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David Menninger's Analyst Perspectives | Data
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"> <br> <h1 class="analyst-header-title">David Menninger's Analyst Perspectives</h1> <!-- <h1><span id="hs_cos_wrapper_name" class="hs_cos_wrapper hs_cos_wrapper_meta_field hs_cos_wrapper_type_text" style="" data-hs-cos-general-type="meta_field" data-hs-cos-type="text" >David Menninger</span>123tests</h1>--> </div> </div> </div> </div> <div class="content-wrapper-listing blog-single"> <div class="blog-content-wrapper"> <div> <!-- class="blog-index" --> <!----> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://davidmenninger.isg-one.com/stream-and-event-processing-require-real-time-analytics">Stream and Event Processing Require Real-Time Analytics</a></h2> <div class="author-meta"> Posted by <a href="https://davidmenninger.isg-one.com/author/david-menninger"> David Menninger </a> on <span class="post-date">25 October 2023 </span> </div> <!-- <hr> --> <hr> </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://davidmenninger.isg-one.com/hs-fs/hubfs/Stream_and_Event_Processing_Require_Real-Time_Analytics.png?width=200&name=Stream_and_Event_Processing_Require_Real-Time_Analytics.png" width="200" !important: srcset="https://davidmenninger.isg-one.com/hs-fs/hubfs/Stream_and_Event_Processing_Require_Real-Time_Analytics.png?width=100&name=Stream_and_Event_Processing_Require_Real-Time_Analytics.png 100w, https://davidmenninger.isg-one.com/hs-fs/hubfs/Stream_and_Event_Processing_Require_Real-Time_Analytics.png?width=200&name=Stream_and_Event_Processing_Require_Real-Time_Analytics.png 200w, https://davidmenninger.isg-one.com/hs-fs/hubfs/Stream_and_Event_Processing_Require_Real-Time_Analytics.png?width=300&name=Stream_and_Event_Processing_Require_Real-Time_Analytics.png 300w, https://davidmenninger.isg-one.com/hs-fs/hubfs/Stream_and_Event_Processing_Require_Real-Time_Analytics.png?width=400&name=Stream_and_Event_Processing_Require_Real-Time_Analytics.png 400w, https://davidmenninger.isg-one.com/hs-fs/hubfs/Stream_and_Event_Processing_Require_Real-Time_Analytics.png?width=500&name=Stream_and_Event_Processing_Require_Real-Time_Analytics.png 500w, https://davidmenninger.isg-one.com/hs-fs/hubfs/Stream_and_Event_Processing_Require_Real-Time_Analytics.png?width=600&name=Stream_and_Event_Processing_Require_Real-Time_Analytics.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p>In my past perspectives, I’ve written about the evolution from <a href="https://davidmenninger.ventanaresearch.com/historical-databases-becoming-a-thing-of-the-past" rel="noopener" target="_blank">data at rest to data in motion</a> and the fact that <a href="https://davidmenninger.ventanaresearch.com/dont-rely-on-dashboards-for-real-time-analytics" rel="noopener" target="_blank">you can’t rely on dashboards for real-time analytics</a>. Organizations are becoming more and more event-driven and operating based on streaming data. As well, analytics are becoming more and more intertwined with operations. More than one-fifth of organizations (22%) describe their analytics workloads as real time in our Data and Analytics Benchmark Research and nearly half (47%) of...</p> </div> <a href="https://davidmenninger.isg-one.com/stream-and-event-processing-require-real-time-analytics"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://davidmenninger.isg-one.com/topic/analytics">Analytics</a>, <a href="https://davidmenninger.isg-one.com/topic/business-intelligence">Business Intelligence</a>, <a href="https://davidmenninger.isg-one.com/topic/data">Data</a>, <a href="https://davidmenninger.isg-one.com/topic/digital-technology">Digital Technology</a>, <a href="https://davidmenninger.isg-one.com/topic/streaming-analytics">Streaming Analytics</a>, <a href="https://davidmenninger.isg-one.com/topic/analytics-data">Analytics & Data</a>, <a href="https://davidmenninger.isg-one.com/topic/streaming-data-events">Streaming Data & Events</a> <hr> </div> <!----> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://davidmenninger.isg-one.com/reversing-the-etl-process-improves-data-operations">Reversing the ETL Process Improves Data Operations</a></h2> <div class="author-meta"> Posted by <a href="https://davidmenninger.isg-one.com/author/david-menninger"> David Menninger </a> on <span class="post-date">19 September 2023 </span> </div> <!-- <hr> --> <hr> </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://davidmenninger.isg-one.com/hs-fs/hubfs/Reversing_the_ETL_Process_Improves_Data_Operations.png?width=200&name=Reversing_the_ETL_Process_Improves_Data_Operations.png" width="200" !important: srcset="https://davidmenninger.isg-one.com/hs-fs/hubfs/Reversing_the_ETL_Process_Improves_Data_Operations.png?width=100&name=Reversing_the_ETL_Process_Improves_Data_Operations.png 100w, https://davidmenninger.isg-one.com/hs-fs/hubfs/Reversing_the_ETL_Process_Improves_Data_Operations.png?width=200&name=Reversing_the_ETL_Process_Improves_Data_Operations.png 200w, https://davidmenninger.isg-one.com/hs-fs/hubfs/Reversing_the_ETL_Process_Improves_Data_Operations.png?width=300&name=Reversing_the_ETL_Process_Improves_Data_Operations.png 300w, https://davidmenninger.isg-one.com/hs-fs/hubfs/Reversing_the_ETL_Process_Improves_Data_Operations.png?width=400&name=Reversing_the_ETL_Process_Improves_Data_Operations.png 400w, https://davidmenninger.isg-one.com/hs-fs/hubfs/Reversing_the_ETL_Process_Improves_Data_Operations.png?width=500&name=Reversing_the_ETL_Process_Improves_Data_Operations.png 500w, https://davidmenninger.isg-one.com/hs-fs/hubfs/Reversing_the_ETL_Process_Improves_Data_Operations.png?width=600&name=Reversing_the_ETL_Process_Improves_Data_Operations.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p>At one point, analytics and business intelligence were considered non-mission critical activities. One of the primary concerns in designing analytics systems was to ensure they didn’t interfere with or draw computing resources away from operational systems. But today, analytical systems are integral to many aspects of operations. More than 9 in 10 participants in our <a href="https://www.ventanaresearch.com/benchmark/analytics/analytics_and_data/research_report/2022" rel="noopener" target="_blank">Analytics and Data Benchmark Research</a> reported analytics had improved activities and processes. However, most analytics and BI...</p> </div> <a href="https://davidmenninger.isg-one.com/reversing-the-etl-process-improves-data-operations"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://davidmenninger.isg-one.com/topic/analytics">Analytics</a>, <a href="https://davidmenninger.isg-one.com/topic/business-intelligence">Business Intelligence</a>, <a href="https://davidmenninger.isg-one.com/topic/data-management">Data Management</a>, <a href="https://davidmenninger.isg-one.com/topic/data">Data</a>, <a href="https://davidmenninger.isg-one.com/topic/digital-technology">Digital Technology</a>, <a href="https://davidmenninger.isg-one.com/topic/data-operations">data operations</a>, <a href="https://davidmenninger.isg-one.com/topic/analytics-data">Analytics & Data</a> <hr> </div> <!----> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://davidmenninger.isg-one.com/hitachi-vantara-dataops-improves-analytics-and-decision-making">Hitachi Vantara DataOps Improves Analytics and Decision-Making</a></h2> <div class="author-meta"> Posted by <a href="https://davidmenninger.isg-one.com/author/david-menninger"> David Menninger </a> on <span class="post-date">21 March 2023 </span> </div> <!-- <hr> --> <hr> </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://davidmenninger.isg-one.com/hs-fs/hubfs/Hitachi_Vantara_DataOps_Improves_Analytics_and_Decision-Making.png?width=200&name=Hitachi_Vantara_DataOps_Improves_Analytics_and_Decision-Making.png" width="200" !important: srcset="https://davidmenninger.isg-one.com/hs-fs/hubfs/Hitachi_Vantara_DataOps_Improves_Analytics_and_Decision-Making.png?width=100&name=Hitachi_Vantara_DataOps_Improves_Analytics_and_Decision-Making.png 100w, https://davidmenninger.isg-one.com/hs-fs/hubfs/Hitachi_Vantara_DataOps_Improves_Analytics_and_Decision-Making.png?width=200&name=Hitachi_Vantara_DataOps_Improves_Analytics_and_Decision-Making.png 200w, https://davidmenninger.isg-one.com/hs-fs/hubfs/Hitachi_Vantara_DataOps_Improves_Analytics_and_Decision-Making.png?width=300&name=Hitachi_Vantara_DataOps_Improves_Analytics_and_Decision-Making.png 300w, https://davidmenninger.isg-one.com/hs-fs/hubfs/Hitachi_Vantara_DataOps_Improves_Analytics_and_Decision-Making.png?width=400&name=Hitachi_Vantara_DataOps_Improves_Analytics_and_Decision-Making.png 400w, https://davidmenninger.isg-one.com/hs-fs/hubfs/Hitachi_Vantara_DataOps_Improves_Analytics_and_Decision-Making.png?width=500&name=Hitachi_Vantara_DataOps_Improves_Analytics_and_Decision-Making.png 500w, https://davidmenninger.isg-one.com/hs-fs/hubfs/Hitachi_Vantara_DataOps_Improves_Analytics_and_Decision-Making.png?width=600&name=Hitachi_Vantara_DataOps_Improves_Analytics_and_Decision-Making.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p>Now more than ever, effective data management is crucial to enable decision-makers to better assess information and take calculated actions. It is also important to keep up with the latest trends and technologies to derive higher value from data and analytics and maintain a competitive edge in the market. However, every organization faces challenges with data management and analytics. And as organizations scale, the complexity only increases, creating a need for better data governance, data...</p> </div> <a href="https://davidmenninger.isg-one.com/hitachi-vantara-dataops-improves-analytics-and-decision-making"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://davidmenninger.isg-one.com/topic/analytics">Analytics</a>, <a href="https://davidmenninger.isg-one.com/topic/data-governance">Data Governance</a>, <a href="https://davidmenninger.isg-one.com/topic/data-management">Data Management</a>, <a href="https://davidmenninger.isg-one.com/topic/data">Data</a>, <a href="https://davidmenninger.isg-one.com/topic/data-operations">data operations</a>, <a href="https://davidmenninger.isg-one.com/topic/analytic-data-platforms">analytic data platforms</a> <hr> </div> <!----> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://davidmenninger.isg-one.com/the-2023-market-agenda-for-analytics-empowering-workforces-to-engage">The 2023 Market Agenda for Analytics: Empowering Workforces to Engage</a></h2> <div class="author-meta"> Posted by <a href="https://davidmenninger.isg-one.com/author/david-menninger"> David Menninger </a> on <span class="post-date">17 January 2023 </span> </div> <!-- <hr> --> <hr> </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://davidmenninger.isg-one.com/hs-fs/hubfs/The%202023%20Market%20Agenda%20for%20Analytics%20Empowering%20Workforces%20to%20Engage.png?width=200&name=The%202023%20Market%20Agenda%20for%20Analytics%20Empowering%20Workforces%20to%20Engage.png" width="200" !important: srcset="https://davidmenninger.isg-one.com/hs-fs/hubfs/The%202023%20Market%20Agenda%20for%20Analytics%20Empowering%20Workforces%20to%20Engage.png?width=100&name=The%202023%20Market%20Agenda%20for%20Analytics%20Empowering%20Workforces%20to%20Engage.png 100w, https://davidmenninger.isg-one.com/hs-fs/hubfs/The%202023%20Market%20Agenda%20for%20Analytics%20Empowering%20Workforces%20to%20Engage.png?width=200&name=The%202023%20Market%20Agenda%20for%20Analytics%20Empowering%20Workforces%20to%20Engage.png 200w, https://davidmenninger.isg-one.com/hs-fs/hubfs/The%202023%20Market%20Agenda%20for%20Analytics%20Empowering%20Workforces%20to%20Engage.png?width=300&name=The%202023%20Market%20Agenda%20for%20Analytics%20Empowering%20Workforces%20to%20Engage.png 300w, https://davidmenninger.isg-one.com/hs-fs/hubfs/The%202023%20Market%20Agenda%20for%20Analytics%20Empowering%20Workforces%20to%20Engage.png?width=400&name=The%202023%20Market%20Agenda%20for%20Analytics%20Empowering%20Workforces%20to%20Engage.png 400w, https://davidmenninger.isg-one.com/hs-fs/hubfs/The%202023%20Market%20Agenda%20for%20Analytics%20Empowering%20Workforces%20to%20Engage.png?width=500&name=The%202023%20Market%20Agenda%20for%20Analytics%20Empowering%20Workforces%20to%20Engage.png 500w, https://davidmenninger.isg-one.com/hs-fs/hubfs/The%202023%20Market%20Agenda%20for%20Analytics%20Empowering%20Workforces%20to%20Engage.png?width=600&name=The%202023%20Market%20Agenda%20for%20Analytics%20Empowering%20Workforces%20to%20Engage.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p>Ventana Research recently announced its 2023 Market Agenda for <a href="https://www.ventanaresearch.com/analytics#research">Analytics</a>, continuing the guidance we have offered for nearly two decades to help organizations derive optimal value from technology investments to improve business outcomes.</p> </div> <a href="https://davidmenninger.isg-one.com/the-2023-market-agenda-for-analytics-empowering-workforces-to-engage"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://davidmenninger.isg-one.com/topic/embedded-analytics">embedded analytics</a>, <a href="https://davidmenninger.isg-one.com/topic/analytics">Analytics</a>, <a href="https://davidmenninger.isg-one.com/topic/business-intelligence">Business Intelligence</a>, <a href="https://davidmenninger.isg-one.com/topic/data">Data</a>, <a href="https://davidmenninger.isg-one.com/topic/digital-technology">Digital Technology</a>, <a href="https://davidmenninger.isg-one.com/topic/natural-language-processing">natural language processing</a>, <a href="https://davidmenninger.isg-one.com/topic/process-mining">Process Mining</a>, <a href="https://davidmenninger.isg-one.com/topic/analytics-data">Analytics & Data</a>, <a href="https://davidmenninger.isg-one.com/topic/collaborative-conversational-computing">Collaborative & Conversational Computing</a> <hr> </div> <!----> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://davidmenninger.isg-one.com/2023-digital-technology-market-agenda-innovation-for-digital-agility">2023 Digital Technology Market Agenda: Innovation for Digital Agility</a></h2> <div class="author-meta"> Posted by <a href="https://davidmenninger.isg-one.com/author/david-menninger"> David Menninger </a> on <span class="post-date">11 January 2023 </span> </div> <!-- <hr> --> <hr> </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://davidmenninger.isg-one.com/hs-fs/hubfs/2023%20Digital%20Technology%20Market%20Agenda%20Innovation%20for%20Digital%20Agility.png?width=200&name=2023%20Digital%20Technology%20Market%20Agenda%20Innovation%20for%20Digital%20Agility.png" width="200" !important: srcset="https://davidmenninger.isg-one.com/hs-fs/hubfs/2023%20Digital%20Technology%20Market%20Agenda%20Innovation%20for%20Digital%20Agility.png?width=100&name=2023%20Digital%20Technology%20Market%20Agenda%20Innovation%20for%20Digital%20Agility.png 100w, https://davidmenninger.isg-one.com/hs-fs/hubfs/2023%20Digital%20Technology%20Market%20Agenda%20Innovation%20for%20Digital%20Agility.png?width=200&name=2023%20Digital%20Technology%20Market%20Agenda%20Innovation%20for%20Digital%20Agility.png 200w, https://davidmenninger.isg-one.com/hs-fs/hubfs/2023%20Digital%20Technology%20Market%20Agenda%20Innovation%20for%20Digital%20Agility.png?width=300&name=2023%20Digital%20Technology%20Market%20Agenda%20Innovation%20for%20Digital%20Agility.png 300w, https://davidmenninger.isg-one.com/hs-fs/hubfs/2023%20Digital%20Technology%20Market%20Agenda%20Innovation%20for%20Digital%20Agility.png?width=400&name=2023%20Digital%20Technology%20Market%20Agenda%20Innovation%20for%20Digital%20Agility.png 400w, https://davidmenninger.isg-one.com/hs-fs/hubfs/2023%20Digital%20Technology%20Market%20Agenda%20Innovation%20for%20Digital%20Agility.png?width=500&name=2023%20Digital%20Technology%20Market%20Agenda%20Innovation%20for%20Digital%20Agility.png 500w, https://davidmenninger.isg-one.com/hs-fs/hubfs/2023%20Digital%20Technology%20Market%20Agenda%20Innovation%20for%20Digital%20Agility.png?width=600&name=2023%20Digital%20Technology%20Market%20Agenda%20Innovation%20for%20Digital%20Agility.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p>I’m proud to share Ventana Research’s 2023 Market Agenda for Digital Technology. Our focus in this agenda is to deliver expertise to help organizations prioritize technology investments that improve customer, partner and workforce experiences while also increasing organizational effectiveness and agility.</p> </div> <a href="https://davidmenninger.isg-one.com/2023-digital-technology-market-agenda-innovation-for-digital-agility"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://davidmenninger.isg-one.com/topic/analytics">Analytics</a>, <a href="https://davidmenninger.isg-one.com/topic/cloud-computing">Cloud Computing</a>, <a href="https://davidmenninger.isg-one.com/topic/internet-of-things">Internet of Things</a>, <a href="https://davidmenninger.isg-one.com/topic/data">Data</a>, <a href="https://davidmenninger.isg-one.com/topic/digital-technology">Digital Technology</a>, <a href="https://davidmenninger.isg-one.com/topic/blockchain">blockchain</a>, <a href="https://davidmenninger.isg-one.com/topic/mobile-computing">mobile computing</a>, <a href="https://davidmenninger.isg-one.com/topic/extended-reality">extended reality</a>, <a href="https://davidmenninger.isg-one.com/topic/robotic-automation">robotic automation</a>, <a href="https://davidmenninger.isg-one.com/topic/collaborative-conversational-computing">Collaborative & Conversational Computing</a>, <a href="https://davidmenninger.isg-one.com/topic/ai-and-machine-learning">AI and Machine Learning</a> <hr> </div> <!----> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://davidmenninger.isg-one.com/master-metrics-with-kyligence-zen">Master Metrics with Kyligence Zen</a></h2> <div class="author-meta"> Posted by <a href="https://davidmenninger.isg-one.com/author/david-menninger"> David Menninger </a> on <span class="post-date">23 December 2022 </span> </div> <!-- <hr> --> <hr> </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://davidmenninger.isg-one.com/hs-fs/hubfs/Master%20Metrics%20with%20Kyligence%20Zen.png?width=200&name=Master%20Metrics%20with%20Kyligence%20Zen.png" width="200" !important: srcset="https://davidmenninger.isg-one.com/hs-fs/hubfs/Master%20Metrics%20with%20Kyligence%20Zen.png?width=100&name=Master%20Metrics%20with%20Kyligence%20Zen.png 100w, https://davidmenninger.isg-one.com/hs-fs/hubfs/Master%20Metrics%20with%20Kyligence%20Zen.png?width=200&name=Master%20Metrics%20with%20Kyligence%20Zen.png 200w, https://davidmenninger.isg-one.com/hs-fs/hubfs/Master%20Metrics%20with%20Kyligence%20Zen.png?width=300&name=Master%20Metrics%20with%20Kyligence%20Zen.png 300w, https://davidmenninger.isg-one.com/hs-fs/hubfs/Master%20Metrics%20with%20Kyligence%20Zen.png?width=400&name=Master%20Metrics%20with%20Kyligence%20Zen.png 400w, https://davidmenninger.isg-one.com/hs-fs/hubfs/Master%20Metrics%20with%20Kyligence%20Zen.png?width=500&name=Master%20Metrics%20with%20Kyligence%20Zen.png 500w, https://davidmenninger.isg-one.com/hs-fs/hubfs/Master%20Metrics%20with%20Kyligence%20Zen.png?width=600&name=Master%20Metrics%20with%20Kyligence%20Zen.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p>Analytics processes are all about how organizations use data to create metrics that help manage and improve operations. Yet, the discipline applied to analytics processes seems to be lacking compared to data processes. I’ve pointed out that <a href="/the-weak-link-in-data-governance-analytics" rel="noopener" target="_blank">the weak link in data governance is often analytics</a>. Organizations can also do a better job tying <a href="/analytic-ops-the-last-mile-of-data-ops" rel="noopener" target="_blank">AnalyticOps to DataOps</a> and do more to <a href="/what-makes-a-metric-a-kpi" rel="noopener" target="_blank">define</a> and manage metrics. Our research has shown that creating and managing metrics in a <a href="/semantic-models-benefit-analytical-processes" rel="noopener" target="_blank">semantic model improves...</a></p> </div> <a href="https://davidmenninger.isg-one.com/master-metrics-with-kyligence-zen"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://davidmenninger.isg-one.com/topic/analytics">Analytics</a>, <a href="https://davidmenninger.isg-one.com/topic/business-intelligence">Business Intelligence</a>, <a href="https://davidmenninger.isg-one.com/topic/data-governance">Data Governance</a>, <a href="https://davidmenninger.isg-one.com/topic/data">Data</a>, <a href="https://davidmenninger.isg-one.com/topic/digital-technology">Digital Technology</a>, <a href="https://davidmenninger.isg-one.com/topic/analytics-data">Analytics & Data</a> <hr> </div> <!----> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://davidmenninger.isg-one.com/enhancing-data-catalog-with-ai">Enhancing Data Catalog with AI</a></h2> <div class="author-meta"> Posted by <a href="https://davidmenninger.isg-one.com/author/david-menninger"> David Menninger </a> on <span class="post-date">22 September 2022 </span> </div> <!-- <hr> --> <hr> </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://davidmenninger.isg-one.com/hs-fs/hubfs/AP_DM_Enhancing%20Data%20Catalog%20with%20AI.png?width=200&name=AP_DM_Enhancing%20Data%20Catalog%20with%20AI.png" width="200" !important: srcset="https://davidmenninger.isg-one.com/hs-fs/hubfs/AP_DM_Enhancing%20Data%20Catalog%20with%20AI.png?width=100&name=AP_DM_Enhancing%20Data%20Catalog%20with%20AI.png 100w, https://davidmenninger.isg-one.com/hs-fs/hubfs/AP_DM_Enhancing%20Data%20Catalog%20with%20AI.png?width=200&name=AP_DM_Enhancing%20Data%20Catalog%20with%20AI.png 200w, https://davidmenninger.isg-one.com/hs-fs/hubfs/AP_DM_Enhancing%20Data%20Catalog%20with%20AI.png?width=300&name=AP_DM_Enhancing%20Data%20Catalog%20with%20AI.png 300w, https://davidmenninger.isg-one.com/hs-fs/hubfs/AP_DM_Enhancing%20Data%20Catalog%20with%20AI.png?width=400&name=AP_DM_Enhancing%20Data%20Catalog%20with%20AI.png 400w, https://davidmenninger.isg-one.com/hs-fs/hubfs/AP_DM_Enhancing%20Data%20Catalog%20with%20AI.png?width=500&name=AP_DM_Enhancing%20Data%20Catalog%20with%20AI.png 500w, https://davidmenninger.isg-one.com/hs-fs/hubfs/AP_DM_Enhancing%20Data%20Catalog%20with%20AI.png?width=600&name=AP_DM_Enhancing%20Data%20Catalog%20with%20AI.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p>Organizations are collecting data from multiple data sources and a variety of systems to enrich their analytics and business intelligence (BI). But collecting data is only half of the equation. As the data grows, it becomes challenging to find the right data at the right time. Many organizations can’t take full advantage of their data lakes because they don’t know what data actually exists. Also, there are more regulations and compliance requirements than ever before. It is critical for...</p> </div> <a href="https://davidmenninger.isg-one.com/enhancing-data-catalog-with-ai"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://davidmenninger.isg-one.com/topic/business-intelligence">Business Intelligence</a>, <a href="https://davidmenninger.isg-one.com/topic/data-governance">Data Governance</a>, <a href="https://davidmenninger.isg-one.com/topic/data-management">Data Management</a>, <a href="https://davidmenninger.isg-one.com/topic/data">Data</a>, <a href="https://davidmenninger.isg-one.com/topic/data-operations">data operations</a>, <a href="https://davidmenninger.isg-one.com/topic/ai-and-machine-learning">AI and Machine Learning</a> <hr> </div> <!----> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://davidmenninger.isg-one.com/dont-rely-on-dashboards-for-real-time-analytics">Don’t Rely on Dashboards for Real-Time Analytics</a></h2> <div class="author-meta"> Posted by <a href="https://davidmenninger.isg-one.com/author/david-menninger"> David Menninger </a> on <span class="post-date">31 March 2022 </span> </div> <!-- <hr> --> <hr> </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://davidmenninger.isg-one.com/hs-fs/hubfs/AP_DM_Don%E2%80%99t%20Rely%20on%20Dashboards%20for%20Real-Time%20Analytics.png?width=200&name=AP_DM_Don%E2%80%99t%20Rely%20on%20Dashboards%20for%20Real-Time%20Analytics.png" width="200" !important: srcset="https://davidmenninger.isg-one.com/hs-fs/hubfs/AP_DM_Don%E2%80%99t%20Rely%20on%20Dashboards%20for%20Real-Time%20Analytics.png?width=100&name=AP_DM_Don%E2%80%99t%20Rely%20on%20Dashboards%20for%20Real-Time%20Analytics.png 100w, https://davidmenninger.isg-one.com/hs-fs/hubfs/AP_DM_Don%E2%80%99t%20Rely%20on%20Dashboards%20for%20Real-Time%20Analytics.png?width=200&name=AP_DM_Don%E2%80%99t%20Rely%20on%20Dashboards%20for%20Real-Time%20Analytics.png 200w, https://davidmenninger.isg-one.com/hs-fs/hubfs/AP_DM_Don%E2%80%99t%20Rely%20on%20Dashboards%20for%20Real-Time%20Analytics.png?width=300&name=AP_DM_Don%E2%80%99t%20Rely%20on%20Dashboards%20for%20Real-Time%20Analytics.png 300w, https://davidmenninger.isg-one.com/hs-fs/hubfs/AP_DM_Don%E2%80%99t%20Rely%20on%20Dashboards%20for%20Real-Time%20Analytics.png?width=400&name=AP_DM_Don%E2%80%99t%20Rely%20on%20Dashboards%20for%20Real-Time%20Analytics.png 400w, https://davidmenninger.isg-one.com/hs-fs/hubfs/AP_DM_Don%E2%80%99t%20Rely%20on%20Dashboards%20for%20Real-Time%20Analytics.png?width=500&name=AP_DM_Don%E2%80%99t%20Rely%20on%20Dashboards%20for%20Real-Time%20Analytics.png 500w, https://davidmenninger.isg-one.com/hs-fs/hubfs/AP_DM_Don%E2%80%99t%20Rely%20on%20Dashboards%20for%20Real-Time%20Analytics.png?width=600&name=AP_DM_Don%E2%80%99t%20Rely%20on%20Dashboards%20for%20Real-Time%20Analytics.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p>I have <a href="https://davidmenninger.ventanaresearch.com/historical-databases-becoming-a-thing-of-the-past" style="text-decoration: none;">written previously</a> that the world of data and analytics will become more and more centered around real-time, streaming data. Data is created constantly and increasingly is being collected simultaneously. Technology advances now enable organizations to process and analyze information as it is being collected to respond in real time to opportunities and threats. Not all use cases require real-time analysis and response, but many do, including multiple use cases that can improve customer...</p> </div> <a href="https://davidmenninger.isg-one.com/dont-rely-on-dashboards-for-real-time-analytics"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://davidmenninger.isg-one.com/topic/analytics">Analytics</a>, <a href="https://davidmenninger.isg-one.com/topic/business-intelligence">Business Intelligence</a>, <a href="https://davidmenninger.isg-one.com/topic/internet-of-things">Internet of Things</a>, <a href="https://davidmenninger.isg-one.com/topic/data">Data</a>, <a href="https://davidmenninger.isg-one.com/topic/digital-technology">Digital Technology</a>, <a href="https://davidmenninger.isg-one.com/topic/streaming-analytics">Streaming Analytics</a>, <a href="https://davidmenninger.isg-one.com/topic/analytics-data">Analytics & Data</a>, <a href="https://davidmenninger.isg-one.com/topic/streaming-data-events">Streaming Data & Events</a>, <a href="https://davidmenninger.isg-one.com/topic/ai-and-machine-learning">AI and Machine Learning</a> <hr> </div> <!----> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://davidmenninger.isg-one.com/working-across-the-aisle-in-analytics-involving-it-and-lob">Working Across the Aisle in Analytics: Involving IT and LOB</a></h2> <div class="author-meta"> Posted by <a href="https://davidmenninger.isg-one.com/author/david-menninger"> David Menninger </a> on <span class="post-date">23 March 2022 </span> </div> <!-- <hr> --> <hr> </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://davidmenninger.isg-one.com/hs-fs/hubfs/AP_DM_Working%20Across%20the%20Aisle%20in%20Analytics_Involving%20IT%20and%20LOB.png?width=200&name=AP_DM_Working%20Across%20the%20Aisle%20in%20Analytics_Involving%20IT%20and%20LOB.png" width="200" !important: srcset="https://davidmenninger.isg-one.com/hs-fs/hubfs/AP_DM_Working%20Across%20the%20Aisle%20in%20Analytics_Involving%20IT%20and%20LOB.png?width=100&name=AP_DM_Working%20Across%20the%20Aisle%20in%20Analytics_Involving%20IT%20and%20LOB.png 100w, https://davidmenninger.isg-one.com/hs-fs/hubfs/AP_DM_Working%20Across%20the%20Aisle%20in%20Analytics_Involving%20IT%20and%20LOB.png?width=200&name=AP_DM_Working%20Across%20the%20Aisle%20in%20Analytics_Involving%20IT%20and%20LOB.png 200w, https://davidmenninger.isg-one.com/hs-fs/hubfs/AP_DM_Working%20Across%20the%20Aisle%20in%20Analytics_Involving%20IT%20and%20LOB.png?width=300&name=AP_DM_Working%20Across%20the%20Aisle%20in%20Analytics_Involving%20IT%20and%20LOB.png 300w, https://davidmenninger.isg-one.com/hs-fs/hubfs/AP_DM_Working%20Across%20the%20Aisle%20in%20Analytics_Involving%20IT%20and%20LOB.png?width=400&name=AP_DM_Working%20Across%20the%20Aisle%20in%20Analytics_Involving%20IT%20and%20LOB.png 400w, https://davidmenninger.isg-one.com/hs-fs/hubfs/AP_DM_Working%20Across%20the%20Aisle%20in%20Analytics_Involving%20IT%20and%20LOB.png?width=500&name=AP_DM_Working%20Across%20the%20Aisle%20in%20Analytics_Involving%20IT%20and%20LOB.png 500w, https://davidmenninger.isg-one.com/hs-fs/hubfs/AP_DM_Working%20Across%20the%20Aisle%20in%20Analytics_Involving%20IT%20and%20LOB.png?width=600&name=AP_DM_Working%20Across%20the%20Aisle%20in%20Analytics_Involving%20IT%20and%20LOB.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p>For years, maybe decades, we have heard about the struggles between IT and line-of-business functions. In this perspective, we will look at some of the data from our <a href="https://www.ventanaresearch.com/benchmark/analytics/analytics_and_data/research_report/2022" style="text-decoration: none;">Analytics and Data Benchmark Research</a> about the roles of IT and line-of-business teams in analytics and data processes. We will also look at some of the disconnects between these two groups. And, by looking at how organizations are operating today and the results they are achieving, we can discern some of the best practices for...</p> </div> <a href="https://davidmenninger.isg-one.com/working-across-the-aisle-in-analytics-involving-it-and-lob"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://davidmenninger.isg-one.com/topic/analytics">Analytics</a>, <a href="https://davidmenninger.isg-one.com/topic/business-intelligence">Business Intelligence</a>, <a href="https://davidmenninger.isg-one.com/topic/data">Data</a>, <a href="https://davidmenninger.isg-one.com/topic/digital-technology">Digital Technology</a>, <a href="https://davidmenninger.isg-one.com/topic/analytics-data">Analytics & Data</a>, <a href="https://davidmenninger.isg-one.com/topic/ai-and-machine-learning">AI and Machine Learning</a> <hr> </div> <!----> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://davidmenninger.isg-one.com/improving-the-state-of-analytics-in-organizations">Improving the State of Analytics in Organizations</a></h2> <div class="author-meta"> Posted by <a href="https://davidmenninger.isg-one.com/author/david-menninger"> David Menninger </a> on <span class="post-date">24 February 2022 </span> </div> <!-- <hr> --> <hr> </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://davidmenninger.isg-one.com/hs-fs/hubfs/AP_DM_Improving%20the%20State%20of%20Analytics%20in%20Organizations.png?width=200&name=AP_DM_Improving%20the%20State%20of%20Analytics%20in%20Organizations.png" width="200" !important: srcset="https://davidmenninger.isg-one.com/hs-fs/hubfs/AP_DM_Improving%20the%20State%20of%20Analytics%20in%20Organizations.png?width=100&name=AP_DM_Improving%20the%20State%20of%20Analytics%20in%20Organizations.png 100w, https://davidmenninger.isg-one.com/hs-fs/hubfs/AP_DM_Improving%20the%20State%20of%20Analytics%20in%20Organizations.png?width=200&name=AP_DM_Improving%20the%20State%20of%20Analytics%20in%20Organizations.png 200w, https://davidmenninger.isg-one.com/hs-fs/hubfs/AP_DM_Improving%20the%20State%20of%20Analytics%20in%20Organizations.png?width=300&name=AP_DM_Improving%20the%20State%20of%20Analytics%20in%20Organizations.png 300w, https://davidmenninger.isg-one.com/hs-fs/hubfs/AP_DM_Improving%20the%20State%20of%20Analytics%20in%20Organizations.png?width=400&name=AP_DM_Improving%20the%20State%20of%20Analytics%20in%20Organizations.png 400w, https://davidmenninger.isg-one.com/hs-fs/hubfs/AP_DM_Improving%20the%20State%20of%20Analytics%20in%20Organizations.png?width=500&name=AP_DM_Improving%20the%20State%20of%20Analytics%20in%20Organizations.png 500w, https://davidmenninger.isg-one.com/hs-fs/hubfs/AP_DM_Improving%20the%20State%20of%20Analytics%20in%20Organizations.png?width=600&name=AP_DM_Improving%20the%20State%20of%20Analytics%20in%20Organizations.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p>Despite all the advances organizations have made with respect to analytics, our most recent research shows the majority of the workforce in the majority of organizations are not using analytics and business intelligence (BI). Less than one-quarter (23%) report that one-half or more of their workforce is using analytics and BI. This is a problem. It means organizations are not enabling their workforce to perform at peak efficiency and effectiveness. It means the workforce in many organizations...</p> </div> <a href="https://davidmenninger.isg-one.com/improving-the-state-of-analytics-in-organizations"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://davidmenninger.isg-one.com/topic/sales">Sales</a>, <a href="https://davidmenninger.isg-one.com/topic/embedded-analytics">embedded analytics</a>, <a href="https://davidmenninger.isg-one.com/topic/analytics">Analytics</a>, <a href="https://davidmenninger.isg-one.com/topic/business-intelligence">Business Intelligence</a>, <a href="https://davidmenninger.isg-one.com/topic/data">Data</a>, <a href="https://davidmenninger.isg-one.com/topic/sales-performance-management">Sales Performance Management</a>, <a href="https://davidmenninger.isg-one.com/topic/digital-technology">Digital Technology</a>, <a href="https://davidmenninger.isg-one.com/topic/digital-commerce">Digital Commerce</a>, <a href="https://davidmenninger.isg-one.com/topic/natural-language-processing">natural language processing</a>, <a href="https://davidmenninger.isg-one.com/topic/subscription-management">Subscription Management</a>, <a href="https://davidmenninger.isg-one.com/topic/partner-management">partner management</a>, <a href="https://davidmenninger.isg-one.com/topic/sales-engagement">sales engagement</a>, <a href="https://davidmenninger.isg-one.com/topic/revenue-management">revenue management</a>, <a href="https://davidmenninger.isg-one.com/topic/collaborative-conversational-computing">Collaborative & Conversational Computing</a> <hr> </div> <script> // Check active classes var checkClass = function() { if ( $('.fil_item').hasClass('hide') ) { $('.fil_item').removeClass('hide'); 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style="color: #255a89;">Our Analysts</h3> <!--start row--> <div class="row-fluid"> <div class="span6" style="text-align: center;"> <a href="https://davidmenninger.isg-research.net"> <img class="analyst_img" style="width: 100px;" src="https://davidmenninger.isg-one.com/hs-fs/hubfs/_images/_team/Menninger.David%20Pic.Circle.png?width=100&name=Menninger.David%20Pic.Circle.png" alt="David Menninger" width="100" data-constrained="true" caption="false" sizes="(max-width: 100px) 100vw, 100px" srcset="https://davidmenninger.isg-one.com/hs-fs/hubfs/_images/_team/Menninger.David%20Pic.Circle.png?width=50&name=Menninger.David%20Pic.Circle.png 50w, https://davidmenninger.isg-one.com/hs-fs/hubfs/_images/_team/Menninger.David%20Pic.Circle.png?width=100&name=Menninger.David%20Pic.Circle.png 100w, https://davidmenninger.isg-one.com/hs-fs/hubfs/_images/_team/Menninger.David%20Pic.Circle.png?width=150&name=Menninger.David%20Pic.Circle.png 150w, https://davidmenninger.isg-one.com/hs-fs/hubfs/_images/_team/Menninger.David%20Pic.Circle.png?width=200&name=Menninger.David%20Pic.Circle.png 200w, https://davidmenninger.isg-one.com/hs-fs/hubfs/_images/_team/Menninger.David%20Pic.Circle.png?width=250&name=Menninger.David%20Pic.Circle.png 250w, https://davidmenninger.isg-one.com/hs-fs/hubfs/_images/_team/Menninger.David%20Pic.Circle.png?width=300&name=Menninger.David%20Pic.Circle.png 300w"> <h5 class="analyst_name">David Menninger</h5> </a> </div> <div class="span6" style="text-align: center;"> <a href="https://jefforr.isg-research.net"> <img class="analyst_img" style="width: 100px;" src="https://davidmenninger.isg-one.com/hs-fs/hubfs/_images/_team/Orr.Jeff.Circle.png?width=100&name=Orr.Jeff.Circle.png" alt="Jeff Orr" width="100" data-constrained="true" caption="false" srcset="https://davidmenninger.isg-one.com/hs-fs/hubfs/_images/_team/Orr.Jeff.Circle.png?width=50&name=Orr.Jeff.Circle.png 50w, https://davidmenninger.isg-one.com/hs-fs/hubfs/_images/_team/Orr.Jeff.Circle.png?width=100&name=Orr.Jeff.Circle.png 100w, https://davidmenninger.isg-one.com/hs-fs/hubfs/_images/_team/Orr.Jeff.Circle.png?width=150&name=Orr.Jeff.Circle.png 150w, https://davidmenninger.isg-one.com/hs-fs/hubfs/_images/_team/Orr.Jeff.Circle.png?width=200&name=Orr.Jeff.Circle.png 200w, https://davidmenninger.isg-one.com/hs-fs/hubfs/_images/_team/Orr.Jeff.Circle.png?width=250&name=Orr.Jeff.Circle.png 250w, https://davidmenninger.isg-one.com/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> <!--end row--> <!--start row--> <div class="row-fluid"> <div class="span6" style="text-align: center;"> <a href="https://keithdawson.isg-research.net"> <img class="analyst_img" style="width: 100px;" src="https://davidmenninger.isg-one.com/hs-fs/hubfs/_images/_team/Dawson.Keith%20Pic.Circle.png?width=100&name=Dawson.Keith%20Pic.Circle.png" alt="Keith Dawson" width="100" data-constrained="true" caption="false" sizes="(max-width: 100px) 100vw, 100px" srcset="https://davidmenninger.isg-one.com/hs-fs/hubfs/_images/_team/Dawson.Keith%20Pic.Circle.png?width=50&name=Dawson.Keith%20Pic.Circle.png 50w, https://davidmenninger.isg-one.com/hs-fs/hubfs/_images/_team/Dawson.Keith%20Pic.Circle.png?width=100&name=Dawson.Keith%20Pic.Circle.png 100w, https://davidmenninger.isg-one.com/hs-fs/hubfs/_images/_team/Dawson.Keith%20Pic.Circle.png?width=150&name=Dawson.Keith%20Pic.Circle.png 150w, https://davidmenninger.isg-one.com/hs-fs/hubfs/_images/_team/Dawson.Keith%20Pic.Circle.png?width=200&name=Dawson.Keith%20Pic.Circle.png 200w, https://davidmenninger.isg-one.com/hs-fs/hubfs/_images/_team/Dawson.Keith%20Pic.Circle.png?width=250&name=Dawson.Keith%20Pic.Circle.png 250w, https://davidmenninger.isg-one.com/hs-fs/hubfs/_images/_team/Dawson.Keith%20Pic.Circle.png?width=300&name=Dawson.Keith%20Pic.Circle.png 300w"> <h5 class="analyst_name">Keith Dawson</h5> </a> </div> <div class="span6" style="text-align:center;"> <a href="https://marksmith.isg-research.net"> <img class="analyst_img" style="width: 100px;" src="https://davidmenninger.isg-one.com/hs-fs/hubfs/_images/_team/Smith.Mark%20Pic.Circle.png?width=100&name=Smith.Mark%20Pic.Circle.png" alt="Mark Smith" width="100" data-constrained="true" caption="false" sizes="(max-width: 100px) 100vw, 100px" srcset="https://davidmenninger.isg-one.com/hs-fs/hubfs/_images/_team/Smith.Mark%20Pic.Circle.png?width=50&name=Smith.Mark%20Pic.Circle.png 50w, https://davidmenninger.isg-one.com/hs-fs/hubfs/_images/_team/Smith.Mark%20Pic.Circle.png?width=100&name=Smith.Mark%20Pic.Circle.png 100w, https://davidmenninger.isg-one.com/hs-fs/hubfs/_images/_team/Smith.Mark%20Pic.Circle.png?width=150&name=Smith.Mark%20Pic.Circle.png 150w, https://davidmenninger.isg-one.com/hs-fs/hubfs/_images/_team/Smith.Mark%20Pic.Circle.png?width=200&name=Smith.Mark%20Pic.Circle.png 200w, https://davidmenninger.isg-one.com/hs-fs/hubfs/_images/_team/Smith.Mark%20Pic.Circle.png?width=250&name=Smith.Mark%20Pic.Circle.png 250w, https://davidmenninger.isg-one.com/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> <!--end row--> <!--start row--> <div class="row-fluid"> <div class="span6" style="text-align: center;"> <a href="https://mattaslett.isg-research.net"> <img class="analyst_img" style="width: 100px;" src="https://davidmenninger.isg-one.com/hs-fs/hubfs/_images/_team/Aslett.Matt%20Pic.Circle.png?width=100&name=Aslett.Matt%20Pic.Circle.png" alt="Matt Aslett" width="100" data-constrained="true" caption="false" sizes="(max-width: 100px) 100vw, 100px" srcset="https://davidmenninger.isg-one.com/hs-fs/hubfs/_images/_team/Aslett.Matt%20Pic.Circle.png?width=50&name=Aslett.Matt%20Pic.Circle.png 50w, https://davidmenninger.isg-one.com/hs-fs/hubfs/_images/_team/Aslett.Matt%20Pic.Circle.png?width=100&name=Aslett.Matt%20Pic.Circle.png 100w, https://davidmenninger.isg-one.com/hs-fs/hubfs/_images/_team/Aslett.Matt%20Pic.Circle.png?width=150&name=Aslett.Matt%20Pic.Circle.png 150w, https://davidmenninger.isg-one.com/hs-fs/hubfs/_images/_team/Aslett.Matt%20Pic.Circle.png?width=200&name=Aslett.Matt%20Pic.Circle.png 200w, https://davidmenninger.isg-one.com/hs-fs/hubfs/_images/_team/Aslett.Matt%20Pic.Circle.png?width=250&name=Aslett.Matt%20Pic.Circle.png 250w, https://davidmenninger.isg-one.com/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://davidmenninger.isg-one.com/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://davidmenninger.isg-one.com/hs-fs/hubfs/_images/_team/profile_vr_matthew_brown_circle_900x900.png?width=50&name=profile_vr_matthew_brown_circle_900x900.png 50w, https://davidmenninger.isg-one.com/hs-fs/hubfs/_images/_team/profile_vr_matthew_brown_circle_900x900.png?width=100&name=profile_vr_matthew_brown_circle_900x900.png 100w, https://davidmenninger.isg-one.com/hs-fs/hubfs/_images/_team/profile_vr_matthew_brown_circle_900x900.png?width=150&name=profile_vr_matthew_brown_circle_900x900.png 150w, https://davidmenninger.isg-one.com/hs-fs/hubfs/_images/_team/profile_vr_matthew_brown_circle_900x900.png?width=200&name=profile_vr_matthew_brown_circle_900x900.png 200w, https://davidmenninger.isg-one.com/hs-fs/hubfs/_images/_team/profile_vr_matthew_brown_circle_900x900.png?width=250&name=profile_vr_matthew_brown_circle_900x900.png 250w, https://davidmenninger.isg-one.com/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> <!