Matt Aslett'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" 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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 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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 minimal-active-item" aria-current="page"><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" 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We offer a portfolio of advisory, research, thought leadership and digital education services to help optimize market strategy, planning and execution.</p> <p><strong><a href="https://www.ventanaresearch.com/services/vendors/analyst_relations" rel="noopener">Analyst Relations</a></strong></p> <p><strong><a href="https://www.ventanaresearch.com/services/vendors/demand_generation" rel="noopener">Demand Generation</a></strong></p> <p><strong><a href="https://www.ventanaresearch.com/services/vendors/product-marketing" rel="noopener">Product Marketing</a></strong></p> <p><strong><a href="https://www.ventanaresearch.com/vendor-coverage" target="_self" role="menuitem">Market Coverage</a></strong></p> <p><strong><a href="https://www.ventanaresearch.com/briefing" target="_self" role="menuitem">Request a Briefing</a></strong><br><br><br></p></div> <span id="hs_cos_wrapper_widget_1687254472480_" 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://mattaslett.isg-research.net/hs-fs/hub/2035844/hub_generated/template_assets/90531223305/1725882478811/Ventana_Research_2023/js/mmenu.min.js"></script> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> <div id="hs_cos_wrapper_Structured_data" class="hs_cos_wrapper hs_cos_wrapper_widget hs_cos_wrapper_type_module" style="" data-hs-cos-general-type="widget" data-hs-cos-type="module"></div> <div class="body-container-wrapper"> <div class="blog-header background-image post_page_2" data-bg-color="secondary" style=" background-size:cover; background-position:50%; background-repeat:no-repeat; background-image:url(https://mattaslett.isg-research.net/hubfs/_images/_backgrounds_expertise/2024_VR_Analytics_and_Data_Expertise_Image_AdobeStock_482832614_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://mattaslett.isg-research.net/hubfs/_images/_team/Aslett.Matt%20Pic.Circle.png" style="padding: 10px; "> <br> <h1 class="analyst-header-title">Matt Aslett'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://mattaslett.isg-research.net/bigeye-enable-monitoring-quality-and-lineage-of-data">Bigeye Enable Monitoring, Quality and Lineage of Data</a></h2> <div class="author-meta"> Posted by <a href="https://mattaslett.isg-research.net/author/matt-aslett"> Matt Aslett </a> on <span class="post-date">20 November 2024 </span> </div>  <hr> </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://mattaslett.isg-research.net/hs-fs/hubfs/Bigeye_Enable_Monitoring_Quality_and_Lineage_of_Data.png?width=200&name=Bigeye_Enable_Monitoring_Quality_and_Lineage_of_Data.png" width="200" !important: srcset="https://mattaslett.isg-research.net/hs-fs/hubfs/Bigeye_Enable_Monitoring_Quality_and_Lineage_of_Data.png?width=100&name=Bigeye_Enable_Monitoring_Quality_and_Lineage_of_Data.png 100w, https://mattaslett.isg-research.net/hs-fs/hubfs/Bigeye_Enable_Monitoring_Quality_and_Lineage_of_Data.png?width=200&name=Bigeye_Enable_Monitoring_Quality_and_Lineage_of_Data.png 200w, https://mattaslett.isg-research.net/hs-fs/hubfs/Bigeye_Enable_Monitoring_Quality_and_Lineage_of_Data.png?width=300&name=Bigeye_Enable_Monitoring_Quality_and_Lineage_of_Data.png 300w, https://mattaslett.isg-research.net/hs-fs/hubfs/Bigeye_Enable_Monitoring_Quality_and_Lineage_of_Data.png?width=400&name=Bigeye_Enable_Monitoring_Quality_and_Lineage_of_Data.png 400w, https://mattaslett.isg-research.net/hs-fs/hubfs/Bigeye_Enable_Monitoring_Quality_and_Lineage_of_Data.png?width=500&name=Bigeye_Enable_Monitoring_Quality_and_Lineage_of_Data.png 500w, https://mattaslett.isg-research.net/hs-fs/hubfs/Bigeye_Enable_Monitoring_Quality_and_Lineage_of_Data.png?width=600&name=Bigeye_Enable_Monitoring_Quality_and_Lineage_of_Data.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p><span data-contrast="auto">I </span><a href="https://blog.ventanaresearch.com/data-observability-improves-trust-in-data-for-decision-making"><span data-contrast="none">previously explained</span></a><span data-contrast="auto"> that data observability software has become a critical component of data-driven decision-making. Data observability addresses one of the most significant impediments to generating value from data by providing an environment for monitoring the quality and reliability of data on a continual basis. Maintaining quality and trust is a perennial data management challenge, the importance of which has come into sharper focus in recent years thanks to the rise of artificial...</span></p> </div> <a href="https://mattaslett.isg-research.net/bigeye-enable-monitoring-quality-and-lineage-of-data"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://mattaslett.isg-research.net/tag/ai">AI</a>, <a href="https://mattaslett.isg-research.net/tag/data-operations">data operations</a>, <a href="https://mattaslett.isg-research.net/tag/machine-learning-operations">Machine Learning Operations</a>, <a href="https://mattaslett.isg-research.net/tag/analytics-and-data">Analytics and Data</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://mattaslett.isg-research.net/capital-one-offers-cost-controls-for-cloud-data-warehouses">Capital One Offers Cost Controls for Cloud Data Warehouses</a></h2> <div class="author-meta"> Posted by <a href="https://mattaslett.isg-research.net/author/matt-aslett"> Matt Aslett </a> on <span class="post-date">07 November 2024 </span> </div>  <hr> </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://mattaslett.isg-research.net/hs-fs/hubfs/Capital_One_Offers_Cost_Controls_for_Cloud_Data_Warehouses.png?width=200&name=Capital_One_Offers_Cost_Controls_for_Cloud_Data_Warehouses.png" width="200" !important: srcset="https://mattaslett.isg-research.net/hs-fs/hubfs/Capital_One_Offers_Cost_Controls_for_Cloud_Data_Warehouses.png?width=100&name=Capital_One_Offers_Cost_Controls_for_Cloud_Data_Warehouses.png 100w, https://mattaslett.isg-research.net/hs-fs/hubfs/Capital_One_Offers_Cost_Controls_for_Cloud_Data_Warehouses.png?width=200&name=Capital_One_Offers_Cost_Controls_for_Cloud_Data_Warehouses.png 200w, https://mattaslett.isg-research.net/hs-fs/hubfs/Capital_One_Offers_Cost_Controls_for_Cloud_Data_Warehouses.png?width=300&name=Capital_One_Offers_Cost_Controls_for_Cloud_Data_Warehouses.png 300w, https://mattaslett.isg-research.net/hs-fs/hubfs/Capital_One_Offers_Cost_Controls_for_Cloud_Data_Warehouses.png?width=400&name=Capital_One_Offers_Cost_Controls_for_Cloud_Data_Warehouses.png 400w, https://mattaslett.isg-research.net/hs-fs/hubfs/Capital_One_Offers_Cost_Controls_for_Cloud_Data_Warehouses.png?width=500&name=Capital_One_Offers_Cost_Controls_for_Cloud_Data_Warehouses.png 500w, https://mattaslett.isg-research.net/hs-fs/hubfs/Capital_One_Offers_Cost_Controls_for_Cloud_Data_Warehouses.png?width=600&name=Capital_One_Offers_Cost_Controls_for_Cloud_Data_Warehouses.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p>The adoption of cloud environments for analytic workloads has been a key feature of the <span><a href="https://www.ventanaresearch.com/focus/analytics-and-data/data-platforms">data platforms</a></span> sector in recent years. For two-thirds (66%) of participants in ISG’s Data Lake Dynamic Insights Research, the primary data platform used for analytics is cloud based. Many enterprises adopted cloud-based analytic data platforms with a view to improving operational efficiencies by reducing the need for upfront investment in physical infrastructure as well as the ability to scale cloud services...</p> </div> <a href="https://mattaslett.isg-research.net/capital-one-offers-cost-controls-for-cloud-data-warehouses"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://mattaslett.isg-research.net/tag/data-operations">data operations</a>, <a href="https://mattaslett.isg-research.net/tag/data-platforms">Data Platforms</a>, <a href="https://mattaslett.isg-research.net/tag/analytics-and-data">Analytics and Data</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://mattaslett.isg-research.net/the-increasing-importance-of-open-table-formats">The Increasing Importance of Open Table Formats</a></h2> <div class="author-meta"> Posted by <a href="https://mattaslett.isg-research.net/author/matt-aslett"> Matt Aslett </a> on <span class="post-date">31 October 2024 </span> </div>  <hr> </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://mattaslett.isg-research.net/hs-fs/hubfs/The_Increasing_Importance_of_Open_Table_Formats.png?width=200&name=The_Increasing_Importance_of_Open_Table_Formats.png" width="200" !important: srcset="https://mattaslett.isg-research.net/hs-fs/hubfs/The_Increasing_Importance_of_Open_Table_Formats.png?width=100&name=The_Increasing_Importance_of_Open_Table_Formats.png 100w, https://mattaslett.isg-research.net/hs-fs/hubfs/The_Increasing_Importance_of_Open_Table_Formats.png?width=200&name=The_Increasing_Importance_of_Open_Table_Formats.png 200w, https://mattaslett.isg-research.net/hs-fs/hubfs/The_Increasing_Importance_of_Open_Table_Formats.png?width=300&name=The_Increasing_Importance_of_Open_Table_Formats.png 300w, https://mattaslett.isg-research.net/hs-fs/hubfs/The_Increasing_Importance_of_Open_Table_Formats.png?width=400&name=The_Increasing_Importance_of_Open_Table_Formats.png 400w, https://mattaslett.isg-research.net/hs-fs/hubfs/The_Increasing_Importance_of_Open_Table_Formats.png?width=500&name=The_Increasing_Importance_of_Open_Table_Formats.png 500w, https://mattaslett.isg-research.net/hs-fs/hubfs/The_Increasing_Importance_of_Open_Table_Formats.png?width=600&name=The_Increasing_Importance_of_Open_Table_Formats.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p>I <span><a href="https://blog.ventanaresearch.com/hydroanalytic-data-platforms-power-data-lakes-strategic-value">previously wrote</a></span> about the importance of open table formats to the evolution of data lakes into data lakehouses. The concept of the data lake was initially proposed as a single environment where data could be combined from multiple sources to be stored and processed to enable analysis by multiple users for multiple purposes.</p> </div> <a href="https://mattaslett.isg-research.net/the-increasing-importance-of-open-table-formats"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://mattaslett.isg-research.net/tag/data-platforms">Data Platforms</a>, <a href="https://mattaslett.isg-research.net/tag/analytics-data">Analytics & Data</a>, <a href="https://mattaslett.isg-research.net/tag/streaming-data-events">Streaming Data & Events</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://mattaslett.isg-research.net/denodo-provides-a-logical-approach-to-data-management">Denodo Provides a Logical Approach to Data Management</a></h2> <div class="author-meta"> Posted by <a href="https://mattaslett.isg-research.net/author/matt-aslett"> Matt Aslett </a> on <span class="post-date">24 October 2024 </span> </div>  <hr> </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://mattaslett.isg-research.net/hs-fs/hubfs/Denodo_Provides_a_Logical_Approach_to_Data_Management.png?width=200&name=Denodo_Provides_a_Logical_Approach_to_Data_Management.png" width="200" !important: srcset="https://mattaslett.isg-research.net/hs-fs/hubfs/Denodo_Provides_a_Logical_Approach_to_Data_Management.png?width=100&name=Denodo_Provides_a_Logical_Approach_to_Data_Management.png 100w, https://mattaslett.isg-research.net/hs-fs/hubfs/Denodo_Provides_a_Logical_Approach_to_Data_Management.png?width=200&name=Denodo_Provides_a_Logical_Approach_to_Data_Management.png 200w, https://mattaslett.isg-research.net/hs-fs/hubfs/Denodo_Provides_a_Logical_Approach_to_Data_Management.png?width=300&name=Denodo_Provides_a_Logical_Approach_to_Data_Management.png 300w, https://mattaslett.isg-research.net/hs-fs/hubfs/Denodo_Provides_a_Logical_Approach_to_Data_Management.png?width=400&name=Denodo_Provides_a_Logical_Approach_to_Data_Management.png 400w, https://mattaslett.isg-research.net/hs-fs/hubfs/Denodo_Provides_a_Logical_Approach_to_Data_Management.png?width=500&name=Denodo_Provides_a_Logical_Approach_to_Data_Management.png 500w, https://mattaslett.isg-research.net/hs-fs/hubfs/Denodo_Provides_a_Logical_Approach_to_Data_Management.png?width=600&name=Denodo_Provides_a_Logical_Approach_to_Data_Management.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p>Although the terms data fabric and data mesh are often used interchangeably, I <span><a href="https://blog.ventanaresearch.com/disentangling-and-demystifying-data-mesh-and-data-fabric">previously explained</a></span> that they are distinct but complementary. Data fabric refers to technology products that can be used to integrate, manage and govern data across distributed environments, supporting the cultural and organizational data ownership and access goals of data mesh. Data fabric and data mesh are also both related to logical data management, which is the approach of providing virtualized access to data...