrounds_expertise/2024_VR_AI_Expertise_Image_AdobeStock_637057734_1200px.jpg); ">  <div class="row-fluid"> <div class="blog-header__inner dnd-row"> <div class="blog--post__image dnd-section"> <img class="analyst-header-photo" src="https://davidmenninger.isg-research.net/hubfs/_images/_team/Menninger.David%20Pic.Circle.png" style="padding: 10px; "> <br> <h1 class="analyst-header-title">David Menninger's 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://davidmenninger.isg-research.net/david-menninger/2016/10/25/big-data-grows-up-at-stratahadoop-world-2016/">Big Data Grows Up at Strata+Hadoop World 2016</a></h2> <div class="author-meta"> Posted by <a href="https://davidmenninger.isg-research.net/author/david-menninger"> David Menninger </a> on <span class="post-date">25 October 2016 </span> </div>  <hr> </div> <div class="list-card-p"><div class="wrapingimage"> <img src="//davidmenninger.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_Big_Data_Analytics_19_important_areas_of_big_data_analytics_wide_updated.jpg?width=200&name=vr_Big_Data_Analytics_19_important_areas_of_big_data_analytics_wide_updated.jpg" width="200" !important: srcset="//davidmenninger.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_Big_Data_Analytics_19_important_areas_of_big_data_analytics_wide_updated.jpg?width=100&name=vr_Big_Data_Analytics_19_important_areas_of_big_data_analytics_wide_updated.jpg 100w, //davidmenninger.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_Big_Data_Analytics_19_important_areas_of_big_data_analytics_wide_updated.jpg?width=200&name=vr_Big_Data_Analytics_19_important_areas_of_big_data_analytics_wide_updated.jpg 200w, //davidmenninger.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_Big_Data_Analytics_19_important_areas_of_big_data_analytics_wide_updated.jpg?width=300&name=vr_Big_Data_Analytics_19_important_areas_of_big_data_analytics_wide_updated.jpg 300w, //davidmenninger.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_Big_Data_Analytics_19_important_areas_of_big_data_analytics_wide_updated.jpg?width=400&name=vr_Big_Data_Analytics_19_important_areas_of_big_data_analytics_wide_updated.jpg 400w, //davidmenninger.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_Big_Data_Analytics_19_important_areas_of_big_data_analytics_wide_updated.jpg?width=500&name=vr_Big_Data_Analytics_19_important_areas_of_big_data_analytics_wide_updated.jpg 500w, //davidmenninger.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_Big_Data_Analytics_19_important_areas_of_big_data_analytics_wide_updated.jpg?width=600&name=vr_Big_Data_Analytics_19_important_areas_of_big_data_analytics_wide_updated.jpg 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p>I recently spent time at <a href="http://conferences.oreilly.com/strata/hadoop-big-data-ny" target="_blank">Strata+Hadoop World 2016 in New York</a>. I attended this event and its predecessor, Hadoop World, off and on for the past six years. This one in New York had a different feel from previous events including the most recent event in San Jose at the end of March. Perhaps because of its location in one of the financial and commercial hubs of the world, the event had much more of a business orientation. But it’s not just location. Past events have been held in New York also,...</p> </div> <a href="https://davidmenninger.isg-research.net/david-menninger/2016/10/25/big-data-grows-up-at-stratahadoop-world-2016/"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://davidmenninger.isg-research.net/topic/big-data">Big Data</a>, <a href="https://davidmenninger.isg-research.net/topic/predictive-analytics">Predictive Analytics</a>, <a href="https://davidmenninger.isg-research.net/topic/stratahadoop">Strata+Hadoop</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://davidmenninger.isg-research.net/david-menninger/2016/05/11/data-lakes-safe-way-to-swim-in-big-data">Data Lakes: Safe Way to Swim in Big Data?</a></h2> <div class="author-meta"> Posted by <a href="https://davidmenninger.isg-research.net/author/david-menninger"> David Menninger </a> on <span class="post-date">11 May 2016 </span> </div>  <hr> </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://davidmenninger.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_big_data_analytics_21_external_data_sources_for_big_data_analytics.png?width=200&name=vr_big_data_analytics_21_external_data_sources_for_big_data_analytics.png" width="200" !important: srcset="https://davidmenninger.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_big_data_analytics_21_external_data_sources_for_big_data_analytics.png?width=100&name=vr_big_data_analytics_21_external_data_sources_for_big_data_analytics.png 100w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_big_data_analytics_21_external_data_sources_for_big_data_analytics.png?width=200&name=vr_big_data_analytics_21_external_data_sources_for_big_data_analytics.png 200w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_big_data_analytics_21_external_data_sources_for_big_data_analytics.png?width=300&name=vr_big_data_analytics_21_external_data_sources_for_big_data_analytics.png 300w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_big_data_analytics_21_external_data_sources_for_big_data_analytics.png?width=400&name=vr_big_data_analytics_21_external_data_sources_for_big_data_analytics.png 400w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_big_data_analytics_21_external_data_sources_for_big_data_analytics.png?width=500&name=vr_big_data_analytics_21_external_data_sources_for_big_data_analytics.png 500w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_big_data_analytics_21_external_data_sources_for_big_data_analytics.png?width=600&name=vr_big_data_analytics_21_external_data_sources_for_big_data_analytics.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p>It has been more than five years since James Dixon of Pentaho coined the term “data lake.” <a href="https://jamesdixon.wordpress.com/2010/10/14/pentaho-hadoop-and-data-lakes/" target="_blank">His original post</a> suggests, “If you think of a data mart as a store of bottled water – cleansed and packaged and structured for easy consumption – the data lake is a large body of water in a more natural state.” The analogy is a simple one, but in my experience talking with many end users there is still mystery surrounding the concept. In this post I’d like to clarify what a data lake is, review the...</p> </div> <a href="https://davidmenninger.isg-research.net/david-menninger/2016/05/11/data-lakes-safe-way-to-swim-in-big-data"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://davidmenninger.isg-research.net/topic/big-data">Big Data</a>, <a href="https://davidmenninger.isg-research.net/topic/data-science">Data Science</a>, <a href="https://davidmenninger.isg-research.net/topic/predictive-analytics">Predictive Analytics</a>, <a href="https://davidmenninger.isg-research.net/topic/social-media">Social Media</a>, <a href="https://davidmenninger.isg-research.net/topic/business-analytics">Business Analytics</a>, <a href="https://davidmenninger.isg-research.net/topic/business-intelligence">Business Intelligence</a>, <a href="https://davidmenninger.isg-research.net/topic/data-governance">Data Governance</a>, <a href="https://davidmenninger.isg-research.net/topic/data-lake">Data Lake</a>, <a href="https://davidmenninger.isg-research.net/topic/governance-risk-compliance-grc">Governance, Risk & Compliance (GRC)</a>, <a href="https://davidmenninger.isg-research.net/topic/information-management">Information Management</a>, <a href="https://davidmenninger.isg-research.net/topic/uncategorized">Uncategorized</a>, <a href="https://davidmenninger.isg-research.net/topic/stratahadoop">Strata+Hadoop</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://davidmenninger.isg-research.net/david-menninger/2016/03/11/sas-analytics-juggernaut-keeps-on-truckin">SAS Analytics Juggernaut Keeps on Truckin’</a></h2> <div class="author-meta"> Posted by <a href="https://davidmenninger.isg-research.net/author/david-menninger"> David Menninger </a> on <span class="post-date">11 March 2016 </span> </div>  <hr> </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://davidmenninger.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_ng_predictive_analytics_16_why_users_dont_produce_predictive_analyses-1.png?width=200&name=vr_ng_predictive_analytics_16_why_users_dont_produce_predictive_analyses-1.png" width="200" !important: srcset="https://davidmenninger.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_ng_predictive_analytics_16_why_users_dont_produce_predictive_analyses-1.png?width=100&name=vr_ng_predictive_analytics_16_why_users_dont_produce_predictive_analyses-1.png 100w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_ng_predictive_analytics_16_why_users_dont_produce_predictive_analyses-1.png?width=200&name=vr_ng_predictive_analytics_16_why_users_dont_produce_predictive_analyses-1.png 200w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_ng_predictive_analytics_16_why_users_dont_produce_predictive_analyses-1.png?width=300&name=vr_ng_predictive_analytics_16_why_users_dont_produce_predictive_analyses-1.png 300w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_ng_predictive_analytics_16_why_users_dont_produce_predictive_analyses-1.png?width=400&name=vr_ng_predictive_analytics_16_why_users_dont_produce_predictive_analyses-1.png 400w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_ng_predictive_analytics_16_why_users_dont_produce_predictive_analyses-1.png?width=500&name=vr_ng_predictive_analytics_16_why_users_dont_produce_predictive_analyses-1.png 500w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_ng_predictive_analytics_16_why_users_dont_produce_predictive_analyses-1.png?width=600&name=vr_ng_predictive_analytics_16_why_users_dont_produce_predictive_analyses-1.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p>I recently attended the SAS Analyst Summit in Steamboat Springs, Colo. (<a href="http://twitter.com/hashtag/sassb" target="_blank">Twitter Hashtag #SASSB</a>) The event offers an occasion for the company to discuss its direction and to assess its strengths and potential weaknesses. SAS is privately held, so customers and prospects cannot subject its performance to the same level of scrutiny as public companies, and thus events like this one provide a valuable source of additional information.</p> </div> <a href="https://davidmenninger.isg-research.net/david-menninger/2016/03/11/sas-analytics-juggernaut-keeps-on-truckin"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://davidmenninger.isg-research.net/topic/big-data">Big Data</a>, <a href="https://davidmenninger.isg-research.net/topic/predictive-analytics">Predictive Analytics</a>, <a href="https://davidmenninger.isg-research.net/topic/sas">SAS</a>, <a href="https://davidmenninger.isg-research.net/topic/analytics">Analytics</a>, <a href="https://davidmenninger.isg-research.net/topic/business-analytics">Business Analytics</a>, <a href="https://davidmenninger.isg-research.net/topic/business-collaboration">Business Collaboration</a>, <a href="https://davidmenninger.isg-research.net/topic/business-intelligence">Business Intelligence</a>, <a href="https://davidmenninger.isg-research.net/topic/business-performance">Business Performance</a>, <a href="https://davidmenninger.isg-research.net/topic/information-applications">Information Applications</a>, <a href="https://davidmenninger.isg-research.net/topic/information-management">Information Management</a>, <a href="https://davidmenninger.isg-research.net/topic/uncategorized">Uncategorized</a>, <a href="https://davidmenninger.isg-research.net/topic/visualization">Visualization</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://davidmenninger.isg-research.net/david-menninger/2016/02/08/pardon-the-interruption-industry-veteran-returns-to-ventana-research">Pardon the Interruption: Industry Veteran Returns to Ventana Research</a></h2> <div class="author-meta"> Posted by <a href="https://davidmenninger.isg-research.net/author/david-menninger"> David Menninger </a> on <span class="post-date">07 February 2016 </span> </div>  <hr> </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://davidmenninger.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_big_data_analytics_18_big_data_technology_in_use.png?width=200&name=vr_big_data_analytics_18_big_data_technology_in_use.png" width="200" !important: srcset="https://davidmenninger.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_big_data_analytics_18_big_data_technology_in_use.png?width=100&name=vr_big_data_analytics_18_big_data_technology_in_use.png 100w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_big_data_analytics_18_big_data_technology_in_use.png?width=200&name=vr_big_data_analytics_18_big_data_technology_in_use.png 200w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_big_data_analytics_18_big_data_technology_in_use.png?width=300&name=vr_big_data_analytics_18_big_data_technology_in_use.png 300w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_big_data_analytics_18_big_data_technology_in_use.png?width=400&name=vr_big_data_analytics_18_big_data_technology_in_use.png 400w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_big_data_analytics_18_big_data_technology_in_use.png?width=500&name=vr_big_data_analytics_18_big_data_technology_in_use.png 500w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_big_data_analytics_18_big_data_technology_in_use.png?width=600&name=vr_big_data_analytics_18_big_data_technology_in_use.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p>Some followers of Ventana Research may recall my work here several years ago. Here and elsewhere I have spent most of my career in the data and analytics markets matching user requirements with technologies to meet those needs. I’m happy to be returning to Ventana Research to resume investigating ways in which organizations can make the most of their data to improve their business processes; for a first look, please see <u><a href="//davidmenninger.ventanaresearch.com/2016/02/08/research-agenda-using-business-analytics-to-make-the-most-of-data-in-2016/" target="_blank">our 2016 research agenda on Big Data and Information Optimization</a></u>. I relish...