--end row--> <!--start row--> <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://davidmenninger.isg-one.com/hs-fs/hubfs/_images/_team/Kugel.Robert%20Pic.Circle.png?width=100&name=Kugel.Robert%20Pic.Circle.png" alt="Robert Kugel" width="100" data-constrained="true" caption="false" sizes="(max-width: 100px) 100vw, 100px" srcset="https://davidmenninger.isg-one.com/hs-fs/hubfs/_images/_team/Kugel.Robert%20Pic.Circle.png?width=50&name=Kugel.Robert%20Pic.Circle.png 50w, https://davidmenninger.isg-one.com/hs-fs/hubfs/_images/_team/Kugel.Robert%20Pic.Circle.png?width=100&name=Kugel.Robert%20Pic.Circle.png 100w, https://davidmenninger.isg-one.com/hs-fs/hubfs/_images/_team/Kugel.Robert%20Pic.Circle.png?width=150&name=Kugel.Robert%20Pic.Circle.png 150w, https://davidmenninger.isg-one.com/hs-fs/hubfs/_images/_team/Kugel.Robert%20Pic.Circle.png?width=200&name=Kugel.Robert%20Pic.Circle.png 200w, https://davidmenninger.isg-one.com/hs-fs/hubfs/_images/_team/Kugel.Robert%20Pic.Circle.png?width=250&name=Kugel.Robert%20Pic.Circle.png 250w, https://davidmenninger.isg-one.com/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://davidmenninger.isg-one.com/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://davidmenninger.isg-one.com/hs-fs/hubfs/_images/_team/Hurrell.Stephen%20Pic.Circle.png?width=50&name=Hurrell.Stephen%20Pic.Circle.png 50w, https://davidmenninger.isg-one.com/hs-fs/hubfs/_images/_team/Hurrell.Stephen%20Pic.Circle.png?width=100&name=Hurrell.Stephen%20Pic.Circle.png 100w, https://davidmenninger.isg-one.com/hs-fs/hubfs/_images/_team/Hurrell.Stephen%20Pic.Circle.png?width=150&name=Hurrell.Stephen%20Pic.Circle.png 150w, https://davidmenninger.isg-one.com/hs-fs/hubfs/_images/_team/Hurrell.Stephen%20Pic.Circle.png?width=200&name=Hurrell.Stephen%20Pic.Circle.png 200w, https://davidmenninger.isg-one.com/hs-fs/hubfs/_images/_team/Hurrell.Stephen%20Pic.Circle.png?width=250&name=Hurrell.Stephen%20Pic.Circle.png 250w, https://davidmenninger.isg-one.com/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> <!--end row--> <!--start row--> <div class="row-fluid"> <!-- <div class="span6" style="text-align: center;"> <a href="https://robertkugel.isg-research.net"> <img class="analyst_img" style="display:inline;" src="https://blog.isg-research.net/hs-fs/hubfs/Images/Analyst_Photos/Rob_2016_Circle_Zoomed.png?width=100&name=Rob_2016_Circle_Zoomed.png" alt="Robert Kugel" width="100" style="width: 100px;" data-constrained="true" caption="false" sizes="(max-width: 100px) 100vw, 100px"> <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="display:inline;" src="https://blog.isg-research.net/hs-fs/hubfs/Images/Analyst_Photos/Stephen-Hurrell-Bio-Photo-Circle-1-1.png?width=100&name=Stephen-Hurrell-Bio-Photo-Circle-1-1.png" alt="Stephen Hurrell" width="100" style="width: 100px;" data-constrained="true" caption="false" sizes="(max-width: 100px) 100vw, 100px"> <h5 class="analyst_name">Stephen Hurrell</h5> </a> </div> --> <div style="text-align: center;"> <a href="https://blog.isg-research.net"> <img class="analyst_img" style="width: 100px;" src="https://davidmenninger.isg-one.com/hs-fs/hubfs/ISG_Bug_Reverse_Transparent.png?width=100&name=ISG_Bug_Reverse_Transparent.png" alt="ISG Software Research" width="100" data-constrained="true" caption="false" srcset="https://davidmenninger.isg-one.com/hs-fs/hubfs/ISG_Bug_Reverse_Transparent.png?width=50&name=ISG_Bug_Reverse_Transparent.png 50w, 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src="https://davidmenninger.isg-one.com/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://davidmenninger.isg-one.com/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": "Stream and Event Processing Require Real-Time Analytics", "mainEntityOfPage": "https://davidmenninger.isg-one.com/stream-and-event-processing-require-real-time-analytics", "datePublished": "25/10/2023", "dateModified": "25/10/2023", "articleBody": "In my past perspectives, I’ve written about the evolution from data at rest to data in motion and the fact that you can’t rely on dashboards for real-time analytics. Organizations are becoming more and more event-driven and operating based on streaming data. As well, analytics are becoming more and more intertwined with operations. More than one-fifth of organizations (22%) describe their analytics workloads as real time in our Data and Analytics Benchmark Research and nearly half (47%) of organizations report their IoT workloads require seconds or sub-second latency. If organizations can’t rely on dashboards for real-time analytics, what should they consider? Let’s start with a consideration of the requirements for real-time analytics. Clearly, latency is one consideration. The analysis must be completed quickly enough to meet the requirements of the specific use case. Some use cases require very low latency, such as digital ad servers. Other use cases, such as social media monitoring, are less demanding. But in either case, one of the key requirements is automation. Manually monitoring continuous streams of data is not a very effective or efficient way to analyze and respond to events as they occur. Another question is how much context is needed to provide an accurate analysis. In some cases, a single event might be sufficient, for instance if you are monitoring temperature and need to know if any single reading is outside normal operating limits. In other cases, you may need a window of events, such as social media monitoring, to see if sentiment has shifted over a period of time. And in still other situations, you may need large amounts of historical context, for example a customer’s past purchase history, so you can make product recommendations as they are browsing your site or speaking with a customer service agent. The options to handle these requirements fall into two broad categories: data platforms and stream-processing frameworks. My colleague, Matt Aslett, has written about and evaluated operational data platforms. Data platform vendors, both relational and non-relational (NoSQL), have been reducing the latency and increasing the throughput of their systems. In many use cases, these capabilities may be sufficient. In other cases, organizations may need to consider a stream-processing framework, or they may have already adopted a streaming data platform and wish to enrich the data as it streams through their organization. There are a variety of Apache open-source projects associated with processing streaming data including Beam, Calcite, Flink, Kafka, Spark, Storm and others. Among these, Kafka and Spark have been widely adopted. Kafka has emerged as a de facto standard for streaming data, with many vendors supporting it in their data platforms or offering it as a managed service. However, while Kafka provides a robust data streaming platform, it is limited in the types of transformations and analyses it supports. Perhaps as a result of these limitations, Apache Flink is on a growth trajectory that matches Kafka’s earlier growth trajectory. Flink provides stateful computations over data streams, including an SQL processing layer. Apache Spark has also emerged as a de facto standard for processing large amounts of data and is the backbone of many data lake implementations. Within Spark are projects to support SQL as well as a project for Structured Streaming which applies SQL to streaming data. For those that require more sophisticated analyses, Spark also includes a machine learning (ML) library. Basically, organizations have three categories to choose from for real-time analytics over streaming data. First, they can rely on their data platform provider. Most are offering some support for stream processing and, depending on the use case and the relationship with the vendor, that may be sufficient. For those organizations that have made big commitments to a data lake architecture, Spark is likely to be a good alternative, again depending on the use case. However, for those organizations that are heavily invested in Kafka or one of its variants, Flink may be the best option, enabling the analyses to be associated with the streams independent of where they land and what data platforms are used. Some operations don’t require real time. Some require near real time and still others will require nearly instantaneous responses. Explore your options so you can be prepared to meet your organization’s needs. Regards, David Menninger", "image": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/Stream_and_Event_Processing_Require_Real-Time_Analytics.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "David Menninger", "url": "https://davidmenninger.isg-one.com/author/david-menninger", "sameAs": "http://www.linkedin.com/in/davidmenninger" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "davidmenninger.isg-one.com", "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": "Reversing the ETL Process Improves Data Operations", "mainEntityOfPage": "https://davidmenninger.isg-one.com/reversing-the-etl-process-improves-data-operations", "datePublished": "19/09/2023", "dateModified": "19/09/2023", "articleBody": "At one point, analytics and business intelligence were considered non-mission critical activities. One of the primary concerns in designing analytics systems was to ensure they didn’t interfere with or draw computing resources away from operational systems. But today, analytical systems are integral to many aspects of operations. More than 9 in 10 participants in our Analytics and Data Benchmark Research reported analytics had improved activities and processes. However, most analytics and BI software products are still designed as read-only systems, and the process of using their output is either a manual process or involves custom coding or scripting to provide inputs to the operational systems. The most common complaint about analytics and BI technologies is that they are hard to integrate with business processes. In this perspective, we’ll look at how the recent trend of “reverse ETL” helps organizations utilize the output of analytic processes more effectively in operations. When data warehouses emerged, ETL – extract, transform and load – became the pattern for data integration. Source data is extracted from operational systems, transformed in flight and loaded into the data warehouse. As data lakes arrived on the scene, ELT – extract, load and transform – became another process where data is extracted from source systems, loaded into a highly scalable data platform and processed there rather than in a separate system. Now, several solutions, including a new software category called reverse ETL, are emerging for moving information from analytical data warehouses and BI tools into the operational systems they are supporting. Reverse ETL applications are based on the premise that information stored in data warehouses is valuable for operational activities and some of it should be moved into those systems. For example, data warehouses often enrich customer data with metrics such as segmentation scores, propensity to respond to different offers and external data such as demographic information. If this data is available within operational systems, it can be used directly in customer service interactions or marketing campaigns. Over the past few years, I’ve noticed analytics vendors starting to recognize the need to move information into operational systems. Most of the solutions are limited, but others are more systematic. The more limited solutions invoke custom scripts from the analytics systems to move information. The more systematic solutions involve two-way connectors with the source systems. Many BI tools have connectors to gather data from source systems. Several vendors now offer two-way connectors to either pull information from source systems or push information to those systems. Simultaneously, a new category of vendors is developing reverse ETL tools independent of the analytics and BI systems. As the name implies, reverse ETL systems extract data from the data warehouse, perhaps perform light transformation and load it into operational systems. There are some challenges with each approach. Custom scripts or application programming interface calls may be useful for moving small amounts of information, but often, large volumes of data must be moved into the source systems. Two-way connectors associated with BI tools can move larger amounts of information but require careful governance so data from the operational system is not corrupted. Reverse ETL systems are primarily designed to work with data from the data warehouse. Often, valuable information exists in the analytics and BI tools that is materialized via calculations. That information wouldn’t be available to the reverse ETL tools. I welcome the developments in this area. BI needs to be actionable, but actions are rarely taken from the BI tools. They are implemented in systems for marketing, customer service, human resources, supply chain and other operational systems. Keep an eye on this area as it develops. Explore the options associated with your information architecture. As applications become available to replace custom coding or scripts, consider switching as a way to scale the value of analytics and BI investments. Regards, David Menninger", "image": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/Reversing_the_ETL_Process_Improves_Data_Operations.