</p> </div> <a href="https://mattaslett.isg-research.net/denodo-provides-a-logical-approach-to-data-management"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://mattaslett.isg-research.net/tag/data-intelligence">Data Intelligence</a>, <a href="https://mattaslett.isg-research.net/tag/analytics-and-data">Analytics and Data</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://mattaslett.isg-research.net/faunas-data-platform-combines-agility-and-transaction-integrity">Fauna’s Data Platform Combines Agility and Transaction Integrity</a></h2> <div class="author-meta"> Posted by <a href="https://mattaslett.isg-research.net/author/matt-aslett"> Matt Aslett </a> on <span class="post-date">16 October 2024 </span> </div>  <hr> </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://mattaslett.isg-research.net/hs-fs/hubfs/Faunas_Data_Platform_Combines_Agility_and_Transaction_Integrity.png?width=200&name=Faunas_Data_Platform_Combines_Agility_and_Transaction_Integrity.png" width="200" !important: srcset="https://mattaslett.isg-research.net/hs-fs/hubfs/Faunas_Data_Platform_Combines_Agility_and_Transaction_Integrity.png?width=100&name=Faunas_Data_Platform_Combines_Agility_and_Transaction_Integrity.png 100w, https://mattaslett.isg-research.net/hs-fs/hubfs/Faunas_Data_Platform_Combines_Agility_and_Transaction_Integrity.png?width=200&name=Faunas_Data_Platform_Combines_Agility_and_Transaction_Integrity.png 200w, https://mattaslett.isg-research.net/hs-fs/hubfs/Faunas_Data_Platform_Combines_Agility_and_Transaction_Integrity.png?width=300&name=Faunas_Data_Platform_Combines_Agility_and_Transaction_Integrity.png 300w, https://mattaslett.isg-research.net/hs-fs/hubfs/Faunas_Data_Platform_Combines_Agility_and_Transaction_Integrity.png?width=400&name=Faunas_Data_Platform_Combines_Agility_and_Transaction_Integrity.png 400w, https://mattaslett.isg-research.net/hs-fs/hubfs/Faunas_Data_Platform_Combines_Agility_and_Transaction_Integrity.png?width=500&name=Faunas_Data_Platform_Combines_Agility_and_Transaction_Integrity.png 500w, https://mattaslett.isg-research.net/hs-fs/hubfs/Faunas_Data_Platform_Combines_Agility_and_Transaction_Integrity.png?width=600&name=Faunas_Data_Platform_Combines_Agility_and_Transaction_Integrity.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p><span data-contrast="auto">As I noted in the </span><a href="https://www.ventanaresearch.com/buyers_guide/analytics_and_data/operational_data_platforms"><span data-contrast="none">2024 Buyers Guide for Operational Data Platforms</span></a><span data-contrast="auto">, intelligent applications powered by artificial intelligence have impacted </span><span data-contrast="none">the requirements for operational data platforms. These</span><span data-contrast="auto"> applications, infused with contextually relevant recommendations, predictions and forecasting, are </span><span data-contrast="none">driven by machine learning and generative AI.</span></p> </div> <a href="https://mattaslett.isg-research.net/faunas-data-platform-combines-agility-and-transaction-integrity"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://mattaslett.isg-research.net/tag/data-platforms">Data Platforms</a>, <a href="https://mattaslett.isg-research.net/tag/analytics-data">Analytics & Data</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://mattaslett.isg-research.net/collibra-provides-a-platform-for-data-intelligence">Collibra Provides a Platform for Data Intelligence</a></h2> <div class="author-meta"> Posted by <a href="https://mattaslett.isg-research.net/author/matt-aslett"> Matt Aslett </a> on <span class="post-date">08 October 2024 </span> </div>  <hr> </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://mattaslett.isg-research.net/hs-fs/hubfs/Collibra_Provides_a_Platform_for_Data_Intelligence.png?width=200&name=Collibra_Provides_a_Platform_for_Data_Intelligence.png" width="200" !important: srcset="https://mattaslett.isg-research.net/hs-fs/hubfs/Collibra_Provides_a_Platform_for_Data_Intelligence.png?width=100&name=Collibra_Provides_a_Platform_for_Data_Intelligence.png 100w, https://mattaslett.isg-research.net/hs-fs/hubfs/Collibra_Provides_a_Platform_for_Data_Intelligence.png?width=200&name=Collibra_Provides_a_Platform_for_Data_Intelligence.png 200w, https://mattaslett.isg-research.net/hs-fs/hubfs/Collibra_Provides_a_Platform_for_Data_Intelligence.png?width=300&name=Collibra_Provides_a_Platform_for_Data_Intelligence.png 300w, https://mattaslett.isg-research.net/hs-fs/hubfs/Collibra_Provides_a_Platform_for_Data_Intelligence.png?width=400&name=Collibra_Provides_a_Platform_for_Data_Intelligence.png 400w, https://mattaslett.isg-research.net/hs-fs/hubfs/Collibra_Provides_a_Platform_for_Data_Intelligence.png?width=500&name=Collibra_Provides_a_Platform_for_Data_Intelligence.png 500w, https://mattaslett.isg-research.net/hs-fs/hubfs/Collibra_Provides_a_Platform_for_Data_Intelligence.png?width=600&name=Collibra_Provides_a_Platform_for_Data_Intelligence.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p>As I <span><a href="https://blog.ventanaresearch.com/data-intelligence-fundamental-to-data-architecture">recently noted</a></span>, the term “data intelligence” has been used by multiple providers across analytics and data for several years and is becoming more widespread as software providers respond to the need to provide enterprises with a holistic view of data production and consumption. I assert that through 2027, three-quarters of enterprises will be engaged in data intelligence initiatives to increase trust in their data by leveraging metadata to understand how, when and where data is used in...</p> </div> <a href="https://mattaslett.isg-research.net/collibra-provides-a-platform-for-data-intelligence"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://mattaslett.isg-research.net/tag/data-intelligence">Data Intelligence</a>, <a href="https://mattaslett.isg-research.net/tag/analytics-and-data">Analytics and Data</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://mattaslett.isg-research.net/data-as-a-product-needs-and-requirements">Data as a Product: Needs and Requirements</a></h2> <div class="author-meta"> Posted by <a href="https://mattaslett.isg-research.net/author/matt-aslett"> Matt Aslett </a> on <span class="post-date">02 October 2024 </span> </div>  <hr> </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://mattaslett.isg-research.net/hs-fs/hubfs/Data_as_a_Product_Needs_and_Requirements.png?width=200&name=Data_as_a_Product_Needs_and_Requirements.png" width="200" !important: srcset="https://mattaslett.isg-research.net/hs-fs/hubfs/Data_as_a_Product_Needs_and_Requirements.png?width=100&name=Data_as_a_Product_Needs_and_Requirements.png 100w, https://mattaslett.isg-research.net/hs-fs/hubfs/Data_as_a_Product_Needs_and_Requirements.png?width=200&name=Data_as_a_Product_Needs_and_Requirements.png 200w, https://mattaslett.isg-research.net/hs-fs/hubfs/Data_as_a_Product_Needs_and_Requirements.png?width=300&name=Data_as_a_Product_Needs_and_Requirements.png 300w, https://mattaslett.isg-research.net/hs-fs/hubfs/Data_as_a_Product_Needs_and_Requirements.png?width=400&name=Data_as_a_Product_Needs_and_Requirements.png 400w, https://mattaslett.isg-research.net/hs-fs/hubfs/Data_as_a_Product_Needs_and_Requirements.png?width=500&name=Data_as_a_Product_Needs_and_Requirements.png 500w, https://mattaslett.isg-research.net/hs-fs/hubfs/Data_as_a_Product_Needs_and_Requirements.png?width=600&name=Data_as_a_Product_Needs_and_Requirements.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p>I previously wrote about <span><a href="https://blog.ventanaresearch.com/the-benefits-of-data-mesh-extend-to-organizational-and-cultural-change">data mesh</a></span> as a cultural and organizational approach to distributed data processing. Data mesh has four key principles—domain-oriented ownership, data as a product, self-serve data infrastructure and federated governance—each of which is being widely adopted. I assert that by 2027, more than 6 in 10 enterprises will adopt technologies to facilitate the delivery of data as a product as they adapt their cultural and organizational approaches to data ownership in the...</p> </div> <a href="https://mattaslett.isg-research.net/data-as-a-product-needs-and-requirements"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://mattaslett.isg-research.net/tag/data-operations">data operations</a>, <a href="https://mattaslett.isg-research.net/tag/analytics-and-data">Analytics and Data</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://mattaslett.isg-research.net/snowflake-offers-a-platform-for-ai-as-well-as-data">Snowflake Offers a Platform for AI as well as Data</a></h2> <div class="author-meta"> Posted by <a href="https://mattaslett.isg-research.net/author/matt-aslett"> Matt Aslett </a> on <span class="post-date">19 September 2024 </span> </div>  <hr> </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://mattaslett.isg-research.net/hs-fs/hubfs/Snowflake_Offers_a_Platform_for_AI_as_well_as_Data.png?width=200&name=Snowflake_Offers_a_Platform_for_AI_as_well_as_Data.png" width="200" !important: srcset="https://mattaslett.isg-research.net/hs-fs/hubfs/Snowflake_Offers_a_Platform_for_AI_as_well_as_Data.png?width=100&name=Snowflake_Offers_a_Platform_for_AI_as_well_as_Data.png 100w, https://mattaslett.isg-research.net/hs-fs/hubfs/Snowflake_Offers_a_Platform_for_AI_as_well_as_Data.png?width=200&name=Snowflake_Offers_a_Platform_for_AI_as_well_as_Data.png 200w, https://mattaslett.isg-research.net/hs-fs/hubfs/Snowflake_Offers_a_Platform_for_AI_as_well_as_Data.png?width=300&name=Snowflake_Offers_a_Platform_for_AI_as_well_as_Data.png 300w, https://mattaslett.isg-research.net/hs-fs/hubfs/Snowflake_Offers_a_Platform_for_AI_as_well_as_Data.png?width=400&name=Snowflake_Offers_a_Platform_for_AI_as_well_as_Data.png 400w, https://mattaslett.isg-research.net/hs-fs/hubfs/Snowflake_Offers_a_Platform_for_AI_as_well_as_Data.png?width=500&name=Snowflake_Offers_a_Platform_for_AI_as_well_as_Data.png 500w, https://mattaslett.isg-research.net/hs-fs/hubfs/Snowflake_Offers_a_Platform_for_AI_as_well_as_Data.png?width=600&name=Snowflake_Offers_a_Platform_for_AI_as_well_as_Data.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p>As I explained in our recent <span><a href="https://www.ventanaresearch.com/buyers_guide/analytics_and_data/data_platforms">Buyers Guide for Data Platforms</a></span>, the popularization of generative artificial intelligence (GenAI) has had a significant impact on the requirements for data platforms in the last 18 months. While there is an ongoing need for data platforms to support data warehousing workloads involving analytic reports and dashboards, there is increasing demand for analytic data platform providers to add dedicated functionality for data engineering, including the development,...</p> </div> <a href="https://mattaslett.isg-research.net/snowflake-offers-a-platform-for-ai-as-well-as-data"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://mattaslett.isg-research.net/tag/analytics">Analytics</a>, <a href="https://mattaslett.isg-research.net/tag/natural-language-processing">natural language processing</a>, <a href="https://mattaslett.isg-research.net/tag/data-platforms">Data Platforms</a>, <a href="https://mattaslett.isg-research.net/tag/ai-and-machine-learning">AI and Machine Learning</a>, <a href="https://mattaslett.isg-research.net/tag/generative-ai">Generative AI</a>, <a href="https://mattaslett.isg-research.net/tag/model-building-and-large-language-models">Model Building and Large Language Models</a>, <a href="https://mattaslett.isg-research.net/tag/machine-learning-operations">Machine Learning Operations</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://mattaslett.isg-research.net/harness-events-to-process-data-at-the-speed-of-business">Harness Events to Process Data at the Speed of Business</a></h2> <div class="author-meta"> Posted by <a href="https://mattaslett.isg-research.net/author/matt-aslett"> Matt Aslett </a> on <span class="post-date">17 September 2024 </span> </div>  <hr> </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://mattaslett.isg-research.net/hs-fs/hubfs/Harness_Events_to_Process_Data_at_the_Speed_of_Business.png?width=200&name=Harness_Events_to_Process_Data_at_the_Speed_of_Business.png" width="200" !important: srcset="https://mattaslett.isg-research.net/hs-fs/hubfs/Harness_Events_to_Process_Data_at_the_Speed_of_Business.png?width=100&name=Harness_Events_to_Process_Data_at_the_Speed_of_Business.png 100w, https://mattaslett.isg-research.net/hs-fs/hubfs/Harness_Events_to_Process_Data_at_the_Speed_of_Business.png?width=200&name=Harness_Events_to_Process_Data_at_the_Speed_of_Business.png 200w, https://mattaslett.isg-research.net/hs-fs/hubfs/Harness_Events_to_Process_Data_at_the_Speed_of_Business.png?width=300&name=Harness_Events_to_Process_Data_at_the_Speed_of_Business.png 300w, https://mattaslett.isg-research.net/hs-fs/hubfs/Harness_Events_to_Process_Data_at_the_Speed_of_Business.png?width=400&name=Harness_Events_to_Process_Data_at_the_Speed_of_Business.png 400w, https://mattaslett.isg-research.net/hs-fs/hubfs/Harness_Events_to_Process_Data_at_the_Speed_of_Business.png?width=500&name=Harness_Events_to_Process_Data_at_the_Speed_of_Business.png 500w, https://mattaslett.isg-research.net/hs-fs/hubfs/Harness_Events_to_Process_Data_at_the_Speed_of_Business.png?width=600&name=Harness_Events_to_Process_Data_at_the_Speed_of_Business.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p>The final of the men’s 100 meters at the Paris Olympics this summer was a reminder that being successful requires not just being fast but performing at the right time. Being fast is obviously a prerequisite for participating in an Olympic 100-meter final, and all the competitors finished the race in under 10 seconds, with just 0.12 seconds separating the first man from the last. While all the athletes were fast, what separated the winner of the gold medal—USA’s Noah Lyles—was execution. He was <a href="https://www.independent.co.uk/sport/olympics/noah-lyles-100m-final-olympics-b2591292.html">...