</p> </div> <a href="https://davidmenninger.isg-research.net/david-menninger/2016/02/08/pardon-the-interruption-industry-veteran-returns-to-ventana-research"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://davidmenninger.isg-research.net/topic/big-data">Big Data</a>, <a href="https://davidmenninger.isg-research.net/topic/predictive-analytics">Predictive Analytics</a>, <a href="https://davidmenninger.isg-research.net/topic/analytics">Analytics</a>, <a href="https://davidmenninger.isg-research.net/topic/business-analytics">Business Analytics</a>, <a href="https://davidmenninger.isg-research.net/topic/business-intelligence">Business Intelligence</a>, <a href="https://davidmenninger.isg-research.net/topic/information-management">Information Management</a>, <a href="https://davidmenninger.isg-research.net/topic/internet-of-things">Internet of Things</a>, <a href="https://davidmenninger.isg-research.net/topic/operational-intelligence">Operational Intelligence</a>, <a href="https://davidmenninger.isg-research.net/topic/uncategorized">Uncategorized</a>, <a href="https://davidmenninger.isg-research.net/topic/unicorns">Unicorns</a>, <a href="https://davidmenninger.isg-research.net/topic/it-performance-management-itpm">IT Performance Management (ITPM)</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://davidmenninger.isg-research.net/david-menninger/2016/02/08/research-agenda-using-business-analytics-to-make-the-most-of-data-in-2016">Research Agenda: Using Business Analytics to Make the Most of Data in 2016</a></h2> <div class="author-meta"> Posted by <a href="https://davidmenninger.isg-research.net/author/david-menninger"> David Menninger </a> on <span class="post-date">07 February 2016 </span> </div>  <hr> </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://davidmenninger.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_ng_predictive_analytics_08_time_spent_in_predictive_analytic_process.png?width=200&name=vr_ng_predictive_analytics_08_time_spent_in_predictive_analytic_process.png" width="200" !important: srcset="https://davidmenninger.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_ng_predictive_analytics_08_time_spent_in_predictive_analytic_process.png?width=100&name=vr_ng_predictive_analytics_08_time_spent_in_predictive_analytic_process.png 100w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_ng_predictive_analytics_08_time_spent_in_predictive_analytic_process.png?width=200&name=vr_ng_predictive_analytics_08_time_spent_in_predictive_analytic_process.png 200w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_ng_predictive_analytics_08_time_spent_in_predictive_analytic_process.png?width=300&name=vr_ng_predictive_analytics_08_time_spent_in_predictive_analytic_process.png 300w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_ng_predictive_analytics_08_time_spent_in_predictive_analytic_process.png?width=400&name=vr_ng_predictive_analytics_08_time_spent_in_predictive_analytic_process.png 400w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_ng_predictive_analytics_08_time_spent_in_predictive_analytic_process.png?width=500&name=vr_ng_predictive_analytics_08_time_spent_in_predictive_analytic_process.png 500w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_ng_predictive_analytics_08_time_spent_in_predictive_analytic_process.png?width=600&name=vr_ng_predictive_analytics_08_time_spent_in_predictive_analytic_process.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p>Throughout the course of our research in 2016, we’ll be exploring ways in which organizations can maximize the value of their data. Ventana Research believes that analytics is the engine and data is the fuel to power better business decisions. Several themes emerged from our benchmark research on incorporating data and analytics into organizational processes, and we will follow them in our 2016 Business Analytics Research Agenda:</p> </div> <a href="https://davidmenninger.isg-research.net/david-menninger/2016/02/08/research-agenda-using-business-analytics-to-make-the-most-of-data-in-2016"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://davidmenninger.isg-research.net/topic/big-data">Big Data</a>, <a href="https://davidmenninger.isg-research.net/topic/predictive-analytics">Predictive Analytics</a>, <a href="https://davidmenninger.isg-research.net/topic/analytics">Analytics</a>, <a href="https://davidmenninger.isg-research.net/topic/business-analytics">Business Analytics</a>, <a href="https://davidmenninger.isg-research.net/topic/business-collaboration">Business Collaboration</a>, <a href="https://davidmenninger.isg-research.net/topic/business-intelligence">Business Intelligence</a>, <a href="https://davidmenninger.isg-research.net/topic/cloud-computing">Cloud Computing</a>, <a href="https://davidmenninger.isg-research.net/topic/information-applications">Information Applications</a>, <a href="https://davidmenninger.isg-research.net/topic/information-management">Information Management</a>, <a href="https://davidmenninger.isg-research.net/topic/operational-intelligence">Operational Intelligence</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://davidmenninger.isg-research.net/david-menninger/2012/03/27/research-uncovers-keys-to-using-predictive-analytics">Research Uncovers Keys to Using Predictive Analytics</a></h2> <div class="author-meta"> Posted by <a href="https://davidmenninger.isg-research.net/author/david-menninger"> David Menninger </a> on <span class="post-date">27 March 2012 </span> </div>  <hr> </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://davidmenninger.isg-research.net/hs-fs/hubfs/Homepage_Slider/Slider_3.png?width=200&name=Slider_3.png" width="200" !important: srcset="https://davidmenninger.isg-research.net/hs-fs/hubfs/Homepage_Slider/Slider_3.png?width=100&name=Slider_3.png 100w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Homepage_Slider/Slider_3.png?width=200&name=Slider_3.png 200w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Homepage_Slider/Slider_3.png?width=300&name=Slider_3.png 300w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Homepage_Slider/Slider_3.png?width=400&name=Slider_3.png 400w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Homepage_Slider/Slider_3.png?width=500&name=Slider_3.png 500w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Homepage_Slider/Slider_3.png?width=600&name=Slider_3.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p><span style="font-family: Verdana; font-size: small;">As a technology, predictive analytics has existed for years, but adoption has not been widespread among businesses. In our recent benchmark research on business analytics among more than 2,600 organizations, predictive analytics ranked only 10th among technologies they use to generate analytics, and only one in eight of those companies use it. Predictive analytics has been costly to acquire, and while enterprises in a few vertical industries and specific lines of business have been willing to...</span></p> </div> <a href="https://davidmenninger.isg-research.net/david-menninger/2012/03/27/research-uncovers-keys-to-using-predictive-analytics"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://davidmenninger.isg-research.net/topic/data-scientist">Data Scientist</a>, <a href="https://davidmenninger.isg-research.net/topic/predictive-analytics">Predictive Analytics</a>, <a href="https://davidmenninger.isg-research.net/topic/sales-performance">Sales Performance</a>, <a href="https://davidmenninger.isg-research.net/topic/social-media">Social Media</a>, <a href="https://davidmenninger.isg-research.net/topic/supply-chain-performance">Supply Chain Performance</a>, <a href="https://davidmenninger.isg-research.net/topic/analytics">Analytics</a>, <a href="https://davidmenninger.isg-research.net/topic/business-analytics">Business Analytics</a>, <a href="https://davidmenninger.isg-research.net/topic/business-intelligence">Business Intelligence</a>, <a href="https://davidmenninger.isg-research.net/topic/customer-contact-center">Customer & Contact Center</a>, <a href="https://davidmenninger.isg-research.net/topic/workforce-performance">Workforce Performance</a>, <a href="https://davidmenninger.isg-research.net/topic/it-performance-management-itpm">IT Performance Management (ITPM)</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://davidmenninger.isg-research.net/david-menninger/2012/03/26/sas-advances-big-data-and-cloud-computing">SAS Advances Big Data and Cloud Computing</a></h2> <div class="author-meta"> Posted by <a href="https://davidmenninger.isg-research.net/author/david-menninger"> David Menninger </a> on <span class="post-date">26 March 2012 </span> </div>  <hr> </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://davidmenninger.isg-research.net/hs-fs/hubfs/Homepage_Slider/Slider_3.png?width=200&name=Slider_3.png" width="200" !important: srcset="https://davidmenninger.isg-research.net/hs-fs/hubfs/Homepage_Slider/Slider_3.png?width=100&name=Slider_3.png 100w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Homepage_Slider/Slider_3.png?width=200&name=Slider_3.png 200w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Homepage_Slider/Slider_3.png?width=300&name=Slider_3.png 300w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Homepage_Slider/Slider_3.png?width=400&name=Slider_3.png 400w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Homepage_Slider/Slider_3.png?width=500&name=Slider_3.png 500w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Homepage_Slider/Slider_3.png?width=600&name=Slider_3.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p><span style="font-size: small;"><span style="font-family: Verdana;">I want to share my observations from the recent annual SAS analyst briefing. SAS is a huge software company with a unique culture and a history of success. Being privately held SAS is not required to make the same financial disclosures as publicly held organizations, it released enough information to suggest another successful year, with more than $2.7 billion in revenue and 10 percent growth in its core analytics and data management businesses. Smaller segments showed even higher growth rates....</span></span></p> </div> <a href="https://davidmenninger.isg-research.net/david-menninger/2012/03/26/sas-advances-big-data-and-cloud-computing"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://davidmenninger.isg-research.net/topic/big-data">Big Data</a>, <a href="https://davidmenninger.isg-research.net/topic/predictive-analytics">Predictive Analytics</a>, <a href="https://davidmenninger.isg-research.net/topic/sas">SAS</a>, <a href="https://davidmenninger.isg-research.net/topic/analytics">Analytics</a>, <a href="https://davidmenninger.isg-research.net/topic/business-analytics">Business Analytics</a>, <a href="https://davidmenninger.isg-research.net/topic/business-intelligence">Business Intelligence</a>, <a href="https://davidmenninger.isg-research.net/topic/business-performance">Business Performance</a>, <a href="https://davidmenninger.isg-research.net/topic/cloud-computing">Cloud Computing</a>, <a href="https://davidmenninger.isg-research.net/topic/stratahadoop">Strata+Hadoop</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://davidmenninger.isg-research.net/david-menninger/2012/03/15/business-intelligence-revolution-and-research-agenda-for-2012">Business Intelligence Revolution and Research Agenda for 2012</a></h2> <div class="author-meta"> Posted by <a href="https://davidmenninger.isg-research.net/author/david-menninger"> David Menninger </a> on <span class="post-date">15 March 2012 </span> </div>  <hr> </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://davidmenninger.isg-research.net/hs-fs/hubfs/Homepage_Slider/Slider_3.png?width=200&name=Slider_3.png" width="200" !important: srcset="https://davidmenninger.isg-research.net/hs-fs/hubfs/Homepage_Slider/Slider_3.png?width=100&name=Slider_3.png 100w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Homepage_Slider/Slider_3.png?width=200&name=Slider_3.png 200w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Homepage_Slider/Slider_3.png?width=300&name=Slider_3.png 300w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Homepage_Slider/Slider_3.png?width=400&name=Slider_3.png 400w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Homepage_Slider/Slider_3.png?width=500&name=Slider_3.png 500w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Homepage_Slider/Slider_3.png?width=600&name=Slider_3.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p><span style="font-size: small;"><span style="font-family: Verdana;">For most people involved with business intelligence (BI), these are exciting times. Using BI to improve business processes continues to motivate organizations to invest in BI. The focus on BI also empowers business analytics and can be rented in the cloud computing model of accessing software. New technologies are adding dimensions to BI and creating both excitement and confusion for enterprises implementing them. We offer a variety of accomplished research that can help organizations overcome...</span></span></p> </div> <a href="https://davidmenninger.isg-research.net/david-menninger/2012/03/15/business-intelligence-revolution-and-research-agenda-for-2012"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://davidmenninger.isg-research.net/topic/mobile-business-intelligence">Mobile Business Intelligence</a>, <a href="https://davidmenninger.isg-research.net/topic/predictive-analytics">Predictive Analytics</a>, <a href="https://davidmenninger.isg-research.net/topic/sales-performance">Sales Performance</a>, <a href="https://davidmenninger.isg-research.net/topic/social-media">Social Media</a>, <a href="https://davidmenninger.isg-research.net/topic/supply-chain-performance">Supply Chain Performance</a>, <a href="https://davidmenninger.isg-research.net/topic/business-analytics">Business Analytics</a>, <a href="https://davidmenninger.isg-research.net/topic/business-intelligence">Business Intelligence</a>, <a href="https://davidmenninger.isg-research.net/topic/business-performance">Business Performance</a>, <a href="https://davidmenninger.isg-research.net/topic/collaboration">Collaboration</a>, <a href="https://davidmenninger.isg-research.net/topic/customer-contact-center">Customer & Contact Center</a>, <a href="https://davidmenninger.isg-research.net/topic/financial-performance">Financial Performance</a>, <a href="https://davidmenninger.isg-research.net/topic/information-management">Information Management</a>, <a href="https://davidmenninger.isg-research.net/topic/operational-intelligence">Operational Intelligence</a>, <a href="https://davidmenninger.isg-research.net/topic/workforce-performance">Workforce Performance</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://davidmenninger.isg-research.net/david-menninger/2012/02/01/revolution-analytics-hosts-contest-on-business-predicting-the-future">Revolution Analytics Hosts Contest on Business Predicting the Future</a></h2> <div class="author-meta"> Posted by <a href="https://davidmenninger.isg-research.net/author/david-menninger"> David Menninger </a> on <span class="post-date">01 February 2012 </span> </div>  <hr> </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://davidmenninger.isg-research.net/hs-fs/hubfs/Homepage_Slider/Slider_3.png?width=200&name=Slider_3.png" width="200" !important: srcset="https://davidmenninger.isg-research.net/hs-fs/hubfs/Homepage_Slider/Slider_3.png?width=100&name=Slider_3.png 100w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Homepage_Slider/Slider_3.png?width=200&name=Slider_3.png 200w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Homepage_Slider/Slider_3.png?width=300&name=Slider_3.png 300w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Homepage_Slider/Slider_3.png?width=400&name=Slider_3.png 400w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Homepage_Slider/Slider_3.png?width=500&name=Slider_3.png 500w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Homepage_Slider/Slider_3.png?width=600&name=Slider_3.