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "David Menninger", "url": "https://davidmenninger.isg-one.com/author/david-menninger", "sameAs": "http://www.linkedin.com/in/davidmenninger" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "davidmenninger.isg-one.com", "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": "Hitachi Vantara DataOps Improves Analytics and Decision-Making", "mainEntityOfPage": "https://davidmenninger.isg-one.com/hitachi-vantara-dataops-improves-analytics-and-decision-making", "datePublished": "21/03/2023", "dateModified": "21/03/2023", "articleBody": "Now more than ever, effective data management is crucial to enable decision-makers to better assess information and take calculated actions. It is also important to keep up with the latest trends and technologies to derive higher value from data and analytics and maintain a competitive edge in the market. However, every organization faces challenges with data management and analytics. And as organizations scale, the complexity only increases, creating a need for better data governance, data quality and streamlined and automated processes. DataOps can help solve many of the challenges organizations encounter when trying to unlock the power of data by expanding data use to various parts of an organization. Hitachi Vantara offers DataOps technology that enables organizations to improve data agility and automation. It provides cloud-ready infrastructure, advanced data management software and a broad range of support services. Hitachi Vantara is a subsidiary of Hitachi, Ltd., delivering various offerings for enterprises and midsize organizations, including infrastructure, storage, data and analytics software, consulting and industry-focused managed services. Hitachi Vantara Lumada DataOps software offers capabilities for automated, artificial intelligence-driven data operations. It provides users advanced analytics for industrial use cases, and can create an integrated data fabric that is supported by a data catalog for data quality improvements and governance. Organizations can build various data products that potentially accelerate time to insight and improve customer experience. Hitachi has made several acquisitions and combined them with internal developments to build out DataOps capabilities. It acquired Pentaho in 2015 for its data integration and visualization capabilities, and acquired Waterline Data in 2020, incorporating its data cataloging technology into its Lumada Data Catalog application to solve data challenges for analytics and governance across cloud environments. Hitachi Vantara’s data integration and analytics platform is powered by Pentaho and enables users to build and deploy data pipelines for automation. It offers a no-code solution with a drag and drop interface for on-premises and cloud data integrations. Its transformation engine allows users to visualize, blend and connect data from on-premises or cloud, including Microsoft Azure, Amazon Web Services and Google Cloud Platform. Its data flow orchestration capability allows users to switch between native Kettle and Spark engines and operationalize artificial intelligence and machine learning models based on R, Python, Scala and WEKA. With Pentaho Data Integration, organizations can access data from various complex and heterogeneous sources and integrate it without writing code. Its graphical user interface – paired with a multithreaded transformation engine – offers extract, transform and load capabilities that cover data integration needs, including big data ingestion and processing. Data Catalog enables data engineers, data scientists and end users to automatically discover and tag data across the organization using machine learning. This decreases the risk of manual processing errors and prevents data silos. Organizations can also use Data Catalog to ensure compliance with regulatory mandates by systemizing and rationalizing the data. Its Data Optimizer for Hadoop provides data tiering capabilities that enable users to optimize data across Hadoop Distributed File System and object storage, balancing speed of access and cost of storage across different workloads. Our research indicates that the more data catalog users an organization has, the greater the trust the organization has in its data and the higher the level of confidence in the organization’s ability to govern and manage data across the business. Almost three-quarters (74%) of organizations with more than 100 data catalog users have trust in the data used in decision-making and operations, compared to 66% of those with 100 or less data catalog users. Similarly, three-quarters (75%) of organizations with more than 100 data catalog users are confident in the organization’s ability to govern and manage data across the business, compared to 56% of organizations with 100 or less data catalog users. Lumada Industrial DataOps and IoT Software is an extension of its data and analytics capabilities and automates onboarding, integrating, contextualizing, transforming and governing IT and operational technology data for analysis, including AI/ML-based applications. Last year, Hitachi launched Lumada Inspection Insights, which is its portfolio of digital applications for the inspection, monitoring and optimization of infrastructure and critical assets, using AI/ML to analyze photographs and video, including LiDAR, thermal and satellite imagery. Hitachi Vantara has chosen to concentrate on the DataOps market. DataOps is enabling organizations to be more agile in data processes. My colleague Matt Aslett asserts that through 2025, awareness of DataOps will continue to increase as organizations adapt data integration and engineering processes to the growing need for continuous and automated data ingestion, transformation and delivery. With parent company Hitachi’s background of providing software and services to a broad range of industries, the company is strongly positioned in the IoT sector. It enables organizations to embed data integration and analytics into various workflows to speed up processes. While Hitachi Vantara has made significant investments to create a DataOps product line, it appears to have struggled with rationalizing the branding of the products. After bringing products together under the Lumada branding, it has alternately downplayed and then featured the Pentaho brand, causing confusion and making it more difficult to understand the elements in its portfolio. Organizations face an increasing challenge to manage and extract value from a growing variety and volume of data across an edge-to-cloud infrastructure. By using DataOps to manage and govern data, organizations can better control data and analytics processes, lower costs and increase productivity. I recommend that organizations looking to streamline and automate data management, data discovery and analytics evaluate Hitachi Vantara. It can speed time to insight and improve information quality for better analytics and decision-making. Regards, David Menninger", "image": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/Hitachi_Vantara_DataOps_Improves_Analytics_and_Decision-Making.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "David Menninger", "url": "https://davidmenninger.isg-one.com/author/david-menninger", "sameAs": "http://www.linkedin.com/in/davidmenninger" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "davidmenninger.isg-one.com", "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 2023 Market Agenda for Analytics: Empowering Workforces to Engage", "mainEntityOfPage": "https://davidmenninger.isg-one.com/the-2023-market-agenda-for-analytics-empowering-workforces-to-engage", "datePublished": "17/01/2023", "dateModified": "17/01/2023", "articleBody": "Ventana Research recently announced its 2023 Market Agenda for Analytics, continuing the guidance we have offered for nearly two decades to help organizations derive optimal value from technology investments to improve business outcomes. It has been exciting to follow innovations in the analytics market, but for businesses, capitalizing on these innovations can be challenging. To support this, I assert that through 2025, the majority of the workforce in more than one-half of organizations will still not be using analytics and business intelligence and, therefore, will not be executing their roles optimally. We craft our annual market agenda using our firm’s knowledge of technology vendors and products as well as our expertise in business requirements. Through this ongoing research, we offer insights and best practices that help both IT and the lines of business understand this dynamic market segment and use these technologies to their maximum potential. Our Analytics practice encompasses six focus areas: Artificial Intelligence and Machine Learning, Business Intelligence, Embedded Analytics, Natural Language Processing, Process Mining and Streaming Analytics. Our overarching Analytics and Data Benchmark Research explores each of these topics, providing insights to guide decision-making. And if you are interested in how well vendors’ products meet organizations’ requirements, then review our Analytics and Data Value Index, which will be updated this year. Here is our view of how each of these areas will advance in 2023. Artificial Intelligence and Machine Learning The application of artificial intelligence and machine learning within organizations is transforming analytics. Opportunities to take advantage of these technologies will increase over the coming year as vendors automate more AI/ML processes and further incorporate AI/ML capabilities in products. Our upcoming research will explore the extent to which these advances reduce the skills needed to reap the full benefit of AI/ML, but we assert that through 2025, AI/ML approaches will remain largely independent of business intelligence approaches, requiring three-quarters of organizations to maintain multiple, separate skill sets. For an assessment of how your organization stacks up in its use of this technology, take our short Dynamic Insights Survey on Machine Learning. Business intelligence produces insights from data to guide decision-making, employing an increasing variety of analytic, presentation and deployment techniques. Our recent research finds that reports and dashboards are still an important component of most organizations’ BI efforts, but it also shows growing interest in additional forms of analysis, including AI/ML and natural language processing. However, the research also shows that two-thirds (65%) of organizations lack the skills needed for AI/ML. These organizations require additional ways to take advantage of these sophisticated analytics. To support this, I assert that by 2025, 8 in 10 business intelligence vendors will include augmented intelligence to make analytics more robust and easier to use. In addition, to creating organizational agility, data and analytics processes are often supplemented with AI/ML-based assistance to enable non-technical workers to use these tools more effectively. This year we will continue to study how software vendors are incorporating these changes, and how well organizations are able to adopt these advances. Embedded Analytics For too long, analytics and business processes have been separate. Line-of-business workers do not have access to the analytics they need to perform their roles optimally. Our research shows that in more than three-quarters of organizations, half or less of the workforce is using analytics. Organizations can more directly support the workforce in various functions by embedding analytics into business processes and applications. Not only will it make analytics more accessible, but it will enable organizations to deliver more sophisticated analytics. I assert that by 2026, more than one-half of embedded analytics processes will include AI/ML to improve line-of-business decision-making. In our recent research, nearly three-quarters (73%) of organizations considered it important to embed analytics into business tools. BI vendors have recognized this need, and continue to invest in and improve application programming interfaces, making it easier for organizations to build custom analytics applications and for independent software vendors to include analytics in products. Natural Language Processing Conversational computing is becoming an increasingly common part of user interfaces. Smart speakers, mobile assistants and chatbots are propelling consumer interest in natural language capabilities. Nearly all BI vendors generate narratives based on a set of data using NLP. In some cases, vendors license these capabilities from other specialist vendors. Or, vendors developed unique capabilities. Most BI vendors also offer natural language queries, using text – and in some cases voice – as inputs to access information. BI vendors are refining natural language processing capabilities. Future enhancement