</a></p> </div> <a href="https://mattaslett.isg-research.net/harness-events-to-process-data-at-the-speed-of-business"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://mattaslett.isg-research.net/tag/analytics-data">Analytics & Data</a>, <a href="https://mattaslett.isg-research.net/tag/streaming-data-events">Streaming Data & Events</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://mattaslett.isg-research.net/ibm-provides-architectural-choice-for-data-platforms">IBM Provides Architectural Choice for Data Platforms</a></h2> <div class="author-meta"> Posted by <a href="https://mattaslett.isg-research.net/author/matt-aslett"> Matt Aslett </a> on <span class="post-date">27 August 2024 </span> </div>  <hr> </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://mattaslett.isg-research.net/hs-fs/hubfs/IBM_Provides_Architectural_Choice_for_Data_Platforms.png?width=200&name=IBM_Provides_Architectural_Choice_for_Data_Platforms.png" width="200" !important: srcset="https://mattaslett.isg-research.net/hs-fs/hubfs/IBM_Provides_Architectural_Choice_for_Data_Platforms.png?width=100&name=IBM_Provides_Architectural_Choice_for_Data_Platforms.png 100w, https://mattaslett.isg-research.net/hs-fs/hubfs/IBM_Provides_Architectural_Choice_for_Data_Platforms.png?width=200&name=IBM_Provides_Architectural_Choice_for_Data_Platforms.png 200w, https://mattaslett.isg-research.net/hs-fs/hubfs/IBM_Provides_Architectural_Choice_for_Data_Platforms.png?width=300&name=IBM_Provides_Architectural_Choice_for_Data_Platforms.png 300w, https://mattaslett.isg-research.net/hs-fs/hubfs/IBM_Provides_Architectural_Choice_for_Data_Platforms.png?width=400&name=IBM_Provides_Architectural_Choice_for_Data_Platforms.png 400w, https://mattaslett.isg-research.net/hs-fs/hubfs/IBM_Provides_Architectural_Choice_for_Data_Platforms.png?width=500&name=IBM_Provides_Architectural_Choice_for_Data_Platforms.png 500w, https://mattaslett.isg-research.net/hs-fs/hubfs/IBM_Provides_Architectural_Choice_for_Data_Platforms.png?width=600&name=IBM_Provides_Architectural_Choice_for_Data_Platforms.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p>Enterprises face a bewildering level of choice in relation to data platforms, as evidenced by the number of software providers and products assessed in our recent <span><a href="https://www.ventanaresearch.com/buyers_guide/analytics_and_data/2024-buyers-guides-for-data-platforms">Data Platforms Buyers Guide</a></span>. There are numerous <span><a href="https://www.ventanaresearch.com/focus/analytics-and-data/data-platforms">data platform</a></span> providers and products to choose from, but also a diverse array of functional and architectural options. Is the workload primarily operational or analytic? Will it be deployed on-premises or in the cloud? Should it be distributed or centralized? Data warehouse or data...</p> </div> <a href="https://mattaslett.isg-research.net/ibm-provides-architectural-choice-for-data-platforms"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://mattaslett.isg-research.net/tag/data-platforms">Data Platforms</a>, <a href="https://mattaslett.isg-research.net/tag/ai-and-machine-learning">AI and Machine Learning</a>, <a href="https://mattaslett.isg-research.net/tag/data-intelligence">Data Intelligence</a>, <a href="https://mattaslett.isg-research.net/tag/analytics-and-data">Analytics and Data</a> <hr> </div> <script> // Check active classes var checkClass = function() { if ( $('.fil_item').hasClass('hide') ) { $('.fil_item').removeClass('hide'); } }; // Category filters $('.all').click( function() { checkClass(); }); // Active tag $('.bttn').click(function(){ $('.bttn').removeClass('active'); $(this).addClass('active'); }) </script> </div> <div class="blog-numeric-pagination span12 ">   <ul class="numeric-pagination"> <li class="disabled"><a href="https://mattaslett.isg-research.net/page/0"><svg xmlns="http://www.w3.org/2000/svg" viewbox="0 0 492 492" width="20px" height="20px"> <path d="M198.608,246.104L382.664,62.04c5.068-5.056,7.856-11.816,7.856-19.024c0-7.212-2.788-13.968-7.856-19.032l-16.128-16.12 C361.476,2.792,354.712,0,347.504,0s-13.964,2.792-19.028,7.864L109.328,227.008c-5.084,5.08-7.868,11.868-7.848,19.084 c-0.02,7.248,2.76,14.028,7.848,19.112l218.944,218.932c5.064,5.072,11.82,7.864,19.032,7.864c7.208,0,13.964-2.792,19.032-7.864 l16.124-16.12c10.492-10.492,10.492-27.572,0-38.06L198.608,246.104z" /> </svg></a></li> <li class="active"><a href="https://mattaslett.isg-research.net">1</a></li> <li><a href="https://mattaslett.isg-research.net/page/2" rel="next">2</a></li> <li><a href="https://mattaslett.isg-research.net/page/3" rel="next">3</a></li> <li><a href="https://mattaslett.isg-research.net/page/4" rel="next">4</a></li> <li><a href="https://mattaslett.isg-research.net/page/5" rel="next">5</a></li> <li class=""><a href="https://mattaslett.isg-research.net/page/2" rel="next"><svg xmlns="http://www.w3.org/2000/svg" width="20px" height="20px" viewbox="0 0 451.846 451.847"> <path d="M345.441,248.292L151.154,442.573c-12.359,12.365-32.397,12.365-44.75,0c-12.354-12.354-12.354-32.391,0-44.744 L278.318,225.92L106.409,54.017c-12.354-12.359-12.354-32.394,0-44.748c12.354-12.359,32.391-12.359,44.75,0l194.287,194.284 c6.177,6.18,9.262,14.271,9.262,22.366C354.708,234.018,351.617,242.115,345.441,248.292z" fill="" /> </svg></a></li> </ul> </div> </div> <div class="blog-sidebar">   <style> .blog-sidebar h3 { font-size: 18px; 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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://mattaslett.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://mattaslett.isg-research.net/hs-fs/hubfs/_images/_team/Menninger.David%20Pic.Circle.png?width=50&name=Menninger.David%20Pic.Circle.png 50w, https://mattaslett.isg-research.net/hs-fs/hubfs/_images/_team/Menninger.David%20Pic.Circle.png?width=100&name=Menninger.David%20Pic.Circle.png 100w, https://mattaslett.isg-research.net/hs-fs/hubfs/_images/_team/Menninger.David%20Pic.Circle.png?width=150&name=Menninger.David%20Pic.Circle.png 150w, https://mattaslett.isg-research.net/hs-fs/hubfs/_images/_team/Menninger.David%20Pic.Circle.png?width=200&name=Menninger.David%20Pic.Circle.png 200w, https://mattaslett.isg-research.net/hs-fs/hubfs/_images/_team/Menninger.David%20Pic.Circle.png?width=250&name=Menninger.David%20Pic.Circle.png 250w, https://mattaslett.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://mattaslett.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://mattaslett.isg-research.net/hs-fs/hubfs/_images/_team/Orr.Jeff.Circle.png?width=50&name=Orr.Jeff.Circle.png 50w, https://mattaslett.isg-research.net/hs-fs/hubfs/_images/_team/Orr.Jeff.Circle.png?width=100&name=Orr.Jeff.Circle.png 100w, https://mattaslett.isg-research.net/hs-fs/hubfs/_images/_team/Orr.Jeff.Circle.png?width=150&name=Orr.Jeff.Circle.png 150w, https://mattaslett.isg-research.net/hs-fs/hubfs/_images/_team/Orr.Jeff.Circle.png?width=200&name=Orr.Jeff.Circle.png 200w, https://mattaslett.isg-research.net/hs-fs/hubfs/_images/_team/Orr.Jeff.Circle.png?width=250&name=Orr.Jeff.Circle.png 250w, https://mattaslett.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://mattaslett.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://mattaslett.isg-research.net/hs-fs/hubfs/_images/_team/Dawson.Keith%20Pic.Circle.png?width=50&name=Dawson.Keith%20Pic.Circle.png 50w, https://mattaslett.isg-research.net/hs-fs/hubfs/_images/_team/Dawson.Keith%20Pic.Circle.png?width=100&name=Dawson.Keith%20Pic.Circle.png 100w, https://mattaslett.isg-research.net/hs-fs/hubfs/_images/_team/Dawson.Keith%20Pic.Circle.png?width=150&name=Dawson.Keith%20Pic.Circle.png 150w, https://mattaslett.isg-research.net/hs-fs/hubfs/_images/_team/Dawson.Keith%20Pic.Circle.png?width=200&name=Dawson.Keith%20Pic.Circle.png 200w, https://mattaslett.isg-research.net/hs-fs/hubfs/_images/_team/Dawson.Keith%20Pic.Circle.png?width=250&name=Dawson.Keith%20Pic.Circle.png 250w, https://mattaslett.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://mattaslett.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://mattaslett.isg-research.net/hs-fs/hubfs/_images/_team/Smith.Mark%20Pic.Circle.png?width=50&name=Smith.Mark%20Pic.Circle.png 50w, https://mattaslett.isg-research.net/hs-fs/hubfs/_images/_team/Smith.Mark%20Pic.Circle.png?width=100&name=Smith.Mark%20Pic.Circle.png 100w, https://mattaslett.isg-research.net/hs-fs/hubfs/_images/_team/Smith.Mark%20Pic.Circle.png?width=150&name=Smith.Mark%20Pic.Circle.png 150w, https://mattaslett.isg-research.net/hs-fs/hubfs/_images/_team/Smith.Mark%20Pic.Circle.png?width=200&name=Smith.Mark%20Pic.Circle.png 200w, https://mattaslett.isg-research.net/hs-fs/hubfs/_images/_team/Smith.Mark%20Pic.Circle.png?width=250&name=Smith.Mark%20Pic.Circle.png 250w, https://mattaslett.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://mattaslett.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://mattaslett.isg-research.net/hs-fs/hubfs/_images/_team/Aslett.Matt%20Pic.Circle.png?width=50&name=Aslett.Matt%20Pic.Circle.png 50w, https://mattaslett.isg-research.net/hs-fs/hubfs/_images/_team/Aslett.Matt%20Pic.Circle.png?width=100&name=Aslett.Matt%20Pic.Circle.png 100w, https://mattaslett.isg-research.net/hs-fs/hubfs/_images/_team/Aslett.Matt%20Pic.Circle.png?width=150&name=Aslett.Matt%20Pic.Circle.png 150w, https://mattaslett.isg-research.net/hs-fs/hubfs/_images/_team/Aslett.Matt%20Pic.Circle.png?width=200&name=Aslett.Matt%20Pic.Circle.png 200w, https://mattaslett.isg-research.net/hs-fs/hubfs/_images/_team/Aslett.Matt%20Pic.Circle.png?width=250&name=Aslett.Matt%20Pic.Circle.png 250w, https://mattaslett.isg-research.net/hs-fs/hubfs/_images/_team/Aslett.Matt%20Pic.Circle.png?width=300&name=Aslett.Matt%20Pic.Circle.png 300w"> <h5 class="analyst_name">Matt Aslett</h5> </a> </div> <div class="span6" style="text-align: center;"> <a href="https://matthewbrown.isg-research.net/"> <img class="analyst_img" style="width: 100px;" src="https://mattaslett.isg-research.net/hs-fs/hubfs/_images/_team/profile_vr_matthew_brown_circle_900x900.png?width=100&name=profile_vr_matthew_brown_circle_900x900.png" alt="Matthew Brown" width="100" data-constrained="true" caption="false" sizes="(max-width: 100px) 100vw, 100px" srcset="https://mattaslett.isg-research.net/hs-fs/hubfs/_images/_team/profile_vr_matthew_brown_circle_900x900.png?width=50&name=profile_vr_matthew_brown_circle_900x900.png 50w, https://mattaslett.isg-research.net/hs-fs/hubfs/_images/_team/profile_vr_matthew_brown_circle_900x900.png?width=100&name=profile_vr_matthew_brown_circle_900x900.png 100w, https://mattaslett.isg-research.net/hs-fs/hubfs/_images/_team/profile_vr_matthew_brown_circle_900x900.png?width=150&name=profile_vr_matthew_brown_circle_900x900.png 150w, https://mattaslett.isg-research.net/hs-fs/hubfs/_images/_team/profile_vr_matthew_brown_circle_900x900.png?width=200&name=profile_vr_matthew_brown_circle_900x900.png 200w, https://mattaslett.isg-research.net/hs-fs/hubfs/_images/_team/profile_vr_matthew_brown_circle_900x900.png?width=250&name=profile_vr_matthew_brown_circle_900x900.png 250w, https://mattaslett.isg-research.net/hs-fs/hubfs/_images/_team/profile_vr_matthew_brown_circle_900x900.png?width=300&name=profile_vr_matthew_brown_circle_900x900.png 300w"> <h5 class="analyst_name">Matthew Brown</h5> </a> </div> </div>   <div class="row-fluid"> <div class="span6" style="text-align: center;"> <a href="https://robertkugel.isg-research.net"> <img class="analyst_img" style="width: 100px;" src="https://mattaslett.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://mattaslett.isg-research.net/hs-fs/hubfs/_images/_team/Kugel.Robert%20Pic.Circle.png?width=50&name=Kugel.Robert%20Pic.Circle.png 50w, https://mattaslett.isg-research.net/hs-fs/hubfs/_images/_team/Kugel.Robert%20Pic.Circle.png?width=100&name=Kugel.Robert%20Pic.Circle.png 100w, https://mattaslett.isg-research.net/hs-fs/hubfs/_images/_team/Kugel.Robert%20Pic.Circle.png?width=150&name=Kugel.Robert%20Pic.Circle.png 150w, https://mattaslett.isg-research.net/hs-fs/hubfs/_images/_team/Kugel.Robert%20Pic.Circle.png?width=200&name=Kugel.Robert%20Pic.Circle.png 200w, https://mattaslett.isg-research.net/hs-fs/hubfs/_images/_team/Kugel.Robert%20Pic.Circle.png?width=250&name=Kugel.Robert%20Pic.Circle.png 250w, https://mattaslett.isg-research.net/hs-fs/hubfs/_images/_team/Kugel.Robert%20Pic.Circle.png?width=300&name=Kugel.Robert%20Pic.Circle.png 300w"> <h5 class="analyst_name">Robert Kugel</h5> </a> </div> <div class="span6" style="text-align: center;"> <a href="https://stephenhurrell.isg-research.net"> <img class="analyst_img" style="width: 100px;" src="https://mattaslett.isg-research.net/hs-fs/hubfs/_images/_team/Hurrell.Stephen%20Pic.Circle.png?width=100&name=Hurrell.Stephen%20Pic.Circle.png" alt="Stephen Hurrell" width="100" data-constrained="true" caption="false" sizes="(max-width: 100px) 100vw, 100px" srcset="https://mattaslett.isg-research.net/hs-fs/hubfs/_images/_team/Hurrell.Stephen%20Pic.Circle.png?width=50&name=Hurrell.Stephen%20Pic.Circle.png 50w, https://mattaslett.isg-research.net/hs-fs/hubfs/_images/_team/Hurrell.Stephen%20Pic.Circle.png?width=100&name=Hurrell.Stephen%20Pic.Circle.png 100w, https://mattaslett.isg-research.net/hs-fs/hubfs/_images/_team/Hurrell.Stephen%20Pic.Circle.png?width=150&name=Hurrell.Stephen%20Pic.Circle.png 150w, https://mattaslett.isg-research.net/hs-fs/hubfs/_images/_team/Hurrell.Stephen%20Pic.Circle.png?width=200&name=Hurrell.Stephen%20Pic.Circle.png 200w, https://mattaslett.isg-research.net/hs-fs/hubfs/_images/_team/Hurrell.Stephen%20Pic.Circle.png?width=250&name=Hurrell.Stephen%20Pic.Circle.png 250w, https://mattaslett.