ISG Software Research Analyst Perspectives

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But as I have remarked elsewhere, it is not new. I recall a conversation with a customer over 30 years ago when I was involved with <span style="color: black;">analytic software who asked, “Why can’t we just pay for the software we use?” And this of course makes sense: only pay for what you use. In fact, I assert that <span style="background-color: white;">by 2027, over one-half of all enterprises will deploy a mixed revenue...</span></span></p> </div> <a href="https://research.isg-one.com/analyst-perspectives/how-software-can-help-usage-pricing-adoption"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://research.isg-one.com/analyst-perspectives/topic/subscription-management">Subscription Management</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/revenue-management">Revenue Management</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/office-of-revenue">Office of Revenue</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://research.isg-one.com/analyst-perspectives/adapting-to-higher-inflation-longer-with-profitability-management">Adapting to Higher Inflation Longer with Profitability Management</a></h2> <div class="author-meta"> Posted by <a href="https://research.isg-one.com/analyst-perspectives/author/robert-kugel"> Robert Kugel </a> on <span class="post-date">05 February 2025 </span> </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://research.isg-one.com/hs-fs/hubfs/Adapting_to_Higher_Inflation_Longer_with_Profitability_Management.png?width=200&name=Adapting_to_Higher_Inflation_Longer_with_Profitability_Management.png" width="200" !important: srcset="https://research.isg-one.com/hs-fs/hubfs/Adapting_to_Higher_Inflation_Longer_with_Profitability_Management.png?width=100&name=Adapting_to_Higher_Inflation_Longer_with_Profitability_Management.png 100w, https://research.isg-one.com/hs-fs/hubfs/Adapting_to_Higher_Inflation_Longer_with_Profitability_Management.png?width=200&name=Adapting_to_Higher_Inflation_Longer_with_Profitability_Management.png 200w, https://research.isg-one.com/hs-fs/hubfs/Adapting_to_Higher_Inflation_Longer_with_Profitability_Management.png?width=300&name=Adapting_to_Higher_Inflation_Longer_with_Profitability_Management.png 300w, https://research.isg-one.com/hs-fs/hubfs/Adapting_to_Higher_Inflation_Longer_with_Profitability_Management.png?width=400&name=Adapting_to_Higher_Inflation_Longer_with_Profitability_Management.png 400w, https://research.isg-one.com/hs-fs/hubfs/Adapting_to_Higher_Inflation_Longer_with_Profitability_Management.png?width=500&name=Adapting_to_Higher_Inflation_Longer_with_Profitability_Management.png 500w, https://research.isg-one.com/hs-fs/hubfs/Adapting_to_Higher_Inflation_Longer_with_Profitability_Management.png?width=600&name=Adapting_to_Higher_Inflation_Longer_with_Profitability_Management.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p><span>In anticipation of conditions that suggested accelerating price increases, I wrote in 2021 about <a href="https://robertkugel.isg-research.net/managing-price-inflation-effectively-with-technology">how technology could be useful in an inflationary period</a>, anticipating the world we live in now. Responding effectively to changes in costs is always challenging, but even more so because of the choppy and chaotic nature of the current environment. Inflation may have abated from its highs throughout the rich world but is still an important factor in business decisions. Many enterprises have limited...</span></p> </div> <a href="https://research.isg-one.com/analyst-perspectives/adapting-to-higher-inflation-longer-with-profitability-management"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://research.isg-one.com/analyst-perspectives/topic/office-of-finance">Office of Finance</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/business-planning">Business Planning</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/erp-and-continuous-accounting">ERP and Continuous Accounting</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/subscription-management">Subscription Management</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/digital-finance">digital finance</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/revenue-management">Revenue Management</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://research.isg-one.com/analyst-perspectives/redpanda-lowers-the-complexity-and-cost-of-streaming-and-event-data">Redpanda Lowers the Complexity and Cost of Streaming and Event Data</a></h2> <div class="author-meta"> Posted by <a href="https://research.isg-one.com/analyst-perspectives/author/matt-aslett"> Matt Aslett </a> on <span class="post-date">04 February 2025 </span> </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://research.isg-one.com/hs-fs/hubfs/Redpanda_Lowers_the_Complexity_and_Cost_of_Streaming_and_Event_Data.png?width=200&name=Redpanda_Lowers_the_Complexity_and_Cost_of_Streaming_and_Event_Data.png" width="200" !important: srcset="https://research.isg-one.com/hs-fs/hubfs/Redpanda_Lowers_the_Complexity_and_Cost_of_Streaming_and_Event_Data.png?width=100&name=Redpanda_Lowers_the_Complexity_and_Cost_of_Streaming_and_Event_Data.png 100w, https://research.isg-one.com/hs-fs/hubfs/Redpanda_Lowers_the_Complexity_and_Cost_of_Streaming_and_Event_Data.png?width=200&name=Redpanda_Lowers_the_Complexity_and_Cost_of_Streaming_and_Event_Data.png 200w, https://research.isg-one.com/hs-fs/hubfs/Redpanda_Lowers_the_Complexity_and_Cost_of_Streaming_and_Event_Data.png?width=300&name=Redpanda_Lowers_the_Complexity_and_Cost_of_Streaming_and_Event_Data.png 300w, https://research.isg-one.com/hs-fs/hubfs/Redpanda_Lowers_the_Complexity_and_Cost_of_Streaming_and_Event_Data.png?width=400&name=Redpanda_Lowers_the_Complexity_and_Cost_of_Streaming_and_Event_Data.png 400w, https://research.isg-one.com/hs-fs/hubfs/Redpanda_Lowers_the_Complexity_and_Cost_of_Streaming_and_Event_Data.png?width=500&name=Redpanda_Lowers_the_Complexity_and_Cost_of_Streaming_and_Event_Data.png 500w, https://research.isg-one.com/hs-fs/hubfs/Redpanda_Lowers_the_Complexity_and_Cost_of_Streaming_and_Event_Data.png?width=600&name=Redpanda_Lowers_the_Complexity_and_Cost_of_Streaming_and_Event_Data.