by ML will improve language recognition, making NLP more accurate and easier to use over time. We also expect to see organizations demanding more multilingual capabilities if NLP is to live up to its promise of reaching more of the workforce. We expect that by 2026, one-half of conversational analytics will incorporate multilingual capabilities to support a larger portion of organizations’ employees. We will continue to explore these issues in our Natural Language Processing Dynamic Insights research, studying these advances and how organizations are embracing them. Process Mining Most business processes are supported by software applications. These applications provide a lens into the multitude of operational processes within an organization. In recent years, we have seen the emergence of a new category of technology that mines the information and statistics collected through these processes to analyze and improve them. We assert that by 2025, one-half of organizations will examine methods to gain intelligence on the events and activities of people and machines, elevating the importance for process-mining technology. Last year we added a focus area to study how this information can be analyzed to better understand and improve the operations of an organization. We’ve launched a new Dynamic Insights study in this area, and we invite you to participate. Streaming Analytics Data now streams into organizations from myriad sources, among them social media feeds and internet-of-things devices. The process of using that data is evolving as the amount and frequency of data collection increases. Including streaming data in analytics efforts enhances operational activities and processes to better support real-time decision-making and help make organizations more agile. We will continue to study these issues in our Streaming Data Dynamic Insights research. We expect that by 2025, one-half of organizations will incorporate streaming analytics into business processes, enabling faster response to opportunities and threats. Analytics is having a major impact across lines of business. It is changing the customer experience, allowing organizations to use data to optimize engagement. It is transforming finance operations, providing insights on performance and enabling more forward-looking planning. It is improving hiring and retention as part of the human capital management process. Sales, marketing and supply chain functions also stand to benefit from the context and guidance that well-implemented analytics can provide. Through our 2023 Market Agenda for Analytics, we will continue our relentless focus on helping organizations realize the valuable operational improvements that can come to light through effective data analysis. Subscribe to our Ventana Research community to stay up-to-date on our 2023 research efforts. Visit our Analytics expertise and focus areas for a detailed agenda and our continuously updated 90-day research calendar as well as additional research facts and best practices. Regards, David Menninger", "image": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/The%202023%20Market%20Agenda%20for%20Analytics%20Empowering%20Workforces%20to%20Engage.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "David Menninger", "url": "https://davidmenninger.isg-one.com/author/david-menninger", "sameAs": "http://www.linkedin.com/in/davidmenninger" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "davidmenninger.isg-one.com", "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": "2023 Digital Technology Market Agenda: Innovation for Digital Agility", "mainEntityOfPage": "https://davidmenninger.isg-one.com/2023-digital-technology-market-agenda-innovation-for-digital-agility", "datePublished": "11/01/2023", "dateModified": "11/01/2023", "articleBody": "I’m proud to share Ventana Research’s 2023 Market Agenda for Digital Technology. Our focus in this agenda is to deliver expertise to help organizations prioritize technology investments that improve customer, partner and workforce experiences while also increasing organizational effectiveness and agility. Digital technology is a business necessity, as demonstrated throughout the pandemic. It enables organizations to operate in the channels and with the speed and flexibility that customers have come to expect. In 2020, many organizations were not prepared to operate digitally. The lesson learned is that, even though significant digital transformation efforts have occurred over the last five-plus years, it is necessary to refine these efforts, ensuring an effective digital strategy that enables organizations to compete for and retain customers now and into the future. Our 2023 market agenda focuses on critical areas of digital technology, using insights and best practices to help organizations ensure optimal outcomes. We continue to track computing as a focus, since the complexities in managing applications and systems across the computing environment have introduced new challenges. In addition, the convergence of collaborative and conversational technologies supports a new generation of digital technology for enterprises. We assert that by 2026, more than one-half of organizations will have invested in newly formed digital platforms to unify collaborative and conversational technology and simplify the work experience. Digital technology helps organizations gain intelligence and launch automation that ensures ongoing levels of effectiveness for the overall business and within specific processes. Our Digital Technology expertise includes nine focus areas: Analytics and Data, Artificial Intelligence and Machine Learning, Blockchain, Cloud Computing, Collaborative and Conversational Computing, the Internet of Things, Mobile Computing, Robotic Automation and Extended Reality. Analytics and Data Today, every aspect of business relies on analytics and data. Organizations use analytics to apply AI/ML to all types of data, generating information and insights necessary to improve outcomes. Our agenda reflects the technology required to achieve this objective and provide data integration - the preparation and processing of data for analytic and operational use. We will also focus on business intelligence-based processes that generate visualization along with important insights across lines of business and IT. For 2023, we will further explore the need to embed analytics and ensure collaboration and availability across mobile technology. Also high on our list is natural language generation from data and analytics as well as the process for simplifying data access and presentation through human and machine-level interactions. Our Benchmark Research and Value Index will identify best practices and assess and rate vendors’ potential to help ensure the right technology is used to provide effective organizational agility. Artificial Intelligence and Machine Learning AI/ML enables the processing of data much the way humans do: providing a context to refine how organizations operate in any activity. Our agenda reflects our work to assess organizations’ use of AI/ML through market research designed to help guide technology and processes necessary for ethical operations. I assert that through 2025, one-half of organizations will realize that AI competencies and skills are insufficient and require new investments to avoid being at a competitive disadvantage. We will also examine the impact of ML- and AI-guided actions and tasks. Anyone can participate in our Dynamic Insights market research on ML that focuses on best practices in the successful deployment of AI/ML. Blockchain A shared database of assets stored in multiple locations of an inter-enterprise network can pose challenges to processing and utilizing the data for a range of digital requirements in operations and analytics. Understanding blockchain-distributed ledger technology is essential to help organizations more effectively operate across systems. Our market agenda is designed to assess the value of technologies that interconnect and secure data across systems and organizations to enable both secure and efficient processing. Our examination of blockchain processing across finance and the supply chain continues to help organizations optimize transactions. The effective application of data governance using enterprise-class integration methods and technologies is necessary to maintain data quality standards. We assert that by 2025, one-third of organizations will adopt blockchain for secure, distributed computing across business processes to realize efficient and effective governance of digital business. We will continue our market research on blockchain technology to determine how the technology is currently being used. In particular, we will examine how organizations effectively use blockchain technology to address gaps and opportunities in business processes and optimize operations across the enterprise and extended network of constituents. Cloud Computing Organizations continue to migrate toward computing technology operating outside of the traditional data center or internally managed servers. Our focus on cloud computing is intended to guide organizations on the use of applications and systems functioning across private and public technology environments. Through 2025, one-fifth of organizations will consolidate public cloud computing platforms to a single provider to redirect resource costs and efforts from infrastructure to the value derived from them. Unlike decades past, today’s challenge is managing the operation of applications and systems across noncorporate-owned environments that may require enhanced governance of service-level agreements necessary for reliability and security. With potentially dozens of providers, an organization may find it necessary to integrate and adapt applications to broader, enterprise-level requirements to achieve workflow and efficiency as well as further customize for competitive differentiation. Our research will examine how the portfolio of digital technologies in this area of expertise operate across cloud computing as well as the applications that are part of the entire enterprise. Collaborative and Conversational Computing Our market agenda in organization-wide collaboration and conversation involves technology-mediated digital engagement using natural language, across the workforce and beyond. This focused digital computing uses unified communication to enable interactions through text, video and voice to adapt to how people work. The use of mobile technology plays a key role in enabling real-time interactions that expedite agility and responsiveness. We assert that by 2025, one-quarter of organizations will have deployed conversational experiences from mobile and desktop environments to work experiences with technology designed to increase productivity. Real-time and recorded video deliver a human connection for collaboration, connecting any number of parties to explore an issue or opportunity. The advancement of intelligent virtual agents that are infused with AI/ML have introduced new opportunities for extending the potential of the workforce and ensure they maximize high-value skills. Our market research will continue to evaluate and guide organizations’ needs, plans and attitudes on collaborative and conversational computing technology to ensure effective interactions between people and machines. The Internet of Things Innovative organizations are embracing a new, intelligent approach to the efficiency of automation as well as the broader digital business opportunity that comes from the use of data in motion. The internet of things spans computing that crosses devices and sensors to transmit data through events and messages, enabling services and unlocking new computing and monetization opportunities. Through 2025, one-half of organizations using IoT technology will rely on real-time events and messages that stream to cloud-based data warehouses or data lake environments to increase operational insights. Event and messaging technologies that broadcast and stream are now making it possible to more continuously operate these technologies to support a broader agenda - from sustainability to business continuity objectives and business processes. Using machine and sensor data delivered in real time by IoT devices makes it possible to monitor and adjust both machine and workforce operations and intelligently respond to situations that require action. Our continuous Benchmark Research on IoT identifies and reports on trends and best practices in using the technology for any organizational purpose. Mobile Computing The simplification of mobile technology has not translated to innovation or advancement in operational software, leading to dissatisfaction from organizations with high levels of expectation. Our focus on mobile computing examines organizational use of smartphones, tablets and wearables to connect workers to applications and information. We also explore the disconnect between expected levels of digital business and responsiveness from these devices. In many cases, our research to date has found that user experiences via mobile devices have not facilitated the engagement found in consumer applications. We assert that through 2025, two-thirds of organizations will conclude that mobile business applications are ineffective for the workforce, requiring replacement to gain business effectiveness. Our agenda will help organizations root out and eliminate vendors with products that lack mobile technology functionality for ease of access and interaction for the worker and manager. Our continuous market research on the use of mobile computing technology - as well as