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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src="/hs/hsstatic/content-cwv-embed/static-1.1293/embed.js"></script> <script src="https://mattaslett.isg-research.net/hs-fs/hub/2035844/hub_generated/template_assets/94278782902/1725882474552/Ventana_Research_2023/Ventana_Research_2023_child.min.js"></script> <script> var hsVars = hsVars || {}; hsVars['language'] = 'en'; </script> <script src="/hs/hsstatic/cos-i18n/static-1.53/bundles/project.js"></script> <script src="https://mattaslett.isg-research.net/hs-fs/hub/2035844/hub_generated/module_assets/90531062927/1702386872146/module_90531062927_Header-Global.min.js"></script> <script type="application/ld+json"> { "@context": "http://schema.org", "@type": "BlogPosting", "headline": "Bigeye Enable Monitoring, Quality and Lineage of Data", "mainEntityOfPage": "https://mattaslett.isg-research.net/bigeye-enable-monitoring-quality-and-lineage-of-data", "datePublished": "20/11/2024", "dateModified": "20/11/2024", "articleBody": "I previously explained that data observability software has become a critical component of data-driven decision-making. Data observability addresses one of the most significant impediments to generating value from data by providing an environment for monitoring the quality and reliability of data on a continual basis. Maintaining quality and trust is a perennial data management challenge, the importance of which has come into sharper focus in recent years thanks to the rise of artificial intelligence (AI). As enterprises seek to automate aspects of decision-making processes using AI, it is essential that they have confidence in the data upon which AI depends. This has increased the focus on data observability software providers such as Bigeye and the role they play in ensuring that data meets quality and reliability requirements.  Bigeye was founded in late 2018 by Chief Executive Officer Kyle Kirwan and Chief Technology Officer Egor Gryaznov. Through their experience with various data-related projects at Uber, Bigeye’s founders had identified reliability as a key concern that impacted the success of data projects. To improve data reliability, enterprises were largely dependent on data-quality tools that required manual effort by data engineers, data architects, data scientists and data analysts. As a result, many data teams were not as productive as they might be, with time and effort spent on manually troubleshooting data-quality issues and testing data pipelines. Additionally, while the tools available at the time enabled data teams to respond to quality issues, they did not provide a way to identify quality thresholds or measure improvement, making it difficult to demonstrate to the business the value of time spent remedying data-quality problems. With the aim of rectifying that situation, Bigeye’s founders set out to build a business around data observability. The company has raised $73.5 million in funding since its formation from the likes of Sequoia Capital, Costanoa Ventures, Coatue, Alteryx and In-Q-Tel, and added a $5 million strategic investment by United Services Automobile Association in October 2024. In addition to expanding its platform through internal research and development, Bigeye also added data lineage capabilities in mid-2023 through the acquisition of Data Advantage Group. Data lineage is now one of three core components of the company’s data observability platform, alongside automated monitoring and anomaly detection. Having trust in data is crucial to business decision-making. However, only 28% of participants in ISG’s Data Governance Benchmark Research report that data is well trusted in their organization. Enterprises have traditionally sought to improve trust in data using data quality tools and platforms to ensure that data used in decision-making processes is accurate, complete, consistent, timely and valid. As I previously explained, although data quality and data observability are closely related and complementary, they are separate product categories. Data quality is concerned with the suitability of the data to a given task and data quality software is used to help users identify and resolve data quality problems. In comparison, data observability is concerned with the reliability and health of the overall data environment. Data observability software automates the detection and identification of the causes of data quality problems, potentially enabling users to prevent data quality issues before they occur. Data observability is a key aspect of data operations (DataOps), which focuses on the application of agile development, DevOps and lean manufacturing by data engineering professionals in support of data production. Bigeye’s monitoring capabilities start with automated dependency mapping to identify the source of data used in analytic dashboards and data products, as well as a lineage graph of the data pipeline. The ability to monitor and measure improvements in data quality relies on instrumentation. This is handled by Bigeye’s data observability platform via what it calls autometrics, which perform automated data quality checks as well as freshness, volume, and schema monitoring. Browser extension-based integration with analytics dashboards provides business and data analysts with instant access to data health information and status alerts. Bigeye’s anomaly detection capabilities rely on the automated generation of data quality thresholds based on machine learning (ML) models fueled by historical data. The company also offers associated alerts delivered to data owners and data consumers, and reinforcement learning to adapt notifications based on user feedback. Bigeye’s data lineage capabilities include connectors to numerous data platforms, data integration tools and business intelligence (BI) dashboards, as well as automated lineage mapping across on-premises and cloud environments, to identify data pipeline dependencies, root cause and impact analysis of data reliability issues, and automatically generate debug queries that aid resolution. I assert that through 2026, two-thirds of enterprises will invest in initiatives to improve trust in data through automated data observability tools addressing the detection, resolution and prevention of data reliability issues. The significance of data observability is increasingly pertinent due to the rise of enterprise AI initiatives that combine enterprise data with AI and generative AI (GenAI) models to automate customer service and business decision-making. Vector search and retrieval-augmented generation can improve trust in GenAI, but are the result of complex data pipelines that need to be monitored and evaluated to ensure the data and processes are reliable. Like other data observability software providers, Bigeye could be making more of its applicability to support AI and GenAI use cases. Nevertheless, I recommend that enterprises exploring potential approaches to improving data reliability should examine Bigeye to understand how its software can facilitate greater trust in data and accelerate data-driven business decisions. Regards, Matt Aslett", "image": { "@type": "ImageObject", "url": "https://www.isg-research.net/hubfs/Bigeye_Enable_Monitoring_Quality_and_Lineage_of_Data.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "Matt Aslett", "url": "https://mattaslett.isg-research.net/author/matt-aslett", "sameAs": "https://www.linkedin.com/in/matt-aslett/" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "mattaslett.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": "Capital One Offers Cost Controls for Cloud Data Warehouses", "mainEntityOfPage": "https://mattaslett.isg-research.net/capital-one-offers-cost-controls-for-cloud-data-warehouses", "datePublished": "07/11/2024", "dateModified": "07/11/2024", "articleBody": "The adoption of cloud environments for analytic workloads has been a key feature of the data platforms sector in recent years. For two-thirds (66%) of participants in ISG’s Data Lake Dynamic Insights Research, the primary data platform used for analytics is cloud based. Many enterprises adopted cloud-based analytic data platforms with a view to improving operational efficiencies by reducing the need for upfront investment in physical infrastructure as well as the ability to scale cloud services up and down to match fluctuating requirements. As adoption has grown, some enterprises found that the theoretical advantages of data processing in the cloud can be more challenging to deliver in practice, with constant monitoring and manual intervention required to optimize resources and realize potential savings. This fostered the emergence of an ecosystem of software providers, including Capital One Software, with products designed to optimize the efficient use of cloud analytic data platforms. Capital One Software was launched in 2022 to build a business around Capital One Slingshot. Slingshot is a data management software product initially developed by Capital One Financial Corporation to accelerate and manage its internal adoption of Snowflake’s cloud-based analytic data platform. The journey to the launch of Capital One Software can be traced back to 2013 and the adoption by Capital One’s internal IT team of a new agile development approach that embraced RESTful application programming interfaces, decentralized and collaborative development and cloud computing. Capital One began its transition to a cloud-first company in 2016 and completed its migration away from on-premises data centers to the cloud in 2020. Along the way, it adopted Snowflake’s AI Data Cloud and became an investor in the company in 2017. The origins of Capital One Slingshot began with the need to develop internal tooling to ensure that the company realized potential business value improvements by managing costs and automating governance processes. Key goals were reducing the amount of time and resources spent on managing Snowflake warehouses and ensuring that Capital One’s central IT teams did not become a bottleneck that limited the adoption of Snowflake across the company. With the launch of Capital One Slingshot in 2022, the internal development work was made available to other enterprises. While Capital One Software has focused specifically on Snowflake AI Data Cloud environments, the company announced in June 2024 that it intends to adapt Slingshot to the Databricks’ Data Intelligence Platform to address cost management. Cloud data warehouse services remove the requirement for upfront hardware and software licensing costs and, in theory, make it far simpler for enterprises to scale data warehouses up and down to maximize the efficient use of resources in response to usage requirements. I assert that through 2026, 8 in 10 enterprises will migrate on-premises analytics and data workloads to cloud environments, shifting focus to improving innovation and efficiency rather than maintaining existing systems. Efficient usage of cloud services is easier said than done, however. Failure to make the most efficient use of cloud infrastructure can result in unexpected costs. Data engineers require a thorough understanding of usage requirements, the ability to accurately predict future needs, the time and expertise to monitor data warehouse environments to ensure efficient operation and the skills to take corrective action to address any inefficiencies. Even if data engineers have all these attributes and are initially successful, operational complexity is likely to grow as more workloads and users are added to the cloud data warehouse environment, resulting in operational overheads that may not have been initially identified and budgeted for. There is an argument that while using cloud data warehouses can reduce upfront capital expenditure on data warehouse hardware and software, it can also increase ongoing operational expenditure on data engineering staff required to monitor and optimize cloud data warehouse deployments and ensure they are running efficiently. These were amongst the concerns that prompted the development of Capital One Slingshot. The product provides data operations and financial governance capabilities, including monitoring Snowflake costs, performance and usage, with granular reporting based on business domain, project or individual user, and alerting to notify database administrators about usage or cost anomalies. Capital One Slingshot also provides data warehouse sizing recommendations to meet cost and performance requirements, identifies inefficient queries and remediation suggestions and sets schedules for automatic provisioning of warehouses. These capabilities are all available for Snowflake’s AI Data Cloud today and will soon be available in private preview for Databricks’ Data Intelligence Platform. Capital One Slingshot for Databricks will take advantage of Databricks’ system tables functionality to provide insight into the cost, performance and usage of Databricks’ Data Intelligence Platform. Enterprises using both Snowflake and Databricks will be able to monitor cost and performance information from both platforms in a single dashboard, providing the opportunity to compare use of the two platforms. The move to expand its addressable market is a wise one for Capital One Software. While it has a solid partnership with Snowflake, the data platform vendor is adding capabilities for observability and financial operations. Databricks also has some capabilities for cost management, so the breadth and depth of its functionality, combined with the ability to monitor cost and performance information from Snowflake and Databricks, will likely be a key differentiator for Capital One Slingshot going forward. I recommend that enterprises using Snowflake and concerned about cost containment and performance efficiency should evaluate the potential benefits of using Capital One Slingshot. Enterprises using both Snowflake and Databricks should keep a watchful eye on the company’s plans. Regards, Matt Aslett", "image": { "@type": "ImageObject", "url": "https://www.isg-research.net/hubfs/Capital_One_Offers_Cost_Controls_for_Cloud_Data_Warehouses.