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p>Revolution Analytics recently <span style="text-decoration: underline;"><a href="http://www.revolutionanalytics.com/news-events/news-room/2012/revolution-analytics-announces-application-of-r-in-business-contest-winners.php" target="_blank">announced the winners</a></span> of its “Applications of R in Business” contest. Revolution Analytics has built a business around supporting R, an open source statistical software package, and extending it with features it licenses to customers. I served as a judge in the contest. Since I was in the midst of analyzing the data for our <span style="text-decoration: underline;"><a href="http://ventanaresearch.com/pat/" target="_blank">predictive analytics benchmark research</a></span>, I was interested to see how the contestants applied predictive analytics techniques to specific...</p> </div> <a href="https://davidmenninger.isg-research.net/david-menninger/2012/02/01/revolution-analytics-hosts-contest-on-business-predicting-the-future"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://davidmenninger.isg-research.net/topic/predictive-analytics">Predictive Analytics</a>, <a href="https://davidmenninger.isg-research.net/topic/sales-performance">Sales Performance</a>, <a href="https://davidmenninger.isg-research.net/topic/social-media">Social Media</a>, <a href="https://davidmenninger.isg-research.net/topic/supply-chain-performance">Supply Chain Performance</a>, <a href="https://davidmenninger.isg-research.net/topic/business-analytics">Business Analytics</a>, <a href="https://davidmenninger.isg-research.net/topic/business-intelligence">Business Intelligence</a>, <a href="https://davidmenninger.isg-research.net/topic/business-performance">Business Performance</a>, <a href="https://davidmenninger.isg-research.net/topic/customer-contact-center">Customer & Contact Center</a>, <a href="https://davidmenninger.isg-research.net/topic/financial-performance">Financial Performance</a>, <a href="https://davidmenninger.isg-research.net/topic/workforce-performance">Workforce Performance</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://davidmenninger.isg-research.net/david-menninger/2012/02/01/technology-terminology-whats-in-a-name">Technology Terminology: What’s in a Name?</a></h2> <div class="author-meta"> Posted by <a href="https://davidmenninger.isg-research.net/author/david-menninger"> David Menninger </a> on <span class="post-date">01 February 2012 </span> </div>  <hr> </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://davidmenninger.isg-research.net/hs-fs/hubfs/Homepage_Slider/Slider_3.png?width=200&name=Slider_3.png" width="200" !important: srcset="https://davidmenninger.isg-research.net/hs-fs/hubfs/Homepage_Slider/Slider_3.png?width=100&name=Slider_3.png 100w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Homepage_Slider/Slider_3.png?width=200&name=Slider_3.png 200w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Homepage_Slider/Slider_3.png?width=300&name=Slider_3.png 300w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Homepage_Slider/Slider_3.png?width=400&name=Slider_3.png 400w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Homepage_Slider/Slider_3.png?width=500&name=Slider_3.png 500w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Homepage_Slider/Slider_3.png?width=600&name=Slider_3.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p><span style="font-family: Verdana;">When it comes to technology, debates about whether a particular name suits its category are rampant. Here is a link to <span style="text-decoration: underline;"><a title="one such argument about the term “big data”" href="http://www.dbms2.com/2011/09/11/big-data-has-jumped-the-shark/" target="_blank">one such argument about the term “big data”</a></span> from Curt Monash, an analyst whom I respect a great deal. This debate rages in the Twittersphere also, as in <span style="text-decoration: underline;"><a title="this comment" href="http://twitter.com/#!/NeilRaden/statuses/117236703005130752" target="_blank">this comment</a></span> from Neil Raden, another analyst I respect, suggesting that “big data is a marketing term … imprecise by design.”  Another term I’ve encountered resistance to recently is “predictive analytics.” See: (<a title="“Revolution Analytics Hosts Contest on Business Predicting the Future“" href="//davidmenninger.ventanaresearch.com/2012/02/01/revolution-analytics-hosts-contest-on-business-predicting-the-future/" target="_blank">“Revolution...</a></span></p> </div> <a href="https://davidmenninger.isg-research.net/david-menninger/2012/02/01/technology-terminology-whats-in-a-name"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://davidmenninger.isg-research.net/topic/predictive-analytics">Predictive Analytics</a>, <a href="https://davidmenninger.isg-research.net/topic/sales-performance">Sales Performance</a>, <a href="https://davidmenninger.isg-research.net/topic/social-media">Social Media</a>, <a href="https://davidmenninger.isg-research.net/topic/supply-chain-performance">Supply Chain Performance</a>, <a href="https://davidmenninger.isg-research.net/topic/business-analytics">Business Analytics</a>, <a href="https://davidmenninger.isg-research.net/topic/business-intelligence">Business Intelligence</a>, <a href="https://davidmenninger.isg-research.net/topic/business-performance">Business Performance</a>, <a href="https://davidmenninger.isg-research.net/topic/cloud-computing">Cloud Computing</a>, <a href="https://davidmenninger.isg-research.net/topic/customer-contact-center">Customer & Contact Center</a>, <a href="https://davidmenninger.isg-research.net/topic/financial-performance">Financial Performance</a>, <a href="https://davidmenninger.isg-research.net/topic/workforce-performance">Workforce Performance</a> <hr> </div> <script> // Check active classes var checkClass = function() { if ( $('.fil_item').hasClass('hide') ) { $('.fil_item').removeClass('hide'); 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If you have any issue with an Analyst Perspective, please email them to <a href="mailto:ChiefResearchOfficer@isg-research.net">ChiefResearchOfficer@isg-research.net</a></p> </li> </ul> <a class="btn-secondary" href="https://www.isg-research.net/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://davidmenninger.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://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Menninger.David%20Pic.Circle.png?width=50&name=Menninger.David%20Pic.Circle.png 50w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Menninger.David%20Pic.Circle.png?width=100&name=Menninger.David%20Pic.Circle.png 100w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Menninger.David%20Pic.Circle.png?width=150&name=Menninger.David%20Pic.Circle.png 150w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Menninger.David%20Pic.Circle.png?width=200&name=Menninger.David%20Pic.Circle.png 200w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Menninger.David%20Pic.Circle.png?width=250&name=Menninger.David%20Pic.Circle.png 250w, https://davidmenninger.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://davidmenninger.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://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Orr.Jeff.Circle.png?width=50&name=Orr.Jeff.Circle.png 50w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Orr.Jeff.Circle.png?width=100&name=Orr.Jeff.Circle.png 100w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Orr.Jeff.Circle.png?width=150&name=Orr.Jeff.Circle.png 150w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Orr.Jeff.Circle.png?width=200&name=Orr.Jeff.Circle.png 200w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Orr.Jeff.Circle.png?width=250&name=Orr.Jeff.Circle.png 250w, https://davidmenninger.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://davidmenninger.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://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Dawson.Keith%20Pic.Circle.png?width=50&name=Dawson.Keith%20Pic.Circle.png 50w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Dawson.Keith%20Pic.Circle.png?width=100&name=Dawson.Keith%20Pic.Circle.png 100w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Dawson.Keith%20Pic.Circle.png?width=150&name=Dawson.Keith%20Pic.Circle.png 150w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Dawson.Keith%20Pic.Circle.png?width=200&name=Dawson.Keith%20Pic.Circle.png 200w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Dawson.Keith%20Pic.Circle.png?width=250&name=Dawson.Keith%20Pic.Circle.png 250w, https://davidmenninger.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://davidmenninger.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://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Smith.Mark%20Pic.Circle.png?width=50&name=Smith.Mark%20Pic.Circle.png 50w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Smith.Mark%20Pic.Circle.png?width=100&name=Smith.Mark%20Pic.Circle.png 100w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Smith.Mark%20Pic.Circle.png?width=150&name=Smith.Mark%20Pic.Circle.png 150w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Smith.Mark%20Pic.Circle.png?width=200&name=Smith.Mark%20Pic.Circle.png 200w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Smith.Mark%20Pic.Circle.png?width=250&name=Smith.Mark%20Pic.Circle.png 250w, https://davidmenninger.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://davidmenninger.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://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Aslett.Matt%20Pic.Circle.png?width=50&name=Aslett.Matt%20Pic.Circle.png 50w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Aslett.Matt%20Pic.Circle.png?width=100&name=Aslett.Matt%20Pic.Circle.png 100w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Aslett.Matt%20Pic.Circle.png?width=150&name=Aslett.Matt%20Pic.Circle.png 150w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Aslett.Matt%20Pic.Circle.png?width=200&name=Aslett.Matt%20Pic.Circle.png 200w, 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style="text-align: center;"> <a href="https://robertkugel.isg-research.net"> <img class="analyst_img" style="width: 100px;" src="https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Kugel.Robert%20Pic.Circle.png?width=100&name=Kugel.Robert%20Pic.Circle.png" alt="Robert Kugel" width="100" data-constrained="true" caption="false" sizes="(max-width: 100px) 100vw, 100px" srcset="https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Kugel.Robert%20Pic.Circle.png?width=50&name=Kugel.Robert%20Pic.Circle.png 50w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Kugel.Robert%20Pic.Circle.png?width=100&name=Kugel.Robert%20Pic.Circle.png 100w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Kugel.Robert%20Pic.Circle.png?width=150&name=Kugel.Robert%20Pic.Circle.png 150w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Kugel.Robert%20Pic.Circle.png?width=200&name=Kugel.Robert%20Pic.Circle.png 200w, 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https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Hurrell.Stephen%20Pic.Circle.png?width=100&name=Hurrell.Stephen%20Pic.Circle.png 100w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Hurrell.Stephen%20Pic.Circle.png?width=150&name=Hurrell.Stephen%20Pic.Circle.png 150w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Hurrell.Stephen%20Pic.Circle.png?width=200&name=Hurrell.Stephen%20Pic.Circle.png 200w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Hurrell.Stephen%20Pic.Circle.png?width=250&name=Hurrell.Stephen%20Pic.Circle.png 250w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Hurrell.Stephen%20Pic.Circle.png?width=300&name=Hurrell.Stephen%20Pic.Circle.png 300w"> <h5 class="analyst_name">Stephen Hurrell</h5> </a> </div> </div>   <div class="row-fluid">  <div style="text-align: center;"> <a href="https://blog.isg-research.net"> <img class="analyst_img" style="width: 100px;" 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dir="ltr">(9)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2019/02">February 2019 <span class="filter-link-count" dir="ltr">(6)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2019/01">January 2019 <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2018/12">December 2018 <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2018/11">November 2018 <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2018/10">October 2018 <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2018/09">September 2018 <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2018/02">February 2018 <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2017/12">December 2017 <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2017/11">November 2017 <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2017/09">September 2017 <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2017/08">August 2017 <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2017/07">July 2017 <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2017/06">June 2017 <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2017/05">May 2017 <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2017/04">April 2017 <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2017/03">March 2017 <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2017/02">February 2017 <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2017/01">January 2017 <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2016/12">December 2016 <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2016/11">November 2016 <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2016/10">October 2016 <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2016/09">September 2016 <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2016/08">August 2016 <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2016/07">July 2016 <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2016/05">May 2016 <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2016/04">April 2016 <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2016/03">March 2016 <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2016/02">February 2016 <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2012/03">March 2012 <span class="filter-link-count" dir="ltr">(5)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2012/02">February 2012 <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2012/01">January 2012 <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2011/12">December 2011 <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2011/11">November 2011 <span class="filter-link-count" dir="ltr">(6)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2011/10">October 2011 <span class="filter-link-count" dir="ltr">(6)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2011/09">September 2011 <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2011/08">August 2011 <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2011/07">July 2011 <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2011/06">June 2011 <span class="filter-link-count" dir="ltr">(6)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2011/05">May 2011 <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2011/04">April 2011 <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2011/03">March 2011 <span class="filter-link-count" dir="ltr">(7)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2011/02">February 2011 <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2011/01">January 2011 <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2010/12">December 2010 <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2010/11">November 2010 <span class="filter-link-count" dir="ltr">(12)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.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://davidmenninger.isg-research.net/topic/ai">AI <span class="filter-link-count" dir="ltr">(11)</span></a> </li> <li> <a href="https://davidmenninger.isg-research.net/topic/ai-and-machine-learning">AI and Machine Learning <span class="filter-link-count" dir="ltr">(95)</span></a> </li> <li> <a href="https://davidmenninger.isg-research.net/topic/alteryx">alteryx <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li> <a href="https://davidmenninger.isg-research.net/topic/alteryx-inspire">Alteryx Inspire <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li> <a href="https://davidmenninger.isg-research.net/topic/analytic-data-platforms">analytic data platforms <span class="filter-link-count" dir="ltr">(1)</span></a> </li> 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href="https://davidmenninger.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://davidmenninger.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://davidmenninger.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://davidmenninger.isg-research.net/topic/exalytics">Exalytics <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/explorium">Explorium <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/extended-reality">extended reality <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.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://davidmenninger.isg-research.net/topic/financial-performance">Financial Performance <span class="filter-link-count" dir="ltr">(32)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/financial-performance-management">Financial Performance Management <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/genai">GenAI <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/generative-ai">Generative AI <span class="filter-link-count" dir="ltr">(8)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/governance-risk">Governance & Risk <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/governance-risk-compliance-grc">Governance, Risk & Compliance (GRC) <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/greenplum">Greenplum <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/hadoop">Hadoop <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/hortonworks">Hortonworks <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/hp">HP <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/hpe">HPE <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/human-capital-management">Human Capital Management <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/ibm">IBM <span class="filter-link-count" dir="ltr">(6)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.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://davidmenninger.isg-research.net/topic/ibm-watson">IBM Watson <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/in-memory">In-memory <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/in-memory-computing">In-Memory Computing <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/inetsoft">InetSoft <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/infor">Infor <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.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://davidmenninger.isg-research.net/topic/informatica">Informatica <span class="filter-link-count" dir="ltr">(5)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.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://davidmenninger.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://davidmenninger.isg-research.net/topic/information-applications">Information Applications <span class="filter-link-count" dir="ltr">(15)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/information-builders">Information Builders <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.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://davidmenninger.isg-research.net/topic/information-management">Information Management <span class="filter-link-count" dir="ltr">(89)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/information-management-im">Information Management (IM) <span class="filter-link-count" dir="ltr">(7)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/information-optimization">Information Optimization <span class="filter-link-count" dir="ltr">(7)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/information-technology">Information Technology <span class="filter-link-count" dir="ltr">(11)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/infosphere">InfoSphere <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/internet-of-things">Internet of Things <span class="filter-link-count" dir="ltr">(26)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/iot">IOT <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/it-analytics">IT Analytics <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/it-performance-management-itpm">IT Performance Management (ITPM) <span class="filter-link-count" dir="ltr">(23)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/it-service-management">IT Service Management <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/itil">ITIL <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/itsm">ITSM <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/kalido">Kalido <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/karmasphere">Karmasphere <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/kognitio">Kognitio <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/kxen">KXEN <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/location-intelligence">Location Intelligence <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/looker">looker <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/machine-data">Machine data <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/machine-learning">Machine Learning <span class="filter-link-count" dir="ltr">(11)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/machine-learning-and-cognitive-computing">Machine Learning and Cognitive Computing <span class="filter-link-count" dir="ltr">(7)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/machine-learning-digital-technology">Machine Learning Digital Technology <span class="filter-link-count" dir="ltr">(5)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/machine-learning-operations">Machine Learning Operations <span class="filter-link-count" dir="ltr">(6)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/mapr">MapR <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/mapreduce">MapReduce <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/marketing">Marketing <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/master-data-management">Master Data Management <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/mdx">MDX <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/microsoft">Microsoft <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.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://davidmenninger.isg-research.net/topic/microstrategy">MicroStrategy <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/mlops">MLOps <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/mobile">Mobile <span class="filter-link-count" dir="ltr">(10)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.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://davidmenninger.isg-research.net/topic/mobile-business-intelligence">Mobile Business Intelligence <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/mobile-computing">mobile computing <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/mobile-intelligence">Mobile Intelligence <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/mobile-technology">Mobile Technology <span class="filter-link-count" dir="ltr">(18)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/mobility">Mobility <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/model-building-and-large-language-models">Model Building and Large Language Models <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/mpp">MPP <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/natural-language">Natural Language <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/natural-language-processing">natural language processing <span class="filter-link-count" dir="ltr">(46)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/natural-language-query">Natural Language Query <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/netapp">NetApp <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/netezza">Netezza <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/nlg">NLG <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/nlp">NLP <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/nosql">NoSQL <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/odpi">ODPI <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/office-of-finance">Office of Finance <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/olap">OLAP <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/operational-data-plaftforms">operational data plaftforms <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/operational-intelligence">Operational Intelligence <span class="filter-link-count" dir="ltr">(22)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/operations-supply-chain">Operations & Supply Chain 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href="https://davidmenninger.isg-research.net/topic/planning">Planning <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/predictive-analytics">Predictive Analytics <span class="filter-link-count" dir="ltr">(26)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/predictive-planning">Predictive Planning <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/predixion">Predixion <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/process-mining">Process Mining <span class="filter-link-count" dir="ltr">(5)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/qlik">Qlik <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.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://davidmenninger.isg-research.net/topic/qlik-sense">Qlik Sense <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/qlikview">QlikView <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/r">R <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/rd">R&D <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/rdbms">RDBMS <span class="filter-link-count" dir="ltr">(5)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.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://davidmenninger.isg-research.net/topic/retail-analytics">Retail Analytics <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/revenue-management">revenue management <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.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://davidmenninger.isg-research.net/topic/robotic-automation">robotic automation <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/saas">SaaS <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/sales">Sales <span class="filter-link-count" dir="ltr">(6)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/sales-engagement">sales engagement <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/sales-performance">Sales Performance <span class="filter-link-count" dir="ltr">(35)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/sales-performance-management">Sales Performance Management <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/salesforce-com">Salesforce.com <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/sap">SAP <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/sas">SAS <span class="filter-link-count" dir="ltr">(6)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.