organizations’ needs and attitudes - will bring best practices and guidance for making smarter decisions regarding vendor offerings. Robotic Automation For many organizations, the disparate operation of applications that support processes must be augmented to enable automation - and hopefully intelligence - for increased efficiency in digital business. Our focus on robotic automation will assess the technology that orchestrates the workflow of messages and transactions supporting activities and tasks, but also needs to balance the workload of resources across the organization. We assert that by 2025, over two-thirds of organizations will standardize to a single digital platform for intelligent workflow management and will deploy robotic process automation to eliminate redundant manual work. Advancements in robotic process automation have evolved to facilitate next-level operational effectiveness in business and IT processes. Our goal is to help organizations prioritize the technology and vendors supporting people and systems that operate across cloud-computing environments. Our continuous market research will examine how automation and intelligence are best instrumented to meet both front- and back-office requirements for workflow and workloads across the enterprise. Extended Reality Advancements in extended reality continue to exploit dedicated technology in mobile computing – including wearables – to operate in a virtualized environment. This can increase efficiency, addressing the gamut of exploration and improvements. Our XR expertise focuses on technology that operates individually and combines augmented, mixed and virtual environments for better experiences. We assert that by 2025, one-third of organizations will adopt XR technologies that augment and virtualize customer and product experiences for effective work engagement. As organizations study methods to service physical assets from remote locations and devices, the opportunity for digital innovation will come from suppliers and organizations working toward a common resolution. Our continuous market research will identify the rapidly evolving best practices in the use of XR across marketing, sales and service that enable efficient operations and engagement with customers and the supply chain. Improving the digital experience across the workforce as well as for customers, partners and the overall supply chain has become a business imperative for 2023. Enhancements to the digital workflows of processes and activities can improve organizational readiness to support the objectives and intentions of everyone in the workforce. Optimizing the path forward in 2023 will require a heightened sense of the purpose and operation of digital technology as well as an understanding of expected outcomes for enabling business continuity and performance. Establishing a priority focus for digital technology investments will ensure appropriate rewards for the time and resources dedicated to this necessity. Subscribe to our Ventana Research community at no cost to stay current on our 2023 research efforts. Explore our Digital Technology expertise and focus areas for more on the detailed research agenda and continuously updated 90-day calendar as well as expanded research facts and best practices. Regards, David Menninger", "image": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/2023%20Digital%20Technology%20Market%20Agenda%20Innovation%20for%20Digital%20Agility.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "David Menninger", "url": "https://davidmenninger.isg-one.com/author/david-menninger", "sameAs": "http://www.linkedin.com/in/davidmenninger" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "davidmenninger.isg-one.com", "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": "Master Metrics with Kyligence Zen", "mainEntityOfPage": "https://davidmenninger.isg-one.com/master-metrics-with-kyligence-zen", "datePublished": "23/12/2022", "dateModified": "23/12/2022", "articleBody": "Analytics processes are all about how organizations use data to create metrics that help manage and improve operations. Yet, the discipline applied to analytics processes seems to be lacking compared to data processes. I’ve pointed out that the weak link in data governance is often analytics. Organizations can also do a better job tying AnalyticOps to DataOps and do more to define and manage metrics. Our research has shown that creating and managing metrics in a semantic model improves analytics processes. Some analytics software vendors are starting to tackle this problem, offering semantic models, semantic layers, metrics stores and headless business intelligence. Kyligence is among those vendors. The company recently expanded its portfolio to include Kyligence Zen, which it refers to as a metrics platform. Zen includes four key components: a metrics catalog, metrics automation, goals and an application programming interface (API). The metrics catalog enables organizations to define and manage metrics, including the ability to import metrics definitions from business intelligence systems and data warehouses. Kyligence and its partners also provide a set of metrics templates which serve as a starting point for specific types of analyses. Metrics automation, another component of Zen, uses artificial intelligence and machine learning to extract information from usage logs, SQL history, data warehouses and data lakes to automatically generate and maintain entries in the metrics catalog. It’s a form of augmented intelligence, designed to make analytics processes easier. We assert that by 2025, 9 in 10 analytics processes will be enhanced by AI and machine learning to streamline operations and increase the value that can be derived from data. The goals portion of Zen focuses on a key aspect of metrics: comparing the value of a metric to its associated target. The goals module also aligns specific metrics with goals of the organization. These goals can be structured in a hierarchical fashion with subgoals, helping to drive overall goals. The API for Zen enables headless BI operations – in other words, programmatic access to the metrics that don’t require human intervention. For example, you may have metrics on cloud cost management that are monitored continuously to shift workloads from one type of resource to another in order to optimize costs. The metrics catalog at the core of Zen provides consistency and trust through a single repository for the various metrics shared across an organization. The value of Zen could be enhanced through further integration with more catalogs and business intelligence tools, extending the consistency across more data and analytics processes. Organizations need to be more disciplined about metrics processes, which deliver significantly increased satisfaction with analytics and better governance capabilities. Kyligence Zen provides a metrics platform that enables that discipline. I recommend that organizations looking for better management of metrics consider Kyligence Zen. Regards, David Menninger", "image": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/Master%20Metrics%20with%20Kyligence%20Zen.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "David Menninger", "url": "https://davidmenninger.isg-one.com/author/david-menninger", "sameAs": "http://www.linkedin.com/in/davidmenninger" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "davidmenninger.isg-one.com", "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": "Enhancing Data Catalog with AI", "mainEntityOfPage": "https://davidmenninger.isg-one.com/enhancing-data-catalog-with-ai", "datePublished": "22/09/2022", "dateModified": "22/09/2022", "articleBody": "Organizations are collecting data from multiple data sources and a variety of systems to enrich their analytics and business intelligence (BI). But collecting data is only half of the equation. As the data grows, it becomes challenging to find the right data at the right time. Many organizations can’t take full advantage of their data lakes because they don’t know what data actually exists. Also, there are more regulations and compliance requirements than ever before. It is critical for organizations to understand the kind of data they have, who is handling it, what it is being used for and how it needs to be protected. They also have to avoid putting too many layers and wrappers around the data as it can make the data difficult to access. These challenges create a need for more automated ways to discover, track, research and govern the data. Data catalogs have become the standard for maintaining metadata for data analytics and self-service BI. They provide a roadmap to all the data sources, both internal and external, and enable data professionals and business users to swiftly sort through the inventory of data assets to find the information they need. Our Analytics and Data Benchmark Research finds that organizations with adequate data catalog technologies reported significantly higher rates of satisfaction (60% compared to 20% of those with inadequate technologies). But, considering the volume and variety of data that organizations deal with today, it becomes difficult to keep the data catalogs regularly updated. Manually searching the database and linking all metadata to the data catalog is a time-consuming and resource-intensive process. And the challenges are further magnified when organizations look to scale such manual methods as the data volume, complexity and sources increase. Artificial intelligence and machine learning (AI/ML) are transforming the data catalog landscape. Machine learning algorithms can be trained to browse through data catalogs to collect metadata and keep it updated with new, incoming information. AI/ML learns from data patterns, queries and interactions to support both data quality and governance. It enables organizations to expand the available data variety, standardize data semantics and simplify data accessibility. Many organizations have started embracing more sophisticated catalogs with AI/ML capabilities to scale operations and harness insights that would otherwise be overlooked. Data management vendors are continuously improving AI/ML capabilities and integrating them into their offerings to enable users to discover, refine, explore and analyze data sets more rapidly and efficiently. AI/ML make it possible for organizations to democratize data, allowing users to easily navigate unstructured data, uncover patterns in data sets and understand the movement and transformation of data through time and data lineage. We assert that by 2025, more than two-thirds of all data processes will use AI/ML to boost the value that can be derived from the data. Additionally, AI/ML can automate identification and tagging of data. It detects unusual usage as well as identify sensitive data and assign protection schemes. AI/ML can assist with many data processes beyond just data privacy. It provides recommendations on what data sources to use and how to use data. It can access data quality and resolve quality issues. With an AI-driven data catalog, organizations can simplify data compliance and governance and standardize the way data is stored and labelled. A data catalog should be a component of every organization’s data governance framework. Many vendors offer integrated data catalog capabilities for data governance, analytics and BI. Organizations should evaluate the AI/ML capabilities of the data catalog technologies they consider and how they can make it easier to find and use data, which will help improve operational results. Regards, David Menninger", "image": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/AP_DM_Enhancing%20Data%20Catalog%20with%20AI.