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p><span style="color: black;">I recently wrote about the need for enterprises to </span><span><a href="https://mattaslett.isg-research.net/harness-events-to-process-data-at-the-speed-of-business">harness events</a></span> <span style="color: black;">to </span><span style="color: black; background-color: white;">process and act upon data at the speed of business. The </span><span><a href="https://blog.isg-research.net/eda-with-events-brokers-enable-stream-processing"><span style="background-color: white;">core technologies</span></a></span><span style="color: black; background-color: white;">that enable enterprises to process and analyze data in real time have been in existence for many years and are widely adopted. However, streaming and events technologies are also commonly seen as a niche requirement, separate from an enterprise’s primary focus on batch processing of data at rest. One of the reasons for this is an entrenched reliance on...</span></p> </div> <a href="https://research.isg-one.com/analyst-perspectives/redpanda-lowers-the-complexity-and-cost-of-streaming-and-event-data"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://research.isg-one.com/analyst-perspectives/topic/analytics-data">Analytics & Data</a>, <a href="https://research.isg-one.com/analyst-perspectives/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://research.isg-one.com/analyst-perspectives/databricks-scores-massive-funding-round-continues-to-expand-its-offerings">Databricks Scores Massive Funding Round, Continues to Expand Its Offerings</a></h2> <div class="author-meta"> Posted by <a href="https://research.isg-one.com/analyst-perspectives/author/david-menninger"> David Menninger </a> on <span class="post-date">29 January 2025 </span> </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://research.isg-one.com/hs-fs/hubfs/Databricks_Scores_Massive_Funding_Round_Continues_to_Expand_Its_Offerings.png?width=200&name=Databricks_Scores_Massive_Funding_Round_Continues_to_Expand_Its_Offerings.png" width="200" !important: srcset="https://research.isg-one.com/hs-fs/hubfs/Databricks_Scores_Massive_Funding_Round_Continues_to_Expand_Its_Offerings.png?width=100&name=Databricks_Scores_Massive_Funding_Round_Continues_to_Expand_Its_Offerings.png 100w, https://research.isg-one.com/hs-fs/hubfs/Databricks_Scores_Massive_Funding_Round_Continues_to_Expand_Its_Offerings.png?width=200&name=Databricks_Scores_Massive_Funding_Round_Continues_to_Expand_Its_Offerings.png 200w, https://research.isg-one.com/hs-fs/hubfs/Databricks_Scores_Massive_Funding_Round_Continues_to_Expand_Its_Offerings.png?width=300&name=Databricks_Scores_Massive_Funding_Round_Continues_to_Expand_Its_Offerings.png 300w, https://research.isg-one.com/hs-fs/hubfs/Databricks_Scores_Massive_Funding_Round_Continues_to_Expand_Its_Offerings.png?width=400&name=Databricks_Scores_Massive_Funding_Round_Continues_to_Expand_Its_Offerings.png 400w, https://research.isg-one.com/hs-fs/hubfs/Databricks_Scores_Massive_Funding_Round_Continues_to_Expand_Its_Offerings.png?width=500&name=Databricks_Scores_Massive_Funding_Round_Continues_to_Expand_Its_Offerings.png 500w, https://research.isg-one.com/hs-fs/hubfs/Databricks_Scores_Massive_Funding_Round_Continues_to_Expand_Its_Offerings.png?width=600&name=Databricks_Scores_Massive_Funding_Round_Continues_to_Expand_Its_Offerings.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p>Databricks <span><a href="https://www.databricks.com/company/newsroom/press-releases/databricks-raising-10b-series-j-investment-62b-valuation">recently announced</a></span> its Series J funding round, successfully raising $10 billion at a valuation of $62 billion. Led by Thrive Capital alongside high-profile investors such as Andreessen Horowitz and Insight Partners, the company intends to invest this capital towards new artificial intelligence (AI) products, acquisitions and significant expansion of its international operations. In the announcement, Databricks reported that it expects to achieve an annual revenue run rate of $3...</p> </div> <a href="https://research.isg-one.com/analyst-perspectives/databricks-scores-massive-funding-round-continues-to-expand-its-offerings"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://research.isg-one.com/analyst-perspectives/topic/analytics">Analytics</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/ai">AI</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/ai-machine-learning">AI & Machine Learning</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/data-platforms">data platforms</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/model-building-and-large-language-models">Model Building and Large Language Models</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/data-intelligence">Data Intelligence</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/analytics-and-data">Analytics and Data</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://research.isg-one.com/analyst-perspectives/data-catalogs-serve-multiple-roles-and-use-cases">Data Catalogs Serve Multiple Roles and Use Cases</a></h2> <div class="author-meta"> Posted by <a href="https://research.isg-one.com/analyst-perspectives/author/matt-aslett"> Matt Aslett </a> on <span class="post-date">29 January 2025 </span> </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://research.isg-one.com/hs-fs/hubfs/Data_Catalogs_Serve_Multiple_Roles_and_Use_Cases.png?width=200&name=Data_Catalogs_Serve_Multiple_Roles_and_Use_Cases.png" width="200" !important: srcset="https://research.isg-one.com/hs-fs/hubfs/Data_Catalogs_Serve_Multiple_Roles_and_Use_Cases.png?width=100&name=Data_Catalogs_Serve_Multiple_Roles_and_Use_Cases.png 100w, https://research.isg-one.com/hs-fs/hubfs/Data_Catalogs_Serve_Multiple_Roles_and_Use_Cases.png?width=200&name=Data_Catalogs_Serve_Multiple_Roles_and_Use_Cases.png 200w, https://research.isg-one.com/hs-fs/hubfs/Data_Catalogs_Serve_Multiple_Roles_and_Use_Cases.png?width=300&name=Data_Catalogs_Serve_Multiple_Roles_and_Use_Cases.png 300w, https://research.isg-one.com/hs-fs/hubfs/Data_Catalogs_Serve_Multiple_Roles_and_Use_Cases.png?width=400&name=Data_Catalogs_Serve_Multiple_Roles_and_Use_Cases.png 400w, https://research.isg-one.com/hs-fs/hubfs/Data_Catalogs_Serve_Multiple_Roles_and_Use_Cases.png?width=500&name=Data_Catalogs_Serve_Multiple_Roles_and_Use_Cases.png 500w, https://research.isg-one.com/hs-fs/hubfs/Data_Catalogs_Serve_Multiple_Roles_and_Use_Cases.png?width=600&name=Data_Catalogs_Serve_Multiple_Roles_and_Use_Cases.