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://davidmenninger.isg-research.net/topic/soa">SOA <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/social-media">Social Media <span class="filter-link-count" dir="ltr">(38)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/software">Software <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/software-as-a-service">Software as a Service <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/software-evaluation">software evaluation <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/splunk">Splunk <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/spotfire">Spotfire <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/strata-data-conference">Strata Data Conference <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/stratahadoop">Strata+Hadoop <span 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"@context": "http://schema.org", "@type": "BlogPosting", "headline": "Big Data Grows Up at Strata+Hadoop World 2016", "mainEntityOfPage": "https://davidmenninger.isg-research.net/david-menninger/2016/10/25/big-data-grows-up-at-stratahadoop-world-2016/", "datePublished": "25/10/2016", "dateModified": "25/10/2016", "articleBody": "I recently spent time at Strata+Hadoop World 2016 in New York. I attended this event and its predecessor, Hadoop World, off and on for the past six years. This one in New York had a different feel from previous events including the most recent event in San Jose at the end of March. Perhaps because of its location in one of the financial and commercial hubs of the world, the event had much more of a business orientation. But it’s not just location. Past events have been held in New York also, and I see the business focus as a sign of the Hadoop market maturing. Our research shows that big data can have significant business benefits. In our Big Data and Analytics benchmark research, more than three-quarters (78%) of participants indicated that predictive analytics is the most important area of big data analytics for their organization. In our Predictive Analytics research almost three out of five (57%) organizations said they have achieved a competitive advantage through their application of advanced analytics. Thus we are moving beyond the early adopter phase of the technology adoption life cycle into the early majority. More and more organizations recognize that big data and advanced analytics can provide a competitive advantage. As a result, we see more focus on the business value of it, not just the technology required to pursue this advantage. At the Strata+Hadoop World keynote presentations many vendors chose to bring their customers on stage or share stories about how their customers are positively impacting their organizations with big data technology. There were also plenty of technical training sessions, including two full days of training prior to the keynotes and expo, but the main stage of the event was focused on what you can do with big data rather than how to do it. The attendees also seemed to bring a business focus to the event. I spoke with multiple vendors in the expo hall who had attended both the Strata+Hadoop event in San Jose earlier this year and the New York event. They all described customer interactions that had more of a business focus than at previous events. People came looking for ways to apply big data technology to real business needs. This is not say there wasn’t plenty of technology at the event including in particular data science, streaming data and data preparation and governance. Tutorials were offered on a variety of data science topics including how to implement machine learning in programming languages such as Python and Spark. Our research shows that Python is one of the most popular languages for data science analyses, in use by more than one-third (36%) of organizations. As I have written previously, Spark is growing in popularity as a way of providing big data, machine learning and real-time capabilities. At least half a dozen vendors ranging from large to small participated in the expo, touting their data science capabilities, and many other vendors’ marketing materials described how they support data science, for instance with data preparation tools that enable the data science process. Processing streaming data in real time was also a frequent theme. Part of what makes big data big is that it is being generated constantly. It follows that you can probably get value out of analyzing that data in real time as it is being generated. In our research real-time analytics is the second-most frequently cited (by 54%) area of big data analytics, after predictive analytics. In its original incarnation, Hadoop was designed as a batch-oriented system, but as it has grown in popularity, much attention has been given to adding real-time capabilities to the Hadoop ecosystem, which I have described. The themes of data preparation and governance come as no surprise. Our Big Data Integration benchmark research shows that reviewing data for quality and consistency issues (52%) and preparing data (46%) are cited as the two most time-consuming aspects of the big data integration process. Similarly our big data analytics research shows that data quality and information management is the second-most common barrier to big data analytics, cited by 39 percent of organizations. Vendors and the big data community are on the right track in addressing these issues. The big data community continues to evolve, and the Strata+Hadoop World events are helping to foster dialog, education and growth. I’d say that this most recent event is evidence that the big data community is “growing up,” meaning that the focus has shifted to delivering business value. Strata+Hadoop World is a place where you can learn not only about the technology of big data but also how to solve business problems. Regards, David Menninger SVP & Research Director Follow Me on Twitter @dmenningerVR and Connect with me on LinkedIn.", "image": { "@type": "ImageObject", "url": "http://research.ventanaresearch.com/hubfs/Imported_Blog_Media/vr_Big_Data_Analytics_19_important_areas_of_big_data_analytics_wide_updated.jpg", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "David Menninger", "url": "https://davidmenninger.isg-research.net/author/david-menninger", "sameAs": "http://www.linkedin.com/in/davidmenninger" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "davidmenninger.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 Lakes: Safe Way to Swim in Big Data?", "mainEntityOfPage": "https://davidmenninger.isg-research.net/david-menninger/2016/05/11/data-lakes-safe-way-to-swim-in-big-data", "datePublished": "11/05/2016", "dateModified": "11/05/2016", "articleBody": "It has been more than five years since James Dixon of Pentaho coined the term “data lake.” His original post suggests, “If you think of a data mart as a store of bottled water – cleansed and packaged and structured for easy consumption – the data lake is a large body of water in a more natural state.” The analogy is a simple one, but in my experience talking with many end users there is still mystery surrounding the concept. In this post I’d like to clarify what a data lake is, review the reasons an organization might consider using one and the challenges they present, and outline some developments in software tools that support data lakes. Data lakes offer a way to deal with big data. A data lake combines massive storage capabilities for any type of data in any format as well as processing power to transform and analyze the data. Often data lakes are implemented using Hadoop technology. Raw, detailed data from various sources is loaded into a single consolidated repository to enable analyses that look across any data available to the user. To understand why data lakes have become popular it’s helpful to contrast this approach with the enterprise data warehouse (EDW). In some ways an EDW is similar to a data lake. Both act as a centralized repository for information from across an organization. However, the data loaded into an EDW is generally summarized, structured data. EDWs are typically based on relational database technologies, which are designed to deal with structured information. And while advances have been made in the scalability of relational databases, they are generally not as scalable as Hadoop. Because these technologies are not as scalable, it is not practical to store all the raw data that come in to the organization. Hence there is a need for summarization. In contrast, a data lake contains the most granular data generated across the organization. The data may be structured information, such as sales transaction data, or unstructured information, such as email exchanged in customer service interactions. Hadoop is often used with data lakes because it can store and manage large volumes of both structured and unstructured data for subsequent analytic processing. The advent of Hadoop made it feasible and more affordable to store much larger volumes of information, and organizations began collecting and storing the raw detail from various systems throughout the organization. Hadoop has also become a repository for unstructured information such as social media and semistructured data such as log files. In fact, our benchmark research shows that social media data is the second-most important source of external information used in big data analytics. In addition to handling larger volumes and more varieties of information, data lakes enable faster access to information as it is generated. Since data is gathered in its raw form, no preprocessing is needed. Therefore, information can be added to the data lake as soon as it is generated and collected. This approach has caused some controversy with many industry analysts and even vendors to raise concerns about data lakes turning into data swamps. In general, the concerns about data lakes becoming data swamps center around the lack of governance of the data in a data lake, an appropriate topic here. These collections of data should be governed like any other set of information assets within an organization. The challenge was that most of the governance tools and technologies had been developed for relational databases and EDWs. In essence, the big data technologies used for data lakes had gotten ahead of themselves, without incorporating all the features needed to support enterprise deployments. Another, perhaps more minor controversy centers around terminology. I raise this issue so that, regardless of the terminology a vendor chooses, you can recognize data lakes and be aware of the challenges. Cloudera uses the term Enterprise Data Hub to represent essentially the same concept as a data lake. Hortonworks embraces the data lake terminology as evidenced in this post. IBM acknowledges the value of data lakes as well as its challenges in this post, but Jim Kobielus, IBM’s Big Data Evangelist, questioned the terminology in a more recent post on LinkedIn, and the term “data lake” is not featured prominently on IBM’s website. Despite the controversy and challenges, data lakes are continuing to grow in popularity. They provide important capabilities for data science. First, they contain the detailed data necessary to perform predictive analytics. Second, they allow efficient access to unstructured data such as social media or other text from customer interactions. For business this information can create a more complete profile of customers and their behavior. Data lakes also make data available sooner than it might be available in a conventional EDW architecture. Our data and analytics in the cloud benchmark research shows that one in five (21%) organizations are integrating their data in real time. The research also shows that those who integrate their data more often are more satisfied and more confident in their results. Granted, a data lake contains raw information, and it may require more analysis or manipulation since the data is not yet cleansed, but time is money and faster access can often lead to new revenue opportunities. Half the participants in our predictive analytics benchmark research said they have created new revenue opportunities with their analytics. Cognizant of the lack of governance and management tools some organizations hesitated to adopt data lakes, while others went ahead. Vendors in this space have advanced their capabilities in the meantime. Some, such as Informatica, are bringing data governance capabilities from the EDW world to data lakes. I wrote about the most recent release of Informatica’s big data capabilities, which it calls Intelligent Data Lake. Other vendors are bringing their EDW capabilities to data lakes as well. Information Builders and Teradata both made data lake announcements this spring. In addition, a new category of vendors is emerging focused specifically on data lakes. Podium Data says it provides an “enterprise data lake management platform,” Zaloni calls itself “the data lake company,” and Waterline Data draws its name “from the metaphor of a data lake where the data is hidden below the waterline.” Is it safe to jump in? Well, just like you shouldn’t jump into a lake without knowing how to swim, you shouldn’t jump into a data lake without plans for managing and governing the information in it. Data lakes can provide unique opportunities to take advantage of big data and create new revenue opportunities. With the right tools and training, it might be worth testing the water. Regards, David Menninger SVP & Research Director", "image": { "@type": "ImageObject", "url": "http://cdn2.hubspot.net/hubfs/2035844/Imported_Blog_Media/vr_big_data_analytics_21_external_data_sources_for_big_data_analytics.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "David Menninger", "url": "https://davidmenninger.isg-research.net/author/david-menninger", "sameAs": "http://www.linkedin.com/in/davidmenninger" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "davidmenninger.