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "David Menninger", "url": "https://davidmenninger.isg-one.com/author/david-menninger", "sameAs": "http://www.linkedin.com/in/davidmenninger" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "davidmenninger.isg-one.com", "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": "Don’t Rely on Dashboards for Real-Time Analytics", "mainEntityOfPage": "https://davidmenninger.isg-one.com/dont-rely-on-dashboards-for-real-time-analytics", "datePublished": "31/03/2022", "dateModified": "31/03/2022", "articleBody": "I have written previously that the world of data and analytics will become more and more centered around real-time, streaming data. Data is created constantly and increasingly is being collected simultaneously. Technology advances now enable organizations to process and analyze information as it is being collected to respond in real time to opportunities and threats. Not all use cases require real-time analysis and response, but many do, including multiple use cases that can improve customer experiences. For example, best-in-class e-commerce interactions should provide real-time updates on inventory status to avoid stock-out or back-order situations. Customer service interactions should provide real-time recommendations that minimize the time to resolution. Location-based offers should be targeted at the customer’s current location, not their location several minutes ago. Another domain where real-time analyses are critical is internet of things (IoT) applications. Additionally, use cases like predictive maintenance require timely information to prevent equipment failures that help avoid additional costs and damage. Our Analytics and Data Benchmark Research shows almost one-quarter (22%) of participants conduct real-time analyses of their data. Another 10% are analyzing data at least hourly. The research also shows a correlation between the frequency of analysis and an organization’s satisfaction with their analytics. Organizations performing real-time analyses report the highest levels of satisfaction. Those performing weekly or monthly analyses report the lowest levels of satisfaction. This correlation is not surprising. Individuals need timely analysis to inform their decision-making processes. We assert that by 2024, one-half of organizations will incorporate streaming analytics into their business processes, enabling them to respond to opportunities and threats faster. The research also explores whether organizations have adequate technologies to support different types of analyses. Half as many organizations (19%) report they have completely adequate technologies for real-time analysis compared with those that have completely adequate technologies for reports (38%) and dashboards (38%). Real-time analyses require a different approach. Having someone sit around and watch a dashboard waiting for information to change is not a very effective way to monitor changes. The data may be changing so frequently that it is hard to even see what is changing. Even if someone were to identify and understand the changes, would they be able to determine the correct response before it was too late? A proactive rather than reactive approach is needed. At a minimum, alerts and notifications can be used to draw attention to issues as they arise rather than expecting someone to watch a dashboard. Unfortunately, the technologies for alerts and notifications are not considered to be much better than the technologies for real-time analysis. It only solves one part of the problem — identifying an issue. Determining and implementing the correct response in real time is the other part of the challenge. In simple cases, evaluating and selecting the correct response can be done with heuristics or straightforward logic. However, it is more likely that artificial intelligence and machine learning (AI/ML) will be needed to effectively evaluate and select an appropriate response. Once an appropriate response is selected, it may need to be reviewed and approved (perhaps via an alert) and then implemented. The implementation may be a recommendation on a customer service screen, it may be updating the inventory status on an e-commerce screen, it may be placing a replenishment order, or it may be a combination of several actions. As organizations become more proactive and oriented around real-time processes, they need to adopt an event-driven approach to their operations. Nearly one-half (48%) are using streaming data in their operational processes, yet only 15% report their event-streaming technologies are completely adequate. Real-time and streaming require a focus on event-driven architectures to support responsiveness and are on the path to what we call a digital business. Analytics vendors must recognize the needs organizations have for real-time operations. They will not be solved by reports and dashboards. Organizations need better alert and notification capabilities. They also require improved AI/ML capabilities to evaluate the appropriate actions to take. And they need enhanced agent technologies to connect the various applications — analytical and operational — involved in taking action. In the meantime, organizations should consider third-party technologies for some of these capabilities. We continue to study the issues associated with real-time analyses and event-driven architectures in our Streaming Data Dynamic Insights Research. Please contribute to this research with input from your organization. Regards, David Menninger", "image": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/AP_DM_Don%E2%80%99t%20Rely%20on%20Dashboards%20for%20Real-Time%20Analytics.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "David Menninger", "url": "https://davidmenninger.isg-one.com/author/david-menninger", "sameAs": "http://www.linkedin.com/in/davidmenninger" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "davidmenninger.isg-one.com", "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": "Working Across the Aisle in Analytics: Involving IT and LOB", "mainEntityOfPage": "https://davidmenninger.isg-one.com/working-across-the-aisle-in-analytics-involving-it-and-lob", "datePublished": "23/03/2022", "dateModified": "23/03/2022", "articleBody": "For years, maybe decades, we have heard about the struggles between IT and line-of-business functions. In this perspective, we will look at some of the data from our Analytics and Data Benchmark Research about the roles of IT and line-of-business teams in analytics and data processes. We will also look at some of the disconnects between these two groups. And, by looking at how organizations are operating today and the results they are achieving, we can discern some of the best practices for improving the outcomes of analytics and data processes. Let’s start with some of the disconnects. The IT function generally has a slightly rosier picture of the state of analytics technologies than the line-of-business function. For example, 45% of IT participants responded that their reporting technology was completely adequate while only 36% of line-of-business participants did. Similarly, 31% of IT reported that its interactive visualization technology was completely adequate while only 23% of line of business did. IT also had a slightly better opinion of the dashboard and ad hoc query technologies. The same is true for most of the data technologies, with data preparation and data integration showing some of the biggest differences of opinion. IT also exhibits more confidence in its ability to analyze large volumes of data and high-velocity data. Two-thirds (66%) of IT participants reported they are confident in their organization’s ability to analyze large volumes of data while 57% of line-of-business participants reported they are confident. When it comes to analyzing data arriving at high velocity, the numbers are 56% compared with 46%, respectively. Let’s turn our attention to the roles of each of these functions in analytics and data processes. There are a wide variety of ways in which organizations assign responsibility for designing and deploying analytics and data. The most common is to a cross-functional team which occurs in 3 in ten organizations. In one-quarter of organizations, line-of-business analysts lead the effort, and in 1 in 5 organizations (21%), a team of business intelligence and data warehousing (BI/DW) specialists from within IT leads the effort. In most cases, the participants in our research prepared analytics and data for others or architected solutions for others. Only 3 in ten were in a line-of-business function and prepared their own analyses. We can’t necessarily conclude that most organizations prepare their analytics this way but coupled with the fact that analytics are still not used by the majority of the workforce in three-quarters (76%) of organizations, it seems like a reasonable assumption. As we look at which efforts, by whom, are correlated with success, it appears that the more involved BI/DW specialist teams are, the more successful the outcomes. Further, it appears that cross-functional teams offer the next best results. Whereas efforts led by line-of-business analysts and other IT functions have less successful outcomes. Two of the measures we look at as broad indicators of success are satisfaction with, and confidence in, analytics. When BI/DW teams lead analytics and data design and deployment, their organizations report 86% satisfaction rates. Analytics efforts led by cross-functional teams report the next highest rates of satisfaction at 78%, followed by line-of-business analysts at 70% and other IT functions at 60%. We see similar results in the confidence organizations report about their ability to analyze data. As you consider best practices for designing and deploying analytics, don’t undervalue the role that IT can play. As I have written previously, analytics need to become much easier to use. Until they do, having technically trained and knowledgeable resources involved in analytics and data efforts appears to be a common thread among those organizations that are achieving the most success. In particular, if you don’t already have a BI/DW center of excellence or competency center, you should consider establishing one. At a minimum, you should consider a cross-functional team to lead analytics and data efforts to ensure your organization has the knowledge it needs to succeed. Regards, David Menninger", "image": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/AP_DM_Working%20Across%20the%20Aisle%20in%20Analytics_Involving%20IT%20and%20LOB.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "David Menninger", "url": "https://davidmenninger.isg-one.com/author/david-menninger", "sameAs": "http://www.linkedin.com/in/davidmenninger" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "davidmenninger.isg-one.com", "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": "Improving the State of Analytics in Organizations", "mainEntityOfPage": "https://davidmenninger.isg-one.com/improving-the-state-of-analytics-in-organizations", "datePublished": "24/02/2022", "dateModified": "24/02/2022", "articleBody": "Despite all the advances organizations have made with respect to analytics, our most recent research shows the majority of the workforce in the majority of organizations are not using analytics and business intelligence (BI). Less than one-quarter (23%) report that one-half or more of their workforce is using analytics and BI. This is a problem. It means organizations are not enabling their workforce to perform at peak efficiency and effectiveness. It means the workforce in many organizations does not have access to the same information by which they are being measured. It means organizations must find other ways to communicate with, and manage, the workforce. My theory about why this situation exists is that analytics are hard to use. They require skills that many people do not have. Some organizations believe the way to solve this problem is to raise data literacy in the workforce: educate and train people to have a better understanding of data and analytics. Our research shows that training in using analytics may be helpful. Those with better training report better outcomes from their analytics efforts. However, while training may be appropriate as a goal for some personas, I think we can do better for other personas. Rather than train the workforce to do their work in a different, more “data literate” way, why not enhance analytics and adapt them to the way people work? More than a decade ago, I wrote that BI vendors have been attempting to solve the wrong problem, trying to bring users to BI rather than bringing BI to users. Since that time, satisfaction rates have remained largely unchanged. We reported 37% of participants in our research were satisfied then and in our most recent Analytics and Data Benchmark Research 33% reported they are satisfied now. Sure, expectations are higher now, but technology is also much more powerful. In addition, we have much more data available to analyze and advanced analytics are much more widely available. So, one might expect that satisfaction rates would have risen. Organizations can increase both the adoption and satisfaction rates for analytics with a two-pronged approach that includes investment in natural language processing and in embedded analytics. It appears that organizations understand the need to embed analytics into operational processes with nearly two-thirds (63%) indicating it is important or very important. However, less than one-third (29%) are using natural language presentation or consider it very important and less than one-quarter (21%) are using natural language search or consider it very important. Organizations should strive to increase both the satisfaction rates and the adoption rates for analytics. They should consider different measures for different personas. Raising data literacy is valuable for certain personas and will likely increase satisfaction among those individuals. It may not increase adoption much though. To increase adoption, organizations need to take a different approach. They need to consider ways to make analytics more accessible. Embedded analytics and natural language processing are two ways that make analytics more accessible to many of the personas that have been left out in the past. Regards, David Menninger", "image": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/AP_DM_Improving%20the%20State%20of%20Analytics%20in%20Organizations.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "David Menninger", "url": "https://davidmenninger.isg-one.com/author/david-menninger", "sameAs": "http://www.linkedin.com/in/davidmenninger" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "davidmenninger.isg-one.com", "logo": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/ISG_Software_Research_Logo_Color@2x.png" } } }</script> <!--[if lte IE 8]> <script charset="utf-8" src="https://js.hsforms.net/forms/v2-legacy.js"></script> <![endif]--> <script data-hs-allowed="true" src="/_hcms/forms/v2.js"></script> <script data-hs-allowed="true"> var options = { portalId: '2035844', formId: 'fa276fc9-9a16-4673-a644-1fcbedcc3c24', formInstanceId: '4759', pageId: '4221377247', region: 'na1', inlineMessage: "Thanks for subscribing!", rawInlineMessage: "Thanks for subscribing!", hsFormKey: "cddd4b81f86d3aa2919ef2835fecf6ed", css: '', target: '#hs_form_target_module_1552944263900179', contentType: "listing-page", formsBaseUrl: '/_hcms/forms/', formData: { cssClass: 'hs-form stacked hs-custom-form' } }; 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