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p>Metadata management has played a role in data governance and <span><a href="https://www.isg-research.net/focus/analytics-and-data/analytics">analytics</a></span> for many years. It wasn’t until the emergence of the data catalog as a product category just over a decade ago that enterprises had a platform for metadata-driven data management that could span multiple departments and use cases across an entire enterprise.</p> </div> <a href="https://research.isg-one.com/analyst-perspectives/data-catalogs-serve-multiple-roles-and-use-cases"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://research.isg-one.com/analyst-perspectives/topic/ai-machine-learning">AI & Machine Learning</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/analytics-data">Analytics & Data</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/data-intelligence">Data Intelligence</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://research.isg-one.com/analyst-perspectives/esg-management-software-is-essential-for-efficient-compliance">ESG Management Software is Essential for Efficient Compliance</a></h2> <div class="author-meta"> Posted by <a href="https://research.isg-one.com/analyst-perspectives/author/robert-kugel"> Robert Kugel </a> on <span class="post-date">28 January 2025 </span> </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://research.isg-one.com/hs-fs/hubfs/ESG_Management_Software_is_Essential_for_Efficient_Compliance.png?width=200&name=ESG_Management_Software_is_Essential_for_Efficient_Compliance.png" width="200" !important: srcset="https://research.isg-one.com/hs-fs/hubfs/ESG_Management_Software_is_Essential_for_Efficient_Compliance.png?width=100&name=ESG_Management_Software_is_Essential_for_Efficient_Compliance.png 100w, https://research.isg-one.com/hs-fs/hubfs/ESG_Management_Software_is_Essential_for_Efficient_Compliance.png?width=200&name=ESG_Management_Software_is_Essential_for_Efficient_Compliance.png 200w, https://research.isg-one.com/hs-fs/hubfs/ESG_Management_Software_is_Essential_for_Efficient_Compliance.png?width=300&name=ESG_Management_Software_is_Essential_for_Efficient_Compliance.png 300w, https://research.isg-one.com/hs-fs/hubfs/ESG_Management_Software_is_Essential_for_Efficient_Compliance.png?width=400&name=ESG_Management_Software_is_Essential_for_Efficient_Compliance.png 400w, https://research.isg-one.com/hs-fs/hubfs/ESG_Management_Software_is_Essential_for_Efficient_Compliance.png?width=500&name=ESG_Management_Software_is_Essential_for_Efficient_Compliance.png 500w, https://research.isg-one.com/hs-fs/hubfs/ESG_Management_Software_is_Essential_for_Efficient_Compliance.png?width=600&name=ESG_Management_Software_is_Essential_for_Efficient_Compliance.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p>Enterprises have been investing in software to manage their environmental, social and governance (ESG) objectives and compliance requirements. The need to account for these considerations in parallel with financial accounting began growing early in the century and accelerated as governments and regulatory authorities began to require companies to measure and document activities and outcomes. Meanwhile, software providers have been offering products to address the needs of those subject to these...</p> </div> <a href="https://research.isg-one.com/analyst-perspectives/esg-management-software-is-essential-for-efficient-compliance"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://research.isg-one.com/analyst-perspectives/topic/office-of-finance">Office of Finance</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/erp-and-continuous-accounting">ERP and Continuous Accounting</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/digital-finance">digital finance</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/sustainability-management">Sustainability Management</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://research.isg-one.com/analyst-perspectives/the-impact-of-the-cloud-and-ai-on-evolving-data-platform-requirements">The Impact of the Cloud and AI on Evolving Data Platform Requirements</a></h2> <div class="author-meta"> Posted by <a href="https://research.isg-one.com/analyst-perspectives/author/isg-software-research"> ISG Software Research </a> on <span class="post-date">23 January 2025 </span> </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://research.isg-one.com/hs-fs/hubfs/The_Impact_of_the_Cloud_and_AI_on_Evolving_Data_Platform_Requirements.png?width=200&name=The_Impact_of_the_Cloud_and_AI_on_Evolving_Data_Platform_Requirements.png" width="200" !important: srcset="https://research.isg-one.com/hs-fs/hubfs/The_Impact_of_the_Cloud_and_AI_on_Evolving_Data_Platform_Requirements.png?width=100&name=The_Impact_of_the_Cloud_and_AI_on_Evolving_Data_Platform_Requirements.png 100w, https://research.isg-one.com/hs-fs/hubfs/The_Impact_of_the_Cloud_and_AI_on_Evolving_Data_Platform_Requirements.png?width=200&name=The_Impact_of_the_Cloud_and_AI_on_Evolving_Data_Platform_Requirements.png 200w, https://research.isg-one.com/hs-fs/hubfs/The_Impact_of_the_Cloud_and_AI_on_Evolving_Data_Platform_Requirements.png?width=300&name=The_Impact_of_the_Cloud_and_AI_on_Evolving_Data_Platform_Requirements.png 300w, https://research.isg-one.com/hs-fs/hubfs/The_Impact_of_the_Cloud_and_AI_on_Evolving_Data_Platform_Requirements.png?width=400&name=The_Impact_of_the_Cloud_and_AI_on_Evolving_Data_Platform_Requirements.png 400w, https://research.isg-one.com/hs-fs/hubfs/The_Impact_of_the_Cloud_and_AI_on_Evolving_Data_Platform_Requirements.png?width=500&name=The_Impact_of_the_Cloud_and_AI_on_Evolving_Data_Platform_Requirements.png 500w, https://research.isg-one.com/hs-fs/hubfs/The_Impact_of_the_Cloud_and_AI_on_Evolving_Data_Platform_Requirements.png?width=600&name=The_Impact_of_the_Cloud_and_AI_on_Evolving_Data_Platform_Requirements.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p>The degree to which data platforms are critical to efficient business operations cannot be overstated. Without data platforms, enterprises would be reliant on a combination of paper records, time-consuming manual processes and huge libraries of physical files to record, process and store business information. The extent to which that is unthinkable highlights the level at which today’s enterprises and society as a whole rely on data platforms. The core persistence, management, processing and...