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": "SAS Analytics Juggernaut Keeps on Truckin’", "mainEntityOfPage": "https://davidmenninger.isg-research.net/david-menninger/2016/03/11/sas-analytics-juggernaut-keeps-on-truckin", "datePublished": "11/03/2016", "dateModified": "11/03/2016", "articleBody": "I recently attended the SAS Analyst Summit in Steamboat Springs, Colo. (Twitter Hashtag #SASSB) The event offers an occasion for the company to discuss its direction and to assess its strengths and potential weaknesses. SAS is privately held, so customers and prospects cannot subject its performance to the same level of scrutiny as public companies, and thus events like this one provide a valuable source of additional information. SAS has been a dominant player in the analytics marketplace for years, celebrating its 40th anniversary this year and reporting US$3.16 billion in 2015 revenue. Both of these are significant accomplishments in the software market. And while validation of vendors – including the company’s viability – often ranks as the least important of the seven product evaluation criteria in our benchmark research, SAS’s ranks as one of the most commonly used tools for predictive analytics: One-third (33%) of participants in our predictive analytics benchmark research use it. The company provides a very broad technology stack including information management, business intelligence, and many types of analytics and visualization. In addition, SAS offers domain-specific applications built on top of these capabilities. A quick count on the products and solutions page on its website shows hundreds of entries, and SAS executives at the event asserted that the company will generate more than 100 releases this year. So one challenge for customers, as with any other large vendor, is navigating through this maze to find something that suits their needs. Also a vendor with a large portfolio is generally not as nimble as one with fewer products. On the other hand a large vendor can more easily manage the interdependencies between products for its customers. That is, if an organization licenses a variety of products from multiple vendors it often falls to the buyer to keep different versions from different vendors in synch. At this year’s event, big data was much less prominent on the agenda than in the past. Over the last several years SAS has made significant investments in big data, supporting Hadoop and in-memory processing to create a scalable, high-performance infrastructure. This year it focused on end-user tools and prebuilt applications for working with data and analytics. In particular, SAS presenters identified three areas of focus in its technology investments: analytics, data management and visualization. My colleague Mark Smith has written about the importance of visual discovery. SAS delivers visualization capabilities through its Visual Analytics product line, in which it continues to invest. A speaker claimed that more than 14,000 servers were licensed to run Visual Analytics as of 2015, up from 8,400 in 2014. SAS will be combining the capabilities of Visual Explorer and Visual Designer to create a more unified user experience. The company also is introducing visual data preparation features that enable users to explore and profile data as part of the data integration and transformation process. No analysis should overlook SAS’s core competency of analytics. I was impressed with its demonstrations of automated evaluation and selection of different predictive analytics algorithms as part of its visualization capabilities. Visual Analytics offers a modern, intuitive user interface for analyzing big data set, but it has only limited collaboration capabilities. Our big data analytics benchmark research finds more than three in four respondents (78%) consider collaboration as important or very important. SAS will need to make further investments to support collaboration. SAS continues to advance another product, Visual Investigator, which it plans to bring to market more broadly in 2016. Announced last year as part of the Security Intelligence products, it is targeted at what SAS calls the “intelligence analyst,” a role between business analyst and data scientist. This tool has great potential as a standalone product as it combines critical components of tasks, activities, cases, and taking action that have broad appeal across all analytic roles and beyond fraud and security investigations. At the event, several analysts questioned SAS executives about the impact of open source systems on the company. This was a hot topic generating much discussion. Executives acknowledged that the company has lost ground to open source systems in the educational market and in response has reinvigorated its efforts there. The SAS University Edition is available for download and on Amazon Web Services. It is also available in a cloud-based offering called SAS On Demand for Academics. Dozens of universities that offer masters of analytics programs are working with SAS to help address the shortage of skilled analytics resources. Our Next Generation Predictive Analytics research shows that lack of skills training and knowledge of the mathematics involved in predictive analytics are significant obstacles to users producing their own analyses, cited by 79% and 66% of participants respectively. It is wise for SAS to invest in supporting these programs. If your organization wants to hire or train additional resources, these programs may be a valuable resource. On the technology front, executives pointed out that SAS has embraced open source systems, with support for Linux, Hadoop and the Python and Lua programming languages. However, it does not plan to support R, which is used by more than half (58%) of participants in our predictive analytics research . The company also plans to introduce a set of open application programming interfaces (APIs) to encourage more developers to work with SAS and create a marketplace of third-party products. The key will be whether the collection of these efforts makes a significant impact on the developer community, which is where open source tools often gain their foothold in organizations. At a minimum I expect SAS will need to offer a “freemium” version of products if it really wants to win over the development community. This event provides a valuable window into SAS’s performance and strategy. The company has proven its staying power and ability to be a relatively fast follower of industry trends to remain competitive in the constantly evolving business intelligence and analytics landscape. For organizations that consider expanding their use of analytics, I recommend placing SAS on the list of vendors they evaluate. Regards, David Menninger SVP & Research Director", "image": { "@type": "ImageObject", "url": "http://cdn2.hubspot.net/hubfs/2035844/Imported_Blog_Media/vr_ng_predictive_analytics_16_why_users_dont_produce_predictive_analyses-1.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "David Menninger", "url": "https://davidmenninger.isg-research.net/author/david-menninger", "sameAs": "http://www.linkedin.com/in/davidmenninger" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "davidmenninger.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": "Pardon the Interruption: Industry Veteran Returns to Ventana Research", "mainEntityOfPage": "https://davidmenninger.isg-research.net/david-menninger/2016/02/08/pardon-the-interruption-industry-veteran-returns-to-ventana-research", "datePublished": "07/02/2016", "dateModified": "07/02/2016", "articleBody": "Some followers of Ventana Research may recall my work here several years ago. Here and elsewhere I have spent most of my career in the data and analytics markets matching user requirements with technologies to meet those needs. I’m happy to be returning to Ventana Research to resume investigating ways in which organizations can make the most of their data to improve their business processes; for a first look, please see our 2016 research agenda on Big Data and Information Optimization. I relish the opportunity to conduct primary market research in the form of Ventana’s well-known benchmark research and to help end users and vendors apply the information collected in those studies. Much has happened since I was previously part of the Ventana Research team. One major change is the explosive growth in the use and acceptance of big data. For example, when I conducted the first benchmark research on big data, only 22 percent of participants were using Hadoop in production. Our more recent research shows more than 50 percent growth in the number of respondents using Hadoop in production (which is now 37%). In the area of predictive analytics, another research study from my prior tenure identified a skills shortage. This shortage was identified in several ways. More than four out of five (83%) participants indicated that users did not have enough skills training, and more than half (58%) said they didn’t understand the mathematics needed to produce their own analyses. In the interim the numerous university programs have begun to help address this shortage. A Google search shows that New York University, Columbia, Indiana University, Wesleyan, University of Washington, University of Michigan, University of Rochester and University of Texas San Antonio have created data science programs – and this is just page one of the search results. I anticipate that these will be increasingly popular programs as the rise of big data will continue to drive demand for these skills. For the time being, our more recent study on predictive analytics suggests that these skill shortages still exist with very similar responses of 79 percent and 66 percent, respectively. I’ll be continuing to watch these and other analytics issues noted in our 2016 Business Analytics Research Agenda. We have also seen a sea change in the acceptance of open source software in enterprises. I think it is fair to say that open source helped drive the growth in big data with various projects including Cassandra, Hadoop, MongoDB and Spark enabling organizations to experiment with large volumes of data before making significant license purchases to put those systems into production. The open source momentum is further evidenced by some large vendors taking formerly proprietary, “closed source” technologies and making them open source. Perhaps the biggest example is Microsoft making its .NET technology open source. My former employer Pivotal also converted its data management products, in which it had invested more than 10 years of proprietary development, to open source versions. Another notable change is the growth of interest in the Internet of Things (IoT). Many years ago I considered a position with a vendor that helped organizations manage RFID data. Adoption was slow at the time, in part because of the cost of RFID tags but also because of the cost and challenges of collecting and analyzing very large volumes of data. As big data technologies have grown, so too has interest in IoT. Technologies exist today to make processing such large amounts of data possible in the time frames and at costs that make it practical to consider how instrumentation of devices can be used to enhance business performance. We’ll be undertaking specific research on this topic in 2016: See our Big Data and Information Optimization Research Agenda. If he were alive today, Charles Darwin might have noted the emergence of a new species: the Unicorn, which Wikipedia defines as a startup company, often software-based, whose valuation exceeds US$1 billion. You might wonder how this financial trend impacts our research and the advice we provide. The answer is that such valuations have the potential to alter the behavior of the markets we cover. They provide these startup vendors access to funding great enough to change the competitive landscape. Such investments can put pressure on existing vendors to step up their game. In some cases it can also cause consolidation in the market or even cause certain vendors to exit markets, such as Intel did when it decided to get out of the Hadoop distribution market. At Ventana Research we are ready to help end-user organizations evaluate whether the unicorns are ready for prime time and how their existence might impact their existing software vendors. One way in which we help in this process is with our Ventana Research Value Indexes, which provide fact-based assessments of various software products within a variety of market segments. So I hope you’ll pardon the interruption in our conversation. It’s good to be back, and I am looking forward to working with the entire Ventana Research team to provide research and insights that will help guide your use of technology to improve your business decisions and processes. Regards, David Menninger SVP & Research Director", "image": { "@type": "ImageObject", "url": "http://cdn2.hubspot.net/hubfs/2035844/Imported_Blog_Media/vr_big_data_analytics_18_big_data_technology_in_use.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "David Menninger", "url": "https://davidmenninger.isg-research.net/author/david-menninger", "sameAs": "http://www.linkedin.com/in/davidmenninger" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "davidmenninger.