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "Matt Aslett", "url": "https://mattaslett.isg-research.net/author/matt-aslett", "sameAs": "https://www.linkedin.com/in/matt-aslett/" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "mattaslett.isg-research.net", "logo": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/ISG_Software_Research_Logo_Color@2x.png" } } }</script> <script type="application/ld+json"> { "@context": "http://schema.org", "@type": "BlogPosting", "headline": "The Increasing Importance of Open Table Formats", "mainEntityOfPage": "https://mattaslett.isg-research.net/the-increasing-importance-of-open-table-formats", "datePublished": "31/10/2024", "dateModified": "31/10/2024", "articleBody": "I previously wrote about the importance of open table formats to the evolution of data lakes into data lakehouses. The concept of the data lake was initially proposed as a single environment where data could be combined from multiple sources to be stored and processed to enable analysis by multiple users for multiple purposes. The first iteration of data lakes, based on Apache Hadoop and then object storage, fulfilled the first part of this equation, providing a relatively inexpensive way of storing large volumes of data from multiple enterprise applications and workloads, especially semi- and unstructured data that is unsuitable for storing and processing in a data warehouse. It was not until the addition of open table formats—specifically Apache Hudi, Apache Iceberg and Delta Lake—that data lakes truly became capable of supporting multiple business intelligence (BI) projects as well as data science and even operational applications and, in doing so, began to evolve into data lakehouses. Support for open table formats is now a critical feature for providers of analytic data platforms to enable the persistence and analysis of structured and unstructured data in object storage. Recent announcements have also highlighted the growing importance of open table formats to the persistence of streaming data, as well. I assert that by 2026, 9 in 10 current data lake adopters will be investing in data lakehouse architecture to improve the business value from the accumulated data. The high level of confidence is due in part to the critical importance of open table formats in delivering value from data. The benefits of open table formats are varied and numerous. Open table formats provide support for atomic, consistent, isolated and durable (ACID) transactions and create, read, update and delete (CRUD) operations on data stored in object storage. Support for ACID transactions and CRUD operations provides consistency and reliability guarantees that give enterprises the confidence that data lakehouse environments are suitable for analytics workloads traditionally associated with data warehouses. Widespread software provider support for open table formats also enables enterprises to use multiple data platform or SQL query engine products to access and query data in cloud object storage environments, providing flexibility and avoiding over-reliance on a single software provider for both compute and storage. The three primary open table formats have much in common: they are all developed by open-source foundations; they all write and store data in Apache Parquet files; and all are supported by multiple software providers. They are separate and competing formats with different origins, however. Apache Hudi was created at Uber in 2016 and became an Apache Software Foundation project in 2020, while Apache Iceberg was created at Netflix in 2017 and also became a project of the ASF the following year. Delta Lake was announced by Databricks in April 2019 and became a project of the Linux Foundation in October 2019, although its development remains dominated by employees of Databricks. Key Apache Hudi developers include employees of Uber, Alibaba and Onehouse, which was founded in 2021 by Vinoth Chandar, the original creator of the Hudi project at Uber, who continues to lead its development under the ASF. Apache Iceberg developers include employees of Netflix, Apple, AWS, Tabular and Dremio. Competition between formats can be good for innovation but also has the potential to force enterprises to make a commitment to one format over the others. A variety of recent initiatives have increased the potential for interoperability between open table formats. This has also lowered barriers to adoption by reducing concerns about the danger of enterprises becoming locked-in to a specific format and software provider, and the overall fragmentation of the market. In 2023, Onehouse announced an initiative to provide interoperability across table formats. Initially called Onetable, the project became Apache XTable in September 2024 and provides a lightweight translation layer to translate metadata between table formats without the need to duplicate or modify the data. The Delta Lake project also introduced interoperability with other table formats in 2023 via Delta Universal Format (or UniForm), which allows data stored in Delta tables to be read as if it were Apache Iceberg or Apache Hudi. Databricks went one step further in June 2024 by announcing the acquisition of Tabular, a data management provider founded in 2021 by the original creators of Apache Iceberg, adding significant Iceberg expertise to help expand the capabilities of UniForm. Given Databricks’ prior commitment to Delta Lake as a direct alternative to Apache Iceberg, the acquisition of Tabular was a significant change of strategy. The fact that the Apache Hudi and Delta Lake projects have both delivered interoperability with Apache Iceberg rather than vice versa is illustrative of the level of momentum behind the latter table format. This has also been highlighted recently by moves by streaming data providers—including Cloudera, Confluent, Redpanda and StreamNative—to add support for converting streaming data into Apache Iceberg tables for long-term persistence and analysis of event data. While it is perhaps still too early to crown Apache Iceberg as the open data format champion, there is clear potential for Apache Iceberg to become the lingua franca for storing and analyzing both transactional and event data. For existing data lake users, the adoption of open table formats is something of a no-brainer given the consistency and reliability guarantees they provide. I assert that by 2026, more than 8 in 10 data lake users will adopt emerging table formats to support transactional consistency when inserting, updating and deleting data in data lake environments. I recommend that all enterprises, whether data lake users or not, evaluate the potential advantages of open table formats in general, and Apache Iceberg in particular, to enable the persistence and analysis of transaction and event data in object storage. I also recommend that when evaluating software providers, enterprises must be sure to check whether support relates to read-only access to data in open table formats or extends to the ability to write data in open data formats as well. Regards, Matt Aslett", "image": { "@type": "ImageObject", "url": "https://www.isg-research.net/hubfs/The_Increasing_Importance_of_Open_Table_Formats.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "Matt Aslett", "url": "https://mattaslett.isg-research.net/author/matt-aslett", "sameAs": "https://www.linkedin.com/in/matt-aslett/" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "mattaslett.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": "Denodo Provides a Logical Approach to Data Management", "mainEntityOfPage": "https://mattaslett.isg-research.net/denodo-provides-a-logical-approach-to-data-management", "datePublished": "24/10/2024", "dateModified": "24/10/2024", "articleBody": "Although the terms data fabric and data mesh are often used interchangeably, I previously explained that they are distinct but complementary. Data fabric refers to technology products that can be used to integrate, manage and govern data across distributed environments, supporting the cultural and organizational data ownership and access goals of data mesh. Data fabric and data mesh are also both related to logical data management, which is the approach of providing virtualized access to data across an enterprise without the requirement to first extract and load it into a central repository. The popularity of data fabric and data mesh has highlighted the importance of software providers, such as Denodo, that utilize data virtualization to enable logical data management. Denodo was founded in 1999 and originated as a research project, led by Denodo’s founder and CEO Angel Vina at the University of A Coruna in Spain, that was focused on the challenges of managing increasing volumes of data generated and persisted by an increasingly disparate set of applications and data platforms. Rather than the traditional physical data management approach of replicating and integrating data into a centralized repository, Denodo was created to deliver logical data management, which is focused on providing virtualized access to data without changing its physical location. In addition to avoiding the cost and complexity of data movement, proponents of logical data management argue that it also encourages a focus on applying business context to the data by abstracting it from the physical environment in which it resides. This is said to help diminish challenges related to silos of data that limit data sharing and data-driven decision-making. Data silos are a perennial data management problem for enterprises, with almost three-quarters (73%) of participants in ISG Research’s Data Governance Benchmark Research citing disparate data sources and systems as a data governance challenge. Denodo remains a specialist data management software provider and in September 2023 announced that it had received a $336 million investment from asset management firm TPG. More recently, the company announced the launch of Denodo Platform 9, enhancing its massively parallel processing engine as well as adding support for natural language queries and query recommendations driven by artificial intelligence (AI). At the heart of the Denodo Platform is a data virtualization access layer that provides a virtual representation of physical data sources, including operational and analytic data platforms, applications, file systems and application programming interfaces. This logical abstraction of physical data sources enables enterprises to share access to and gain insight from data that might otherwise be limited to individual applications or business units. Denodo also offers query optimization and acceleration capabilities to deliver high-performance analytics, as well as support for business semantics and security and access controls. The ability to discover and access data via Denodo Platform is enabled by Denodo Data Catalog, which provides a search-based interface for finding data sources based on metadata or content, as well as metrics related to data popularity and usage. The company added a variety of new features with the launch of Denodo Platform 9 in June, designed to address requirements for intelligent applications and AI-driven integration. Among the new features in Denodo Platform 9 is support for natural language queries via integration with ChatGPT, Amazon Bedrock and other generative AI (GenAI) services, along with AI-driven query recommendations and support for retrieval augmented generation. Denodo Platform 9 also includes enhancements to its massively parallel distributed SQL query engine, based on the Presto open-source project, including support for Delta Lake and Iceberg tables to enable accelerated analysis of data in data lake environments. With Denodo Platform 9, the company also added enhanced federated governance support to enable the creation, management and reuse of data products by different business units and teams. Federated governance is a common element of data fabric and data mesh, alongside self-service data access. Both data fabric and data mesh are driving interest in logical data management and Denodo. Data fabric is a technical approach to automating data management and data governance in a distributed architecture, while data mesh is an organizational and cultural approach to distributed data ownership, access and governance. I assert that by 2027, three-quarters of enterprises will adopt data fabric technologies to facilitate the management and processing of data across multiple data platforms and cloud environments. While data virtualization is central to Denodo’s logical data management proposition, the company also offers flexibility to adapt to a variety of use cases and data integration requirements. For example, to support selective materialization of data from the source platform to accelerate queries via caching, full replication using extract, transform and load or extract, load and transform patterns, and streaming integration. The breadth and depth of Denodo Platform’s functionality is illustrated by its designation as a Leader in Capability in our 2024 Data Integration Buyers Guide. As such, I recommend that enterprises considering new data management providers include Denodo in their evaluations, especially—although not only—if the use case requires data virtualization. Regards, Matt Aslett", "image": { "@type": "ImageObject", "url": "https://www.isg-research.net/hubfs/Denodo_Provides_a_Logical_Approach_to_Data_Management.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "Matt Aslett", "url": "https://mattaslett.isg-research.net/author/matt-aslett", "sameAs": "https://www.linkedin.com/in/matt-aslett/" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "mattaslett.