</p> </div> <a href="https://research.isg-one.com/analyst-perspectives/the-impact-of-the-cloud-and-ai-on-evolving-data-platform-requirements"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://research.isg-one.com/analyst-perspectives/topic/analytics">Analytics</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/ai-machine-learning">AI & Machine Learning</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/data-platforms">data platforms</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/analytics-and-data">Analytics and Data</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://research.isg-one.com/analyst-perspectives/intercompany-financial-management-is-essential-for-multinationals">Intercompany Financial Management is Essential for Multinationals</a></h2> <div class="author-meta"> Posted by <a href="https://research.isg-one.com/analyst-perspectives/author/robert-kugel"> Robert Kugel </a> on <span class="post-date">22 January 2025 </span> </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://research.isg-one.com/hs-fs/hubfs/Intercompany_Financial_Management_is_Essential_for_Multinationals.png?width=200&name=Intercompany_Financial_Management_is_Essential_for_Multinationals.png" width="200" !important: srcset="https://research.isg-one.com/hs-fs/hubfs/Intercompany_Financial_Management_is_Essential_for_Multinationals.png?width=100&name=Intercompany_Financial_Management_is_Essential_for_Multinationals.png 100w, https://research.isg-one.com/hs-fs/hubfs/Intercompany_Financial_Management_is_Essential_for_Multinationals.png?width=200&name=Intercompany_Financial_Management_is_Essential_for_Multinationals.png 200w, https://research.isg-one.com/hs-fs/hubfs/Intercompany_Financial_Management_is_Essential_for_Multinationals.png?width=300&name=Intercompany_Financial_Management_is_Essential_for_Multinationals.png 300w, https://research.isg-one.com/hs-fs/hubfs/Intercompany_Financial_Management_is_Essential_for_Multinationals.png?width=400&name=Intercompany_Financial_Management_is_Essential_for_Multinationals.png 400w, https://research.isg-one.com/hs-fs/hubfs/Intercompany_Financial_Management_is_Essential_for_Multinationals.png?width=500&name=Intercompany_Financial_Management_is_Essential_for_Multinationals.png 500w, https://research.isg-one.com/hs-fs/hubfs/Intercompany_Financial_Management_is_Essential_for_Multinationals.png?width=600&name=Intercompany_Financial_Management_is_Essential_for_Multinationals.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p><span>ISG Software Research coined the term intercompany financial management to define a discipline for structuring and handling transactions within an enterprise and between its legal entities. IFM is designed to maximize staff efficiency and accounting accuracy while optimizing tax exposure, minimizing tax leakage and ensuring consistent tax and regulatory compliance. Technology has advanced to a point where this approach is feasible and cost-effective. For that reason, ISG Software Research...</span></p> </div> <a href="https://research.isg-one.com/analyst-perspectives/intercompany-financial-management-is-essential-for-multinationals"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://research.isg-one.com/analyst-perspectives/topic/office-of-finance">Office of Finance</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/erp-and-continuous-accounting">ERP and Continuous Accounting</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/digital-finance">digital finance</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/consolidate-and-close-management">Consolidate and Close Management</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/intercompany-financial-management">Intercompany Financial Management</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://research.isg-one.com/analyst-perspectives/mongodb-enhances-developer-data-platform">MongoDB Enhances Developer Data Platform</a></h2> <div class="author-meta"> Posted by <a href="https://research.isg-one.com/analyst-perspectives/author/matt-aslett"> Matt Aslett </a> on <span class="post-date">21 January 2025 </span> </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://research.isg-one.com/hs-fs/hubfs/MongoDB_Enhances_Developer_Data_Platform.png?width=200&name=MongoDB_Enhances_Developer_Data_Platform.png" width="200" !important: srcset="https://research.isg-one.com/hs-fs/hubfs/MongoDB_Enhances_Developer_Data_Platform.png?width=100&name=MongoDB_Enhances_Developer_Data_Platform.png 100w, https://research.isg-one.com/hs-fs/hubfs/MongoDB_Enhances_Developer_Data_Platform.png?width=200&name=MongoDB_Enhances_Developer_Data_Platform.png 200w, https://research.isg-one.com/hs-fs/hubfs/MongoDB_Enhances_Developer_Data_Platform.png?width=300&name=MongoDB_Enhances_Developer_Data_Platform.png 300w, https://research.isg-one.com/hs-fs/hubfs/MongoDB_Enhances_Developer_Data_Platform.png?width=400&name=MongoDB_Enhances_Developer_Data_Platform.png 400w, https://research.isg-one.com/hs-fs/hubfs/MongoDB_Enhances_Developer_Data_Platform.png?width=500&name=MongoDB_Enhances_Developer_Data_Platform.png 500w, https://research.isg-one.com/hs-fs/hubfs/MongoDB_Enhances_Developer_Data_Platform.png?width=600&name=MongoDB_Enhances_Developer_Data_Platform.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p>Despite all the interest in artificial intelligence (AI) and generative AI (GenAI), ISG’s <span><a href="https://www.isg-research.net/buyers_guide/analytics_and_data/2024-buyers-guides-for-data-platforms">Buyers Guide for Data Platforms</a></span> serves as a reminder of the ongoing importance of product experience functionality to address adaptability, manageability, reliability and usability. While new and emerging capabilities might catch the eye, features that address data platform security, performance and availability remain some of the most significant deal-breakers when enterprises are considering potential...