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": "Research Agenda: Using Business Analytics to Make the Most of Data in 2016", "mainEntityOfPage": "https://davidmenninger.isg-research.net/david-menninger/2016/02/08/research-agenda-using-business-analytics-to-make-the-most-of-data-in-2016", "datePublished": "07/02/2016", "dateModified": "07/02/2016", "articleBody": "Throughout the course of our research in 2016, we’ll be exploring ways in which organizations can maximize the value of their data. Ventana Research believes that analytics is the engine and data is the fuel to power better business decisions. Several themes emerged from our benchmark research on incorporating data and analytics into organizational processes, and we will follow them in our 2016 Business Analytics Research Agenda: Analytics enabling continuous optimization Streamlining analytics with data and information technology Digital technologies transforming analytics for business. Organizations generate data continuously, and they should analyze and refine it continuously – that is, optimize it – to improve their actions, decisions and processes. Our research shows predictive analytics to be a key tool for such optimization and indicates the convergence of analytics with applications to accomplish these goals. Nearly half (49%) of participants in our next-generation predictive analytics benchmark research said they prefer to deploy their predictive analytics within business applications to enable real-time execution. Finding that more than nine out of 10 (92%) participants plan to deploy more predictive analytics, we will monitor the role of data science and analytics teams in these activities. We will also follow how the exploration of big data sets intersects with the discovery portion of analytic processes. Organizations need to use data and information technology better to streamline analytics. Our research suggests that they will need to invest in data preparation for analytics, as three out of five (62%) participants identified accessing and preparing data as a challenge in their predictive analytics process. The same percentage (62%) cited accessing and integrating data as a main reason for dissatisfaction with their analytic processes. Organizations can address some of these challenges in gaining access to information by virtualizing data. We also will follow the progress of organizations in using cloud-based technologies to help streamline their analytics; more than three-fourths (76%) of participants in our data and analytics in the cloud benchmark research said that accessing data from cloud-based systems is important. We will continue to investigate and report on these topics in the course of planned benchmark research on data preparation. Other digital technologies will impact analytics for business. More than four-fifths (82%) of organizations in our data and analytics in the cloud research can access and review data and analytics on mobile devices. Simplification of mobile technologies can speed access to an organization’s information, yet access alone is not sufficient. We will track developments in collaboration across the organization to enable action on analytics. The Internet of Things (IoT) – the network of devices, vehicles, buildings and other items that are embedded with electronics, software, sensors and connectivity – brings data and in-motion challenges due to the combination of large volumes of information and requirements for low latency in processing. We will be studying these topics in our Internet of Things and Operational Intelligence benchmark research. Finally, we will follow the evolution of natural-language processing and cognitive systems as methods for searching and presenting information to ordinary business users. We’ll be studying these issues and more throughout the year. Please download and review our full Business Analytics agenda. I invite you to participate in this research as it is conducted during the year. I look forward to sharing the insights it provides and to helping your organization apply those insights to its business needs. Regards, David Menninger SVP & Research Director", "image": { "@type": "ImageObject", "url": "http://cdn2.hubspot.net/hubfs/2035844/Imported_Blog_Media/vr_ng_predictive_analytics_08_time_spent_in_predictive_analytic_process.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "David Menninger", "url": "https://davidmenninger.isg-research.net/author/david-menninger", "sameAs": "http://www.linkedin.com/in/davidmenninger" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "davidmenninger.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": "Research Uncovers Keys to Using Predictive Analytics", "mainEntityOfPage": "https://davidmenninger.isg-research.net/david-menninger/2012/03/27/research-uncovers-keys-to-using-predictive-analytics", "datePublished": "27/03/2012", "dateModified": "27/03/2012", "articleBody": "As a technology, predictive analytics has existed for years, but adoption has not been widespread among businesses. In our recent benchmark research on business analytics among more than 2,600 organizations, predictive analytics ranked only 10th among technologies they use to generate analytics, and only one in eight of those companies use it. Predictive analytics has been costly to acquire, and while enterprises in a few vertical industries and specific lines of business have been willing to invest large sums in it, they constitute only a fraction of the organizations that could benefit from them. Ventana Research has just completed a benchmark research project to learn about how the organizations that have adopted predictive analytics are using it and to acquire real-world information about their levels of maturity, trends and best practices. In this post I want to share some of the key findings from our research. As I have noted, varieties of predictive analytics are on the rise. The huge volumes of data that organizations accumulate are driving some of this interest. Our Hadoop research highlights the intersection of this big data and predictive analytics: More than two-thirds (69%) of Hadoop users perform advanced analytics such as data mining. Regardless of the reasons for the rise, our new research confirms the importance of predictive analytics. Participants overwhelmingly reported that these capabilities are important or very important to their organization (86%) and that they plan to deploy more predictive analytics (94%). One reason for the importance assigned to predictive analytics is that most organizations apply it to core functions that produce revenue. Marketing and sales are the most common of those. The top five sources of data tapped for predictive analytics also relate directly to revenue: customer, marketing, product, sales and financial. Although participants are using predictive analytics for important purposes and are generally positive about the experience, they do not minimize its complexities. While now usable by more types of people, this technology still requires special skills to design and deploy, and in half of organizations the users of it don’t have them. Having worked for two different vendors in the predictive analytics space, I personally can testify that the mathematics of it requires special training. Our research bears this out. For example, 58 percent don’t understand the mathematics required. Although not a math major, I had always been analytically oriented, but to get involved in predictive analytics I had to learn new concepts or new ways to apply concepts I knew. Organizations can overcome these issues with training and support. Unfortunately, most are not doing an adequate job in these areas. Not half (44%) said their training in predictive analytics concepts and techniques is adequate, and fewer than one-fourth (24%) provide adequate help desk resources. These are important places to invest because organizations that do an adequate job in these two areas have the highest levels of satisfaction with their use of predictive analytics; 89% of them are satisfied vs. 66% overall. But we note that product training is not the most important type. That also correlated to higher levels of satisfaction, but training in concepts and the application of those concepts to business problems showed stronger correlation. Timeliness of results also has an impact on satisfaction. Organizations that use real-time scoring of records occasionally or regularly are more satisfied than those that use real-time scoring infrequently or not at all. Our research also shows that organizations need to update their models more frequently. Almost four in 10 update their models quarterly or less frequently, and they are less satisfied with their predictive analytics projects than those who update more frequently. In some ways model updates represent the “last mile” of the predictive analytics process. To be fully effective, organizations need to build predictive analytics into ongoing business processes so the results can be used in real time. Using models that aren’t up to date undermines the whole effort. Thanks to our sponsors, IBM and Alpine Data Labs, for helping to make this research available. And thanks to our media sponsors, Information Management, KD Nuggets and TechTarget, for helping in gaining participants and promoting the research and educating the market. I encourage you to explore these results in more detail to help ensure your organization maximizes the value of its predictive analytics efforts. Regards, David Menninger – VP & Research Director", "image": { "@type": "ImageObject", "url": "", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "David Menninger", "url": "https://davidmenninger.isg-research.net/author/david-menninger", "sameAs": "http://www.linkedin.com/in/davidmenninger" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "davidmenninger.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": "SAS Advances Big Data and Cloud Computing", "mainEntityOfPage": "https://davidmenninger.isg-research.net/david-menninger/2012/03/26/sas-advances-big-data-and-cloud-computing", "datePublished": "26/03/2012", "dateModified": "26/03/2012", "articleBody": "I want to share my observations from the recent annual SAS analyst briefing. SAS is a huge software company with a unique culture and a history of success. Being privately held SAS is not required to make the same financial disclosures as publicly held organizations, it released enough information to suggest another successful year, with more than $2.7 billion in revenue and 10 percent growth in its core analytics and data management businesses. Smaller segments showed even higher growth rates. With only selective information disclosed, it’s hard to dissect the numbers to spot specific areas of weakness, but the top-line figures suggest SAS is in good health. One of the impressive figures SAS chooses to disclose is its investment in research and development, which at 24 percent of total revenue is a significant amount. Based on presentations at the analyst event, it appears a large amount of this investment is being directed toward big data and cloud computing. At last year’s event SAS unveiled plans for big data, and much of the focus at this year’s event was on the company’s current capabilities, which consist of high-performance computing and analytics. SAS has three ways of processing and managing large amounts of data. Its high-performance computing (HPC) capabilities are effectively a massively parallel processing (MPP) database, albeit with rich analytic functionality. The main benefit of HPC is scalability; it allows processing of large data sets. A variation on the HPC configuration includes pushing down operations into third-party databases for “in-database” analytics. Currently, in-database capabilities are available from Teradata and EMC Greenplum, plus SAS has announced plans to integrate with other database technologies. The main benefit of in-database processing is that it minimizes the need to move data out of the database and into the SAS application, which saves time and effort. More recently, SAS introduced a third alternative that it calls High Performance Analytics (HPA), which provides in-memory processing and can be used with either configuration. The main benefit of in-memory processing is enhanced performance. These different configurations each have advantages and disadvantages, but having multiple alternatives can create confusion about which one to use. As a general rule of thumb, if your analysis involves a relatively small amount of data, perhaps as much as a couple of gigabytes, you can run HPA on a single node. If your system involves larger amounts of data coming from a database on multiple nodes, you will want to install HPA on each node to be able to process the information more quickly and handle internode transfers of data. SAS also has the ability to work with data in Hadoop. Users can access information in Hadoop via Hive to interact with tables as if they were native SAS data sets. The analytic processing is done in SAS, but this approach eliminates the need to extract the data from Hadoop and put it into SAS. Users can also invoke MapReduce jobs in Hadoop from the SAS environment. To be clear, SAS does not automatically generate these jobs or assist users in creating them, but this does offer a way for SAS users to create a single process that mixes SAS and Hadoop processing. I’d like to see SAS push down more processing into the Hadoop environment and make more of the Hadoop processing automatic. SAS plans to introduce a new capability later in the year called LASR Analytic Server that is supposed to deliver better integration with Hadoop as well as better integration with the other distributed databases SAS supports, such as EMC Greenplum and Teradata. There were some other items to note at the event. One is a new product for end-user interactive data visualization called Visual Analytics Explorer, which is scheduled to be introduced during the first quarter of this year. For years SAS was known for having powerful analytics but lackluster user interfaces, so this came as a bit of a surprise, but the initial impression shared by many in attendance was that SAS has done a good job on the design of the user interface for this product. In the analytics software market, many vendors have introduced products recently that provide interactive visualization. Companies such as QlikView, Tableau and Tibco Spotfire have built their businesses around interactive visualization and data exploration. Within the last year, IBM introduced Cognos Insight, MicroStrategy introduced Visual Insight, and Oracle introduced visualization capabilities in its Exalytics appliance. SAS customers will soon have the option of using an integrated product rather than a third-party product for these visualization capabilities. Based on SAS CTO Keith Collins’s presentation I expect to see SAS making a big investment in SaaS (software as a service, pun intended) and other cloud offerings, including platform as a service and infrastructure as a service. Collins outlined the company’s OnCloud initiative, which begins with offering some applications on demand and will roll out additional capabilities over the next two years. SAS plans full cloud support for its products, including a self-service subscription portal, developer capabilities and a marketplace for SAS and third-party cloud-based applications and also plans to support public cloud, private cloud and hybrid configurations. Since SAS already offers its products on a subscription basis, the transition to a SaaS offering should be relatively easy from a financial perspective. This move is consistent with the market trends identified in our Business Data in the Cloud benchmark research. We also see other business intelligence vendors such as MicroStrategy and information management vendors such as Informatica adopting similarly broad commitments to cloud-based versions of their products. Overall, SAS continues to execute well. Its customers should welcome these new developments, particularly the interactive visualization. The big-data strategy is still too SAS-centric, focused primarily on extracting information from Hadoop and other databases. I expect that the upcoming LASR Analytics Server will leverage these underlying MPP environments better. The cloud offerings will make it easier for new customers to evaluate the SAS products. I recommend you keep an eye on these developments at they come to market. Regards, David Menninger – VP & Research Director", "image": { "@type": "ImageObject", "url": "", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "David Menninger", "url": "https://davidmenninger.isg-research.net/author/david-menninger", "sameAs": "http://www.linkedin.com/in/davidmenninger" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "davidmenninger.