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": "Fauna’s Data Platform Combines Agility and Transaction Integrity", "mainEntityOfPage": "https://mattaslett.isg-research.net/faunas-data-platform-combines-agility-and-transaction-integrity", "datePublished": "16/10/2024", "dateModified": "16/10/2024", "articleBody": "As I noted in the 2024 Buyers Guide for Operational Data Platforms, intelligent applications powered by artificial intelligence have impacted the requirements for operational data platforms. These applications, infused with contextually relevant recommendations, predictions and forecasting, are driven by machine learning and generative AI. Traditionally, operational data platforms support applications used to run the business. Data is then extracted and loaded into analytic data platforms for analysis. The requirement for operational applications to support real-time interactivity and AI changes this dynamic, with the need for analytic processing of data in the operational data platform to deliver predictions and recommendations to accelerate operational decision-making. This has significant implications for emerging operational database providers, such as Fauna, that are positioned specifically to support the development of the next generation of applications. Fauna was founded in 2012 by software infrastructure engineers Evan Weaver and Matt Freels to develop the cloud-native transactional database product they would have liked to have had at their disposal in their former roles at what was then known as Twitter (now X). Weaver left Fauna in 2023, but Freels remains with the company as chief architect, leading the continued development of the company’s serverless document-relational database. Fauna raised a $27 million funding round from Madrona Venture Group, Addition Capital, GV and CRV, among others, in 2020, coinciding with the arrival of former Okta chief product officer Eric Berg as CEO and former Snowflake CEO Bob Muglia as chairman. Rather than targeting database administrators, Fauna focuses on application developers as the primary users for its managed database service which, it states, has been used to create more than 300,000 databases by over 80,000 development teams in 180 countries. Fauna’s database is typically used to support the development of software-as-a-service applications in industries such as retail and e-commerce, gaming and the Internet of Things. Given the rapid rise of generative AI, Fauna is also increasingly being used to support the development of intelligent operational applications designed to provide integration with large language models and other GenAI application services. Fauna describes its product as a document-relational database. As this terminology implies, the product combines the flexibility of the document data model with the consistency and query capabilities associated with relational databases. As I previously described, document databases provide developer agility and application flexibility. Documents can be mapped to objects in application code, providing intuitive ease of use for application developers. The fields used by a document can evolve in response to changing data and application requirements, making document databases suitable for rapid and agile development projects. I assert that by 2027, more than one-half of enterprises will adopt document databases to store data without fixed schema, facilitating rapid application development and business agility. Fauna combines the document model with strong consistency thanks to its Distributed Transaction Engine. The engine provides low latency, high availability and atomic, consistent, isolated and durable (or ACID) transactions across geographically distributed regions. I previously explained that the term distributed SQL has been widely adopted to describe operational data platform products that combine the benefits of the relational database model and native support for distributed cloud architecture, including resilience that spans multiple data centers and/or cloud regions. While Fauna delivers the latter, it does not support SQL. However, the Fauna Query Language is a TypeScript-like language that can express declarative relational queries and functional business logic in strongly consistent transactions. FQL also delivers native support for queries requiring joins of data across multiple documents as well as user-defined functions. Fauna was developed with native support for event streaming and is delivered as a serverless managed service, which eliminates the need for users to install, configure and manage the database and any associated infrastructure. As I previously explained, although serverless databases are not without challenges, application programming interface-based interactions with serverless databases have the potential to enhance developer productivity and lower the learning curve for developing new, data-driven applications. Fauna also recently delivered Fauna Schema to enable developers to define and manage database schema, including Fauna Schema Language to define and manage database schemas as code with version control, CI/CD pipeline integration and schema enforcement capabilities. Fauna’s API delivery model also lends itself to integration with LLMs and other GenAI cloud services in support of intelligent operational applications. The emergence of intelligent applications does not eradicate the use of specialist analytic data platforms, such as data warehouses and data lakehouses. It does, however, impact the requirements for operational data platforms to support real-time analytic functionality for recommendations and predictions. Fauna is positioning its database as a system of record for GenAI applications, storing and processing an enterprise’s user and application data alongside a vector database, which is used to store vector embeddings generated from the enterprise data that can be used to complement and improve trust in GenAI applications. Additionally, the company has delivered its own Fauna AI Assistant, providing a natural language interface to help developers work with FQL via access to documentation, code samples and other content. I recommend that enterprises considering data platform providers for the development of next-generation operational applications include Fauna in evaluations. The company has a relatively low profile among emerging database providers, and its lack of support for the SQL standard will give some enterprises pause for thought, but it has already been widely adopted. For developers looking for a combination of agility and transactional consistency, Fauna’s document-relational approach could be ideal. Regards, Matt Aslett", "image": { "@type": "ImageObject", "url": "https://www.isg-research.net/hubfs/Faunas_Data_Platform_Combines_Agility_and_Transaction_Integrity.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "Matt Aslett", "url": "https://mattaslett.isg-research.net/author/matt-aslett", "sameAs": "https://www.linkedin.com/in/matt-aslett/" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "mattaslett.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": "Collibra Provides a Platform for Data Intelligence", "mainEntityOfPage": "https://mattaslett.isg-research.net/collibra-provides-a-platform-for-data-intelligence", "datePublished": "08/10/2024", "dateModified": "08/10/2024", "articleBody": "As I recently noted, the term “data intelligence” has been used by multiple providers across analytics and data for several years and is becoming more widespread as software providers respond to the need to provide enterprises with a holistic view of data production and consumption. I assert that through 2027, three-quarters of enterprises will be engaged in data intelligence initiatives to increase trust in their data by leveraging metadata to understand how, when and where data is used in their organization, and by whom. One of the first software providers to embrace the term data intelligence was Collibra. Although the company remains best known for its data governance and data catalog functionality, Collibra has created a platform for data intelligence through a combination of internal development and acquisitions that provides functionality for the governance of data and artificial intelligence (AI) applications, as well as data lineage, data quality and data privacy. Collibra was founded in 2008 by Chief Executive Officer Felix Van de Maele and Chief Data Citizen Stijn Christiaens. A spin-off from the Semantics Technology and Applications Research Laboratory at the Vrije Universiteit in Brussels, Belgium, Collibra quickly established itself as a key provider in the data governance sector and the emerging market for data catalog software products. In addition to providing the core functionality for standardizing data governance and enabling self-service data access across a distributed enterprise, Collibra was early to identify the need to provide customers with information about how, when and where data is being produced and consumed across an enterprise. This is the essence of the concept of Data Intelligence and is combined with the company’s core functionality for data integration, data governance, data quality, data lineage, data privacy and AI governance in the Collibra Data Intelligence Platform. Collibra is funded by investors including Sequoia Capital Global Equities, Sofina, Tiger Global Management, Battery Ventures, CapitalG, Dawn Capital, Durable Capital Partners, ICONIQ Capital and Index Ventures. Together, these investors provided Collibra with a $250 million Series G funding round, announced in November 2021, that valued the company at $5.25 billion. AI and data platforms provider Snowflake also became an investor in early 2022. Data intelligence is fundamental to strategic data democratization initiatives to provide data analysts and business users with governed self-service access to data. Removing barriers that prevent or delay users from gaining access to data enables it to be treated as a product that is generated and consumed internally by workers or externally by partners and customers. For many enterprises, self-service access to data has long been a goal, but few have achieved it. Only 15% of participants in our ISG Analytics and Data Benchmark Research are very comfortable allowing business users to work with data that has not been integrated or prepared by IT. Many enterprises see data catalogs as the solution to data democratization because they provide a central repository of the data used across an enterprise, along with guided data discovery capabilities and natural language search. Self-service access to data is only truly valuable if users can trust the data they have access to, however. As such, data intelligence requires a combination of data catalog, data governance, data quality and data privacy capabilities. Collibra’s initial focus on data governance and data cataloging has been expanded in recent years through a number of acquisitions and internal development, all of which are available via the Collibra Data Intelligence Platform. The company acquired automated technical lineage provider SQLdep in 2019 before launching what is now Collibra Data Lineage the following year. In 2021, it acquired predictive data quality software provider OwlDQ to form the basis of Collibra Data Quality & Observability. That was followed in 2022 by the introduction of Collibra Protect for data access policy development and management as well as Collibra Data Marketplace capabilities for self-service data discovery. Collibra also announced the acquisition of Husprey in 2023 for its SQL data notebook functionality. This was the basis for Collibra Data Notebook, which enables users to query and visualize data residing in data platforms governed by Collibra without having to exit Collibra Data Intelligence Platform, and publish notebooks containing queries and visualizations as assets to be consumed by other users. Most recently, Collibra expanded its purview to cataloging AI use cases and governing the use of data by AI applications with the launch of Collibra AI Governance in February 2024. In addition to the core functionality, another component of Collibra Data Intelligence Platform is Collibra Usage Analytics, which provides enterprises with insights on their use of Collibra Data Intelligence Cloud. We see this as critical to the concept of data intelligence since it provides enterprises with information about how, when and where data is being produced and consumed, and as such, it could benefit from greater emphasis by Collibra as a differentiating capability. In the interim, I recommend that enterprises include Collibra in their evaluations for a strategic investment in data intelligence, as well as more tactical data governance, data cataloging, data quality and data lineage use cases. Regards, Matt Aslett", "image": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/Collibra_Provides_a_Platform_for_Data_Intelligence.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "Matt Aslett", "url": "https://mattaslett.isg-research.net/author/matt-aslett", "sameAs": "https://www.linkedin.com/in/matt-aslett/" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "mattaslett.