</p> </div> <a href="https://research.isg-one.com/analyst-perspectives/mongodb-enhances-developer-data-platform"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://research.isg-one.com/analyst-perspectives/topic/ai">AI</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/ai-machine-learning">AI & Machine Learning</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/data-platforms">data platforms</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/generative-ai">Generative AI</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/analytics-and-data">Analytics and Data</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://research.isg-one.com/analyst-perspectives/preparing-for-q-day-safeguarding-enterprises-against-quantum-threats">Preparing for Q-Day: Safeguarding Enterprises Against Quantum Threats</a></h2> <div class="author-meta"> Posted by <a href="https://research.isg-one.com/analyst-perspectives/author/jeff-orr"> Jeff Orr </a> on <span class="post-date">16 January 2025 </span> </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://research.isg-one.com/hs-fs/hubfs/Preparing_for_Q-Day_Safeguarding_Enterprise_Against_Quantum_Threats.png?width=200&name=Preparing_for_Q-Day_Safeguarding_Enterprise_Against_Quantum_Threats.png" width="200" !important: srcset="https://research.isg-one.com/hs-fs/hubfs/Preparing_for_Q-Day_Safeguarding_Enterprise_Against_Quantum_Threats.png?width=100&name=Preparing_for_Q-Day_Safeguarding_Enterprise_Against_Quantum_Threats.png 100w, https://research.isg-one.com/hs-fs/hubfs/Preparing_for_Q-Day_Safeguarding_Enterprise_Against_Quantum_Threats.png?width=200&name=Preparing_for_Q-Day_Safeguarding_Enterprise_Against_Quantum_Threats.png 200w, https://research.isg-one.com/hs-fs/hubfs/Preparing_for_Q-Day_Safeguarding_Enterprise_Against_Quantum_Threats.png?width=300&name=Preparing_for_Q-Day_Safeguarding_Enterprise_Against_Quantum_Threats.png 300w, https://research.isg-one.com/hs-fs/hubfs/Preparing_for_Q-Day_Safeguarding_Enterprise_Against_Quantum_Threats.png?width=400&name=Preparing_for_Q-Day_Safeguarding_Enterprise_Against_Quantum_Threats.png 400w, https://research.isg-one.com/hs-fs/hubfs/Preparing_for_Q-Day_Safeguarding_Enterprise_Against_Quantum_Threats.png?width=500&name=Preparing_for_Q-Day_Safeguarding_Enterprise_Against_Quantum_Threats.png 500w, https://research.isg-one.com/hs-fs/hubfs/Preparing_for_Q-Day_Safeguarding_Enterprise_Against_Quantum_Threats.png?width=600&name=Preparing_for_Q-Day_Safeguarding_Enterprise_Against_Quantum_Threats.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p>As quantum computing advances at an unprecedented pace, the concept of Q-Day—a day when quantum computers can fundamentally undermine our current encryption methods—has entered discussions among cybersecurity professionals and business leaders alike. While there is no definitive date set for Q-Day, we are approaching a critical juncture where traditional cryptographic techniques may no longer suffice to protect sensitive data, digital communications and transactions. This impending shift not...</p> </div> <a href="https://research.isg-one.com/analyst-perspectives/preparing-for-q-day-safeguarding-enterprises-against-quantum-threats"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://research.isg-one.com/analyst-perspectives/topic/digital-technology">Digital Technology</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/cybersecurity">Cybersecurity</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/digital-security">Digital Security</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/q-day">Q-Day</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/quantum-computing">Quantum Computing</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/cryptography">Cryptography</a> <hr> </div> <script> // Check active classes var checkClass = function() { if ( $('.fil_item').hasClass('hide') ) { $('.fil_item').removeClass('hide'); } }; // Category filters $('.all').click( function() { checkClass(); 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100px) 100vw, 100px" srcset="https://research.isg-one.com/hs-fs/hubfs/_images/_team/Menninger.David%20Pic.Circle.png?width=50&name=Menninger.David%20Pic.Circle.png 50w, https://research.isg-one.com/hs-fs/hubfs/_images/_team/Menninger.David%20Pic.Circle.png?width=100&name=Menninger.David%20Pic.Circle.png 100w, https://research.isg-one.com/hs-fs/hubfs/_images/_team/Menninger.David%20Pic.Circle.png?width=150&name=Menninger.David%20Pic.Circle.png 150w, https://research.isg-one.com/hs-fs/hubfs/_images/_team/Menninger.David%20Pic.Circle.png?width=200&name=Menninger.David%20Pic.Circle.png 200w, https://research.isg-one.com/hs-fs/hubfs/_images/_team/Menninger.David%20Pic.Circle.png?width=250&name=Menninger.David%20Pic.Circle.png 250w, https://research.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://research.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://research.isg-one.com/hs-fs/hubfs/_images/_team/Orr.Jeff.Circle.png?width=50&name=Orr.Jeff.Circle.png 50w, https://research.isg-one.com/hs-fs/hubfs/_images/_team/Orr.Jeff.Circle.png?width=100&name=Orr.Jeff.Circle.png 100w, https://research.isg-one.com/hs-fs/hubfs/_images/_team/Orr.Jeff.Circle.png?width=150&name=Orr.Jeff.Circle.png 150w, https://research.isg-one.com/hs-fs/hubfs/_images/_team/Orr.Jeff.Circle.png?width=200&name=Orr.Jeff.Circle.png 200w, https://research.isg-one.com/hs-fs/hubfs/_images/_team/Orr.Jeff.Circle.png?width=250&name=Orr.Jeff.Circle.png 250w, https://research.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>   <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://research.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://research.isg-one.com/hs-fs/hubfs/_images/_team/Dawson.Keith%20Pic.Circle.png?width=50&name=Dawson.Keith%20Pic.Circle.png 50w, https://research.isg-one.com/hs-fs/hubfs/_images/_team/Dawson.Keith%20Pic.Circle.png?width=100&name=Dawson.Keith%20Pic.Circle.png 100w, https://research.isg-one.com/hs-fs/hubfs/_images/_team/Dawson.Keith%20Pic.Circle.png?width=150&name=Dawson.Keith%20Pic.Circle.png 150w, https://research.isg-one.com/hs-fs/hubfs/_images/_team/Dawson.Keith%20Pic.Circle.png?width=200&name=Dawson.Keith%20Pic.Circle.png 200w, https://research.isg-one.com/hs-fs/hubfs/_images/_team/Dawson.Keith%20Pic.Circle.png?width=250&name=Dawson.Keith%20Pic.Circle.png 250w, https://research.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://research.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://research.isg-one.com/hs-fs/hubfs/_images/_team/Smith.Mark%20Pic.Circle.png?width=50&name=Smith.Mark%20Pic.Circle.png 50w, https://research.isg-one.com/hs-fs/hubfs/_images/_team/Smith.Mark%20Pic.Circle.png?width=100&name=Smith.Mark%20Pic.Circle.png 100w, https://research.isg-one.com/hs-fs/hubfs/_images/_team/Smith.Mark%20Pic.Circle.png?width=150&name=Smith.Mark%20Pic.Circle.png 150w, https://research.isg-one.com/hs-fs/hubfs/_images/_team/Smith.Mark%20Pic.Circle.png?width=200&name=Smith.Mark%20Pic.Circle.png 200w, https://research.isg-one.com/hs-fs/hubfs/_images/_team/Smith.Mark%20Pic.Circle.png?width=250&name=Smith.Mark%20Pic.Circle.png 250w, https://research.