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 Intelligence Revolution and Research Agenda for 2012", "mainEntityOfPage": "https://davidmenninger.isg-research.net/david-menninger/2012/03/15/business-intelligence-revolution-and-research-agenda-for-2012", "datePublished": "15/03/2012", "dateModified": "15/03/2012", "articleBody": "For most people involved with business intelligence (BI), these are exciting times. Using BI to improve business processes continues to motivate organizations to invest in BI. The focus on BI also empowers business analytics and can be rented in the cloud computing model of accessing software. New technologies are adding dimensions to BI and creating both excitement and confusion for enterprises implementing them. We offer a variety of accomplished research that can help organizations overcome the hype and understand how to use these technologies to improve business decision-making, and we’re planning new research in 2012 on these topics. Big data continues to be one of hottest topics in the market. Every vendor wants to claim it has a solution for managing it. Multiple approaches exist to tackle the proliferation of huge volumes of data at accelerated paces, which we addressed recently in our Big Data benchmark research. We also see the pace of business forcing organizations to analyze data more frequently; one-fourth of research participants now analyze data hourly or even more often. In 2012 we will continue our research by exploring specific vendor offerings in big-data analytics. We’ll also be conducting new benchmark research into operational intelligence to explore how streaming data and event-based data impact organizations. The march to add mobile capabilities across smart-phones and tablets to business intelligence is becoming a stampede. Nearly every vendor I cover has added such capabilities or significantly enhanced those it already has. However, questions remain on the best ways to utilize mobile capabilities and which parts of the organization really need them. Our upcoming Next-Generation BI benchmark research will look at how businesses are utilizing mobile BI or intend to. In addition, it will examine how collaborative technologies are influencing BI processes and organizational decision-making. We’ve already seen the consumerization of collaborative BI. This research will explore how well businesses understand the intersection of collaborative technologies and social media with BI. Vendors such as QlikTech, Tibco Spotfire and Yellowfin recently enhanced their products with collaborative capabilities joining others like IBM and SAP who have advanced in collaboration capabilities with BI. As you consider your organization’s requirements, the best practices identified in this research will help you plan to incorporate these capabilities into your business processes. We also continue to explore the role of analytics, closely associated with BI. Next week we will share the results of our Predictive Analytics benchmark research Our published business analytics research shows only one-quarter of organizations using planning and forecasting and only 13 percent using predictive analytics. Yet nearly 80 percent said both of these capabilities are important or very important. Organizations need to address that gap to enhance their business decision-making abilities. Because business intelligence also is closely entwined with information management, I encourage you to review our Information Management research agenda as well. Almost one-third of organizations have more than 10 sources of data they have to integrate, and more than two-thirds spend more time preparing data than analyzing it. Without addressing these issues, no organization is likely to realize the full potential of business intelligence. Business intelligence continues to be a strategic business imperative. Our research will deliver education and best practices that can help you reduce the costs, time and risk of making the wrong choices or being uninformed of this strategic imperative. Regards, David Menninger – VP & Research Director", "image": { "@type": "ImageObject", "url": "", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "David Menninger", "url": "https://davidmenninger.isg-research.net/author/david-menninger", "sameAs": "http://www.linkedin.com/in/davidmenninger" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "davidmenninger.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": "Revolution Analytics Hosts Contest on Business Predicting the Future", "mainEntityOfPage": "https://davidmenninger.isg-research.net/david-menninger/2012/02/01/revolution-analytics-hosts-contest-on-business-predicting-the-future", "datePublished": "01/02/2012", "dateModified": "01/02/2012", "articleBody": "Revolution Analytics recently announced the winners of its “Applications of R in Business” contest. Revolution Analytics has built a business around supporting R, an open source statistical software package, and extending it with features it licenses to customers. I served as a judge in the contest. Since I was in the midst of analyzing the data for our predictive analytics benchmark research, I was interested to see how the contestants applied predictive analytics techniques to specific business problems. As I’ve written previously, predictive analytics is on the rise, despite the fact that the term is a bit of a misnomer. What software vendors, industry analysts and others call predictive analytics may be described more accurately as data mining. Data mining includes both predictive and descriptive analytics. Predictive analytics is used to predict future occurrences, such as the likelihood of an individual customer purchasing a particular product. Descriptive analytics is used to classify things (say, customers or products) into groups. These descriptive characteristics can be used in conjunction with predictive analytics to help produce more accurate predictions. For example, single male customers in group A may be more likely to purchase a particular product than single male customers in group B. Nevertheless, the market seems to have adopted “predictive analytics” to encompass the broader category of data mining. I’m comfortable with that use of the term, so here and elsewhere in my research, unless specified otherwise, I consider predictive analytics and data mining to be interchangeable labels. (See: “Technology Terminology: What’s in a Name?”) R, as a statistical package, includes many algorithms for predictive analytics, including regression, clustering, classification, text mining and other techniques. The contest submissions supported a variety of business cases, including, among others, predicting order amounts to optimize manufacturing processes, predicting marketing campaign effectiveness to optimize marketing spending, predicting liquid steel temperatures to optimize steel plant processes and performing sentiment analysis of Twitter data. The entries served to reinforce the notion that using predictive analytics requires specialized skills. Take a look at the entries above and ask how many people in your organization could perform those types of analyses. Despite the requirement for these additional skills, the demand for predictive analytics continues to rise. The entries show one of the reasons for this rise: the value that predictive analytics can provide. Imagine if you could redirect marketing spending from ineffective campaigns to more productive campaigns, or squeeze costs out of your manufacturing processes. The entries also demonstrated a best practice: close alignment between the analyst and the underlying business objectives. Predictive analytics is not magic. It requires an understanding of business processes and an understanding of statistical techniques. The judging criteria reflected this requirement as well. One of the three categories we were asked to score was applicability of the submission to business. I think it’s clear how the analyses in the winning entries could provide significant business value. If you are not yet applying predictive analytics, check out the submissions for examples of where you might apply them to your business; you can skip the implementation details. If you are using predictive analytics and understand the statistics involved, check out the submissions for suggestions of how you might enhance your own applications. In either case, keep an eye out for the results of our predictive analytics benchmark research to learn more about best practices and how others are using predictive analytics. Regards, David Menninger – VP & Research Director", "image": { "@type": "ImageObject", "url": "", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "David Menninger", "url": "https://davidmenninger.isg-research.net/author/david-menninger", "sameAs": "http://www.linkedin.com/in/davidmenninger" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "davidmenninger.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": "Technology Terminology: What’s in a Name?", "mainEntityOfPage": "https://davidmenninger.isg-research.net/david-menninger/2012/02/01/technology-terminology-whats-in-a-name", "datePublished": "01/02/2012", "dateModified": "01/02/2012", "articleBody": "When it comes to technology, debates about whether a particular name suits its category are rampant. Here is a link to one such argument about the term “big data” from Curt Monash, an analyst whom I respect a great deal. This debate rages in the Twittersphere also, as in this comment from Neil Raden, another analyst I respect, suggesting that “big data is a marketing term … imprecise by design.” Another term I’ve encountered resistance to recently is “predictive analytics.” See: (“Revolution Analytics Hosts Contest on Business Predicting the Future“). Having spent a large portion of my career developing and marketing software products, I am probably biased, but I see value in broad, easily understood – even if imprecise – terms. Such terms are inclusive rather than exclusive, and that allows more vendors to participate in the markets, prompting more competition, more debate and ultimately better products and more variety of products. Broad terms such as “big data” and “predictive analytics” are also easily understood, so potential buyers can get an immediate idea of how such categories might fit in their organization. The result is a bigger market for products, which in turn leads to more investment in the category. On the other hand, broad terms do create some confusion for buyers. Worse, some vendors corrupt the meanings of these terms, and too many jump on the bandwagon. So buyers have to do some homework, research the subject and understand what vendors have to offer. Yes, our business as analysts benefits from some of this confusion because we are hired to help with research and education, but organizations making a large technology purchase should be doing this anyway. The alternative – very precise and exclusive terms – might eliminate one type of confusion but would add another: an even greater proliferation of technology categories. A more precise approach would place a burden on buyers. In such a world, they would need to know which specific types of solutions to consider early in the buying cycle, and they might not find other categories that provide similar capabilities. The result would be lower overall adoption rates, smaller markets and less investment to help solve business problems with those technologies. So I like broader terms as the lesser evil. Besides, the market has spoken. While specific terms may be promoted by certain vendors or analysts, traction comes from widespread adoption. So go ahead, debate what these widely adopted terms mean. It will create a better understanding for all. Vendors, find a way to make your products relevant to a particular category. Venture capitalists, invest in a new startup that extends or alters or even replaces a category. In the end, we’ll all have to do a little work, but we’ll have wider selection of better products as a result. Regards, David Menninger – VP & Research Director", "image": { "@type": "ImageObject", "url": "", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "David Menninger", "url": "https://davidmenninger.isg-research.net/author/david-menninger", "sameAs": "http://www.linkedin.com/in/davidmenninger" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "davidmenninger.isg-research.net", "logo": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/ISG_Software_Research_Logo_Color@2x.png" } } }</script>  <script data-hs-allowed="true" src="/_hcms/forms/v2.js"></script> <script data-hs-allowed="true"> var options = { portalId: '2035844', formId: 'fa276fc9-9a16-4673-a644-1fcbedcc3c24', formInstanceId: '5181', pageId: '4221377247', region: 'na1', inlineMessage: "Thanks for subscribing!", rawInlineMessage: "Thanks for subscribing!", hsFormKey: "2ee5447e42c614cb2eb41722cb6c6b53", css: '', target: '#hs_form_target_module_1552944263900179', contentType: "listing-page", formsBaseUrl: '/_hcms/forms/', formData: { cssClass: 'hs-form stacked hs-custom-form' } }; 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