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 as a Product: Needs and Requirements", "mainEntityOfPage": "https://mattaslett.isg-research.net/data-as-a-product-needs-and-requirements", "datePublished": "02/10/2024", "dateModified": "02/10/2024", "articleBody": "I previously wrote about data mesh as a cultural and organizational approach to distributed data processing. Data mesh has four key principles—domain-oriented ownership, data as a product, self-serve data infrastructure and federated governance—each of which is being widely adopted. I assert that by 2027, more than 6 in 10 enterprises will adopt technologies to facilitate the delivery of data as a product as they adapt their cultural and organizational approaches to data ownership in the context of data mesh, for example. Each of the four principles is also gaining momentum outside the context of data mesh, however, with data as a product attracting attention as it enables enterprises to streamline and accelerate the delivery of analytics and data initiatives. The terms “data as a product” and “data product” are often used interchangeably but have distinct meanings. Data as a product is the process of applying product thinking to data initiatives to ensure that the outcome—the data product—is designed to be shared and reused for multiple use cases across the business. The format of the outcome is not a defining characteristic of the data product, which could be a business intelligence (BI) dashboard (and the underlying data warehouse), a decision intelligence application, an algorithm or artificial intelligence/machine learning (AI/ML) model, or a custom-built operational application. All of these have traditionally been delivered on a project-by-project basis, often by a centralized IT team, with little or no effort to ensure the data can easily be accessed and used for other purposes without duplication. The defining characteristic of a data product is the application of product thinking in the development process to ensure that the outcome is designed to be delivered as a reusable asset that can be discovered and consumed by others on a self-service basis. The principle of domain-oriented ownership is also important to the development of data products. Domain-oriented ownership makes business departments responsible for managing the data generated by their applications and making it available to others. Data as a product is primarily concerned with the sharing of data within an enterprise, rather than selling data products to external parties (which is addressed by data as a service), although the two are complementary. The application of product thinking ensures that consumers of data products are treated as customers. It means that data owners must be aware of data requirements from across the enterprise to understand how the resulting data product will be used, as well providing instructions and service-level commitments so data consumers can feel confident that the data product is up-to-date and of sufficient quality to be relied on for business decision-making. This is fulfilled through the development of data contracts, which are created alongside the data product and provide an agreement between the data owner and the data consumer about the nature of the data product. Data contracts should include a description of the data product, defining the structure, format and meaning of the data, as well as licensing terms and usage recommendations. A data contract should also define data quality and service-level key performance indicators and commitments. Enterprise interest in data as a product has driven the emergence of a new category of software designed to provide an environment for the development, publication and consumption of data products. Key capabilities for these data product platforms include a dedicated interface for the development of data products with versioning, change tracking and data lineage capabilities, as well templates for the classification of data products and data contracts. A data product platform also needs to provide a dedicated interface for the self-service discovery and consumption of data products and their related data contracts. As with any product, consumers of data products should be able to provide feedback, comments and ratings as well as request improvements or new products. Data owners also require the functionality to view and manage requests for data product modifications and the development of new data products, as well as to monitor data product usage and performance metrics. Some data product platforms will also offer functionality to support the sale and licensing of data as a service to external partners or customers. The development of any product relies on a complex supply chain of components, and data products are no exception. As such, data product platforms need to provide native or integrated data operations (DataOps) functionality, including the development and testing of data pipelines, as well as data orchestration and data observability functionality to provide the all-important information related to the validity, integrity, quality and lineage of the underlying data. Making data available as a product on a self-service basis also increases the importance of agreed-upon data definitions and entity resolution. Only 16% of participants in our Data Governance Benchmark Research say data is well-trusted in their organization, while one-half cite agreement on the definitions of data as a primary concern in managing data effectively. It is therefore important that data product platforms provide native or integrated functionality for data governance, data cataloging and master data management. Enterprises adopting data as a product stand to benefit from interoperability and the accelerated delivery of data products that more rapidly provide business stakeholders with high-quality trusted data. I recommend that all enterprises evaluate the principle of data as a product and platforms that enable the development and delivery of data products. The capabilities required for delivering data products will be covered in detail in our forthcoming Data Operations Buyers Guide research, which is being expanded this year to assess platforms and tools for Data Products, alongside Data Pipelines, Data Observability, Data Orchestration and overall DataOps. Regards, Matt Aslett", "image": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/Data_as_a_Product_Needs_and_Requirements.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "Matt Aslett", "url": "https://mattaslett.isg-research.net/author/matt-aslett", "sameAs": "https://www.linkedin.com/in/matt-aslett/" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "mattaslett.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": "Snowflake Offers a Platform for AI as well as Data", "mainEntityOfPage": "https://mattaslett.isg-research.net/snowflake-offers-a-platform-for-ai-as-well-as-data", "datePublished": "19/09/2024", "dateModified": "19/09/2024", "articleBody": "As I explained in our recent Buyers Guide for Data Platforms, the popularization of generative artificial intelligence (GenAI) has had a significant impact on the requirements for data platforms in the last 18 months. While there is an ongoing need for data platforms to support data warehousing workloads involving analytic reports and dashboards, there is increasing demand for analytic data platform providers to add dedicated functionality for data engineering, including the development, training and tuning of machine learning (ML) and GenAI models. Snowflake is a prime example. The company remains best known as a cloud data platform provider for data warehousing workloads but, like many other data platform providers, has improved its support for AI workloads in the last year. In particular, the company made several announcements at its Snowflake Summit 2024 customer event in June that highlighted considerable advancements to its capabilities in relation to AI and GenAI. Snowflake was founded in 2012 to build a business around its cloud-based data warehouse with built-in data-sharing capabilities. Snowflake has expanded its reach over the years to address data engineering and data science, and long ago moved beyond being seen as just a cloud data warehouse. The company was rated Exemplary in our recent Buyers Guide for Analytic Data Platforms and a Provider of Assurance in our Buyers Guide for AI Platforms. Snowflake is not alone in adding support for AI workloads to its data platform. I assert that through 2026, analytic data platform software providers will increase their native support for AI and ML algorithms, functions and tools to enable in-database data science. The extent to which the company is positioning itself for AI and GenAI workloads was a focus at Snowflake Summit this year, with Snowflake describing its platform as an AI Data Cloud. The company did more than just add the letters A and I to its former Data Cloud branding, however, as it also made numerous announcements that justified its increased emphasis on AI. These included improvements to its Snowflake Cortex managed service for developing applications based on large language models (LLMs), as well as its Snowflake ML offering for training and operationalizing ML models for predictive analytics. Snowflake also enhanced its capabilities for data management and data governance, which will improve support for both analytics and AI workloads. Snowflake has been hugely successful in helping to drive adoption of cloud-based analytic databases, establishing itself as one of the major cloud data platforms for SQL-based data warehousing. The launch of the Snowpark developer environment in 2020 was significant in widening the target workloads by enabling data engineers, data scientists and developers to execute custom Python, Java and Scala code against data in Snowflake. This was followed in 2023 by the launch of Snowpark Container Services, which enables customers to run their own choice of third-party software, including programming languages, data science libraries and GenAI models, on the platform. This avoids the need for users to move data out of Snowflake for processing and potentially reduces complexity and infrastructure resource requirements. I noted in the wake of the Snowflake Summit customer event last year that the company’s plans for native support for GenAI were comparatively nascent relative to some rival providers that had been quicker to add support for GenAI models and development. Snowflake’s GenAI strategy took a leap forward in late 2023 with the launch of the Cortex AI development platform, which provides users with access to LLMs, AI models and vector search functionality. That was followed in April by the delivery of Snowflake’s own Arctic family of LLMs. At Snowflake Summit 2024, the company announced a number of enhancements to Cortex, including the preview of Snowflake Cortex Analyst, which provides an interface for natural language querying of structured data by business users, and Snowflake Cortex Search, which provides developers with a combination of keyword and vector search to support enterprise search and retrieval augmented generation workloads. Natural language search and query are amongst the most popular early use cases for GenAI, with 99% of participants in the ISG Market Lens AI Study having seen positive outcomes from natural language search and 97% having seen positive outcomes from the interpretation of data. Snowflake also added Snowflake Cortex Guard to automatically identify inappropriate language in the output of LLMs. In addition to GenAI enhancements, Snowflake also expanded the MLOps capabilities of its Snowflake ML offering, including the general availability of Snowflake Model Registry and the preview of Snowflake Feature Store and ML Lineage. Snowflake also announced several data management and governance enhancements that will support both analytic and AI workloads, including the general availability of support for Apache Iceberg Tables and the launch of the Polaris Catalog open-source project built on the Apache Iceberg REST protocol. Polaris is designed to provide a vendor-neutral technical catalog for Iceberg tables but complements the data governance and discovery capabilities of Snowflake Horizon, which was also recently enhanced with the preview availability of Internal Marketplace, which enables the creation, sharing and consumption of data products. Many of the new capabilities announced at Snowflake Summit 2024 remain in private or public preview. As such, they were not included for assessment in our recent Buyers Guide reports. They nevertheless represent a significant enhancement of the company’s native support for AI and GenAI that will improve our evaluation of Snowflake in future reports. As a result, I recommend that enterprises include Snowflake in their evaluations for AI platforms, as well as analytic data platforms. Regards, Matt Aslett", "image": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/Snowflake_Offers_a_Platform_for_AI_as_well_as_Data.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "Matt Aslett", "url": "https://mattaslett.isg-research.net/author/matt-aslett", "sameAs": "https://www.linkedin.com/in/matt-aslett/" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "mattaslett.