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>   <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://research.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://research.isg-one.com/hs-fs/hubfs/_images/_team/Aslett.Matt%20Pic.Circle.png?width=50&name=Aslett.Matt%20Pic.Circle.png 50w, https://research.isg-one.com/hs-fs/hubfs/_images/_team/Aslett.Matt%20Pic.Circle.png?width=100&name=Aslett.Matt%20Pic.Circle.png 100w, https://research.isg-one.com/hs-fs/hubfs/_images/_team/Aslett.Matt%20Pic.Circle.png?width=150&name=Aslett.Matt%20Pic.Circle.png 150w, https://research.isg-one.com/hs-fs/hubfs/_images/_team/Aslett.Matt%20Pic.Circle.png?width=200&name=Aslett.Matt%20Pic.Circle.png 200w, https://research.isg-one.com/hs-fs/hubfs/_images/_team/Aslett.Matt%20Pic.Circle.png?width=250&name=Aslett.Matt%20Pic.Circle.png 250w, https://research.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://research.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://research.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://research.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://research.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://research.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://research.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://research.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>   <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://research.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://research.isg-one.com/hs-fs/hubfs/_images/_team/Kugel.Robert%20Pic.Circle.png?width=50&name=Kugel.Robert%20Pic.Circle.png 50w, https://research.isg-one.com/hs-fs/hubfs/_images/_team/Kugel.Robert%20Pic.Circle.png?width=100&name=Kugel.Robert%20Pic.Circle.png 100w, https://research.isg-one.com/hs-fs/hubfs/_images/_team/Kugel.Robert%20Pic.Circle.png?width=150&name=Kugel.Robert%20Pic.Circle.png 150w, https://research.isg-one.com/hs-fs/hubfs/_images/_team/Kugel.Robert%20Pic.Circle.png?width=200&name=Kugel.Robert%20Pic.Circle.png 200w, https://research.isg-one.com/hs-fs/hubfs/_images/_team/Kugel.Robert%20Pic.Circle.png?width=250&name=Kugel.Robert%20Pic.Circle.png 250w, https://research.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://research.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://research.isg-one.com/hs-fs/hubfs/_images/_team/Hurrell.Stephen%20Pic.Circle.png?width=50&name=Hurrell.Stephen%20Pic.Circle.png 50w, https://research.isg-one.com/hs-fs/hubfs/_images/_team/Hurrell.Stephen%20Pic.Circle.png?width=100&name=Hurrell.Stephen%20Pic.Circle.png 100w, https://research.isg-one.com/hs-fs/hubfs/_images/_team/Hurrell.Stephen%20Pic.Circle.png?width=150&name=Hurrell.Stephen%20Pic.Circle.png 150w, https://research.isg-one.com/hs-fs/hubfs/_images/_team/Hurrell.Stephen%20Pic.Circle.png?width=200&name=Hurrell.Stephen%20Pic.Circle.png 200w, https://research.isg-one.com/hs-fs/hubfs/_images/_team/Hurrell.Stephen%20Pic.Circle.png?width=250&name=Hurrell.Stephen%20Pic.Circle.png 250w, https://research.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>   <div class="row-fluid">  <div style="text-align: center;"> <a href="https://blog.isg-research.net"> <img class="analyst_img" style="width: 100px;" src="https://research.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" 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href="https://research.isg-one.com/analyst-perspectives/archive/2013/01">January 2013 <span class="filter-link-count" dir="ltr">(31)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/archive/2012/12">December 2012 <span class="filter-link-count" dir="ltr">(20)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/archive/2012/11">November 2012 <span class="filter-link-count" dir="ltr">(21)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/archive/2012/10">October 2012 <span class="filter-link-count" dir="ltr">(25)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/archive/2012/09">September 2012 <span class="filter-link-count" dir="ltr">(21)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/archive/2012/08">August 2012 <span 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href="https://research.isg-one.com/analyst-perspectives/topic/finance">finance <span class="filter-link-count" dir="ltr">(53)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/finance-analytics">Finance Analytics <span class="filter-link-count" dir="ltr">(7)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/finance-cloud">finance cloud <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/finance-financial-applications-financial-close">Finance Financial Applications Financial Close <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/finance-performance-management">Finance Performance Management <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li 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href="https://research.isg-one.com/analyst-perspectives/topic/financial-governance">Financial Governance <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/financial-management">Financial Management <span class="filter-link-count" dir="ltr">(7)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/financial-model">financial model <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/financial-performance">Financial Performance <span class="filter-link-count" dir="ltr">(561)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/financial-performance-2">financial performance <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a 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href="https://research.isg-one.com/analyst-perspectives/topic/flow">flow <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/fms">FMS <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/for-example-genesys">for example Genesys <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/forecast">Forecast <span class="filter-link-count" dir="ltr">(9)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/forecasting">forecasting <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/fpa">FP&A <span class="filter-link-count" 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href="https://research.isg-one.com/analyst-perspectives/topic/google">Google <span class="filter-link-count" dir="ltr">(10)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/governance">Governance <span class="filter-link-count" dir="ltr">(49)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/governance-risk">Governance & Risk <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/governance-risk-and-compliance">Governance Risk and Compliance <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/governance-risk-compliance-grc">Governance, Risk & Compliance (GRC) <span class="filter-link-count" dir="ltr">(143)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/grc">GRC <span class="filter-link-count" dir="ltr">(55)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/grc-governance-risk-compliance-risk-management-wat">GRC, governance, risk, compliance, risk management <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/green">Green <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/greenplum">Greenplum <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/gui">GUI <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/hadoop">Hadoop <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/hana">HANA <span class="filter-link-count" dir="ltr">(9)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/harvard-business-review">Harvard Business Review <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/hawq">HAWQ <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/hcm">HCM <span class="filter-link-count" dir="ltr">(57)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/hcm-analytics">HCM Analytics <span class="filter-link-count" 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providers may use cookies for storing information to help provide you with a better, faster and safer experience and for marketing purposes.</span></p> </div> </div> </div> </footer></div>  <script defer src="/hs/hsstatic/content-cwv-embed/static-1.1293/embed.js"></script> <script src="https://research.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-us'; </script> <script src="/hs/hsstatic/cos-i18n/static-1.53/bundles/project.js"></script> <script src="https://research.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": "How Software Can Help Usage Pricing Adoption", "mainEntityOfPage": "https://research.isg-one.com/analyst-perspectives/how-software-can-help-usage-pricing-adoption", "datePublished": "13/02/2025", "dateModified": "13/02/2025", "articleBody": "Based on the number of invitations I have received to speak on the topic, usage pricing (also referred to as consumption pricing) is hot. But as I have remarked elsewhere, it is not new. I recall a conversation with a customer over 30 years ago when I was involved with analytic software who asked, “Why can’t we just pay for the software we use?” And this of course makes sense: only pay for what you use. In fact, I assert that by 2027, over one-half of all enterprises will deploy a mixed revenue model that includes subscriptions and usage pricing in addition to one-time sales as enterprises make adjustments to remain competitive. Going back to the days of the on-premise, perpetual license business software application market, when it came to the split of risk between buyer and seller, it was really caveat emptor—the buyer carried most of the risk. Wind the clock to the turn of the century, and the advent of per-seat, by-subscription and termed contracts shifted the risk to be more equitably shared as the buyer could decline renewal if that particular purchase did not deliver the expected benefits. Wind on still further and there has been more discussion about usage pricing in the last 18 months than in the previous 18 years. I wrote a paper on subscription and usage pricing in September 2020, so what has changed since then? Generative Artificial Intelligence (GenAI) is what has changed. The initial wave of public AI providers chose to offer free, then paid-for subscription pricing as their monetization model. More recent business offerings have proposed usage models based on individual queries. Salesforce has announced per-transaction charges for their most common agent offerings. Though some providers plan on offering AI-driven features as part of their standard applications and without charging specifically for them, I believe a key driver for the majority of pronouncements on proposed usage-based pricing for GenAI offerings is cost. Training and running models is expensive. And directly linked to this expense, at least on the execution side, is how many AI-based queries or transactions are run. So, in essence, cost-plus for usage charges is a significant share of these pricing models. And while there are some good reasons for why usage pricing is an attractive option, it does present problems as to how it fits into the way businesses and consumers operate. A major attraction of subscription pricing was that it established a predictable revenue stream that continued until the customer cancelled or in the event of termed contracts, until the contract was not renewed. This predictability is attractive to both buyer and seller. For the buyer, the resulting recurring payments can be projected against fixed, budgeted amounts of expenditure. Likewise for sellers, although the initial amount received is smaller than the amount for a one-time sale, the recurring revenue over time can be projected with relative ease. Contrast this with the unpredictability of a usage-based pricing model, where neither the revenue for a seller or expense for a buyer are known in advance. This is not typically how businesses operate where expenditure is agreed against budgeted amounts of spend. This would be operationally problematic as well; would the AI based activity stop if that month’s budgeted amount of expense had been reached? In addition, processing usage data is potentially a problem to solve as well. As the price to be charged by a supplier is based on how much has been used, there is a need to acquire and ingest transactional data. So how can software help in resolving some of these issues? When it comes to addressing the impact of usage on the predictability of expense and revenue for both the buyer and the seller, there are several methods that are deployed. The simplest method is the concept of a digital wallet, that a buying enterprise would draw down against as used and then top up with funds as needed. It effectively operates as a Purchase Order (P.O.) defining how much money has been budgeted or allocated for a set of defined products or services. But this still does not help with predictability as it would be hard to know when top-ups will be needed. Forecasting of usage would certainly help both buyers and sellers and would address revenue recognition concerns for suppliers as they need a concept