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": "Harness Events to Process Data at the Speed of Business", "mainEntityOfPage": "https://mattaslett.isg-research.net/harness-events-to-process-data-at-the-speed-of-business", "datePublished": "17/09/2024", "dateModified": "17/09/2024", "articleBody": "The final of the men’s 100 meters at the Paris Olympics this summer was a reminder that being successful requires not just being fast but performing at the right time. Being fast is obviously a prerequisite for participating in an Olympic 100-meter final, and all the competitors finished the race in under 10 seconds, with just 0.12 seconds separating the first man from the last. While all the athletes were fast, what separated the winner of the gold medal—USA’s Noah Lyles—was execution. He was in last place at 50 meters, but as others were slowing down in the second half of the race, Lyles was accelerating towards the finish line. Lyles was not even the first person to cross the finish line, but since athletics measures at the torso rather than at the toes, Lyles’ lean for the line secured him victory. Execution at speed was the difference between being an Olympic 100-meter finalist and the Olympic 100-meter champion. There is a lesson to be learned for enterprises as they strive to make use of analytics and data to out-compete their rivals. Just as all 100-meter runners are fast, all enterprises strive to be data-driven. Making decisions based on data is not enough to be successful, however. The winners are those that can process and act upon data at the speed of business. This is easier said than done, given the overwhelming historical reliance on batch data processing. As I have previously stated, the execution of business events has always occurred in real time. Batch data processing is an artificial construct driven by the limitations of traditional data processing capabilities that require enterprises to process data minutes, hours or even days after an event. Processing and acting on data at the speed of business necessitates a change of approach by enterprises to make real-time data processing a central component of their analytics and data strategy, rather than an exception. Streaming and events focuses on the uninterrupted management, processing and analysis of data generated by applications, systems and devices on a continuous basis. The processing of business events in real time has been adopted as a standard approach to data processing in industry segments with high-performance, real-time requirements such as financial services and telecommunications. In many other industries, however, the reliance on batch data processing is so entrenched that streaming and events has primarily been seen as a niche requirement, separate from the primary focus on batch processing of data at rest. Less than one-quarter (22%) of enterprises participating in our Analytics and Data Benchmark Research are currently analyzing data in real time. Enterprises that fail to process and analyze data in real time run the risk of failing to operate at the pace of the real world. The pressure on enterprises to improve their ability to process and analyze data in real time is being exacerbated by increased demand for intelligent operational applications infused with the results of analytic processes, such as personalization and artificial intelligence (AI)-driven recommendations. AI-driven intelligent applications require a new approach to data processing that enables real-time performance of machine learning (ML) on operational data to deliver instant, relevant information for accelerated decision-making. As demand for real-time interactive applications becomes more pervasive, the processing of streaming and events is becoming a more mainstream pursuit, aided by the proliferation of streaming and event technologies, which have lowered the cost and technical barriers to developing new applications that take advantage of data in motion. Fortunately, for enterprises that are yet to embrace streaming and events, the core enabling technologies are mature and readily available. The fundamental enabler of an event-driven architecture is the event broker, which is responsible for handling the communication of messages between applications and systems. Messages can be published sequentially, in the form of message queues, or as a stream: a continuous flow of event messages. A network of event brokers, in combination with event-management software for discovering, cataloging, governing and securing events and event-driven applications, can enable enterprises to act on the event data as it is generated through streaming integration (including aggregation, transformation, enrichment and ingestion of event streams) and streaming analytics, which uses compute engines to analyze streams of event data to deliver actionable insight in real time. Processing and analyzing event data in isolation is valuable. As I have previously stated, however, success with streaming data relies on the holistic management and governance of data in motion and data at rest. In recent years, we have seen the emergence of streaming databases designed to provide a single environment for continually processing streams of event data using SQL queries and real-time materialized views. At the same time, we have also seen streaming and event specialist providers improve their capabilities for the persistence of event data in a data warehouse, data lake or cloud storage for batch processing of historical event data. I assert that by 2026, more than three-quarters of enterprises’ standard information architectures will include streaming data and event processing, allowing enterprises to be more responsive and provide better customer experiences. As more enterprises adopt event-driven architecture, the addition of capabilities for the persistence and processing of historical event data increases the potential for streaming and event specialists to stake a claim to be considered an enterprise’s primary data platform provider, rather than being utilized for a limited set of use cases. Accelerating this potential is the rise of AI-driven operational and analytic applications. Providing real-time interactivity is table stakes for operational applications. Enterprises can differentiate user experiences with real-time, AI-driven functionality. Doing so requires AI models to have access to up-to-date data via streams of events as they are generated in real time, as well as the ability to incorporate model inferencing into streaming analytics pipelines. Enterprises with an over-reliance on batch data processing will not be able to match those that are able to harness real-time data as it is generated. The ability to execute at the speed of business by processing and acting on events as they occur will be the difference between competing and winning with analytics and data. I recommend that enterprises evaluating their current and future data architecture requirements consider streaming and event technologies alongside more traditional batch-oriented data platforms to provide a holistic view of all data, in motion and at rest. Regards, Matt Aslett", "image": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/Harness_Events_to_Process_Data_at_the_Speed_of_Business.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "Matt Aslett", "url": "https://mattaslett.isg-research.net/author/matt-aslett", "sameAs": "https://www.linkedin.com/in/matt-aslett/" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "mattaslett.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": "IBM Provides Architectural Choice for Data Platforms", "mainEntityOfPage": "https://mattaslett.isg-research.net/ibm-provides-architectural-choice-for-data-platforms", "datePublished": "27/08/2024", "dateModified": "27/08/2024", "articleBody": "Enterprises face a bewildering level of choice in relation to data platforms, as evidenced by the number of software providers and products assessed in our recent Data Platforms Buyers Guide. There are numerous data platform providers and products to choose from, but also a diverse array of functional and architectural options. Is the workload primarily operational or analytic? Will it be deployed on-premises or in the cloud? Should it be distributed or centralized? Data warehouse or data lakehouse? Data fabric or data grid? These are often presented as binary choices, but they very rarely are. Many enterprises are looking for data platforms that support the flexibility to address a combination of functional and architectural options. Multi-product providers, such as IBM, are arguably at an advantage, if they can clearly articulate how and when their different options work together. At its recent Think 2024 customer event, IBM announced several enhancements to its watsonx.data lakehouse offering and articulated how it can coexist with IBM Cloud Pak for Data as part of a data fabric architecture. IBM unveiled its watsonx brand at Think 2023, delivering an artificial intelligence (AI) and data platform designed to address the AI development life cycle, data storage processing and AI governance. The watsonx platform consists of three components. watsonx.ai is a development environment running on the Red Hat OpenShift Container Platform for data scientists to train, validate, tune and deploy generative AI (GenAI) and machine learning (ML) models. watsonx.data is a data lakehouse environment that enables users to store data in object storage and process it using a variety of query engines. Our research indicates that this approach is quickly becoming mainstream, with the use of object stores for analytics in production in more than one-half of enterprises. watsonx.governance provides an environment to collaboratively manage, catalog and monitor AI models in the context of ethical concerns and regulatory requirements. Together, these provide a comprehensive platform designed to support an enterprise’s strategic adoption of ML and foundation models, either standalone or in combination with IBM’s AI consulting services. IBM made numerous announcements at Think 2024 related to watsonx, including the preview of the watsonx BI Assistant natural language interface. Since it has become clear that data processing and management are essential to delivering trust in data for AI, I will focus the remainder of this perspective on recent enhancements at the data layer. The concept of the data lake emerged over a decade ago in response to the demand for analytic data platforms that could economically store and process large volumes of raw data, either in the cloud or on premises. The concept has rapidly evolved into the data lakehouse in recent years, with the addition of query processing, data cataloging, transaction processing guarantees, and table format functionality normally associated with data warehouse environments. The data lakehouse is now accepted as a standard analytic data platform architecture, especially for storing and processing the large volumes of data required for strategic AI initiatives. I assert that by 2026, 9 in 10 current data lake adopters will be investing in data lakehouse architecture to improve the business value generated from the accumulated data. IBM watsonx.data is on IBM Cloud, Amazon Web Services and Microsoft Azure, as well as on premises, and enables users to store large volumes of data in object storage using the Apache Iceberg table format for transactional consistency. The data can be processed using a choice of query engines, including Apache Spark and Presto, as well as IBM’s own Db2 and Netezza. At Think 2024, IBM announced that it had updated watsonx.data with support for Presto 2.0, including Presto C++, co-developed by IBM employees as part of the open-source Linux Foundation project, to run Presto with the Velox open source C++ acceleration library for improved performance. IBM also announced the addition of IBM Data Gate for watsonx to enable access to data in IBM zSystems environments, facilitating the development of AI models using transactional mainframe data. IBM also announced the integration of a semantic layer into IBM Knowledge Catalog, embeddable within watsonx.data, to accelerate data discovery and enrichment via semantic search capabilities. Previously, IBM had also announced the addition of vector database capabilities based on the open-source Milvus database, enabling the use of vector search to augment GenAI with context from enterprise content and data via retrieval-augmented generation. As I previously noted, while some of the functionality delivered in watsonx is new, some is also available via other IBM products, such as IBM Cloud Pak for Data. It has subsequently become clearer how these products relate to each other, with the IBM watsonx.data license including entitlements to IBM Cloud Pak for Data platform software, as well as other prerequisites such as the Red Hat OpenShift Container Platform. IBM recently announced Cloud Pak for Data 5.0, including a new Immersive Experience feature which enables administrators to easily toggle between the dedicated user experiences for Cloud Pak for Data and watsonx, facilitating the use of watsonx.data as a data lakehouse within a larger data fabric architecture supported by Cloud Pak for Data and its combination of data integration, data governance, data observability, master data management and data lineage functionality. The Immersive Experience feature also provides access to IBM Data Product Hub, which was unveiled at Think 2024, to facilitate the development and sharing of data products. Included as part of IBM Cloud Pak for Data 5.0 and integrated with IBM watsonx.data, IBM Data Product Hub is built on Watson Knowledge Catalog to enable and control data access based on metadata and governance rules, with additional functionality for defining and enforcing data contracts between data producers and consumers. As such, IBM Data Product Hub has the potential to support the holistic view of data production and consumption that we see as critical to data intelligence. IBM has also boosted its data fabric capabilities with the recently closed acquisition of the StreamSets real-time data integration capabilities from Software AG, along with the webMethods Integration Platform as a Service. These investments complement IBM’s previous acquisitions of Databand.ai for data observability in 2022 and Manta for data lineage in 2023, as well as the company’s ongoing internal development of data processing and management capabilities. The breadth of functionality available from IBM has the potential to be overwhelming for would-be customers, but the product positioning is becoming clearer with watsonx.data and Cloud Pak for Data as the delivery vehicles for data lakehouse and data fabric environments, respectively, and Cloud Pak for Data’s Immersive Experience feature facilitating co-existence. I recommend that any enterprises considering their data platform options include IBM in their evaluations. The company was rated Exemplary in our recent Data Platforms Buyers Guides for Operational Data Platforms, Analytic Data Platforms and overall Data Platforms. 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Matt Aslett's Analyst Perspectives