of an “average” usage pattern to establish a baseline. This would also be helpful information for use cases beyond expense and revenue considerations. Usage data is a record of how your product and service is being used in terms of what, when and by whom. Many subscription and management providers are now offering integrated data ingestion and transformation functionality, sometimes referred to as data mediation, a legacy of their origin in the telco industry. There are also specialist providers in this area, while other enterprises are looking to use general data mart or warehouse models. Managing usage data requires the collection, filtering, aggregating and normalizing potentially large volumes of data. Pricing these transactions often requires accumulating the data over a period for tiered pricing based on hitting volume targets. For performance reasons, incrementally processing the data may be preferrable. And as mentioned this data has value as source of usage data that can be used for monitoring adoption, identifying profiles for up- and cross-sell and in general information as to what is used, when and by whom. When considering usage pricing, whether as a buyer or as seller, know that it is not just about how the price is calculated. It also relates to how an enterprise puts in place technology and processes to make sure the promise of usage is fully realized within the confines of budget and expenditure controls and while providing visibility into other ways that the data can be used in terms of understand patterns and projected future consumption. Make sure you ask your provider, whether a point solution, a subscription management and billing system, or platform—or as part of an integrated front and back office—about how their offerings can help not with just pricing, but also as to whether they have both the technology and the processes to also help with enabling the model to work with existing business norms. Regards, Stephen Hurrell", "image": { "@type": "ImageObject", "url": "https://www.isg-research.net/hubfs/How_Software_Can_Help_Usage_Pricing_Adoption.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "Stephen Hurrell", "url": "https://research.isg-one.com/analyst-perspectives/author/stephen-hurrell", "sameAs": "https://www.linkedin.com/in/stephenhurrell" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "research.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": "Adapting to Higher Inflation Longer with Profitability Management", "mainEntityOfPage": "https://research.isg-one.com/analyst-perspectives/adapting-to-higher-inflation-longer-with-profitability-management", "datePublished": "05/02/2025", "dateModified": "05/02/2025", "articleBody": "In anticipation of conditions that suggested accelerating price increases, I wrote in 2021 about how technology could be useful in an inflationary period, anticipating the world we live in now. Responding effectively to changes in costs is always challenging, but even more so because of the choppy and chaotic nature of the current environment. Inflation may have abated from its highs throughout the rich world but is still an important factor in business decisions. Many enterprises have limited or no ability to raise prices and are forced to find ways to minimize the impact of rising costs. And while it’s true that some organizations have a degree of pricing power, behind this generalization is a more complex reality because this ability to raise prices varies depending on specific products, customers and channels. Companies can best address the challenges of inflation by adopting a technique that ISG Software Research calls “profitability management.” Profitability management goes beyond setting targets. Most companies manage to profitability objectives: CEOs are accountable for meeting company-wide financial objectives, including return on equity or return on assets, because investors use these in assessing the financial health and attractiveness of a business. In turn, they assign responsibility for achieving profitability to business unit owners across and down an organization. Sales quotas are put in place to achieve revenue goals are, and budget owners have cost and margin objectives. However, this is not the same as profitability management. Profitability management is a software-enabled discipline that’s especially useful in today’s rapidly changing market conditions. In an inflationary environment, sellers must be able to quickly adapt by having a full understanding of the true economic costs of products, the cost to serve customers and to operate channels. An enterprise must be able to make pricing decisions that enable it to successfully execute a go-to-market strategy. Because revenue recognition and stock-based compensation can distort measures of economic profitability, chief financial officers must have an appropriate set of lenses through which to view targets and results simultaneously from multiple perspectives. Profitability management is not simple, which is the reason it can be a source of sustainable competitive advantage. From an organizational perspective, profitability management poses challenges because it is a cross-functional effort that integrates finance and sales to achieve an optimal balance of revenue and margin objectives. This is a more effective performance management tool because it is a data- and analytics-based approach designed to consistently achieve higher sales and fatter margins. ISG Software Research asserts that by 2026, just 35% of enterprise financial planning and analysis teams will deliver actionable customer and product profitability analysis. Finance departments that do will be a strategic asset. A profitability management initiative involves parallel efforts by the Office of Finance and the sales organization. The first, handled by financial planning and analysis, requires setting up and running a profit analytics program. These analytics provide the rest of the organization with consistent and reliable detailed information about product, customer and channel profitability to support better decisions about pricing, product bundling, sales quotas, incentive compensation and production. The second supports a price and revenue management structure in the sales organization that achieves an optimal trade-off between revenue and margin. Organizations that use software to gain better insight into a buyer’s willingness to pay can set the highest possible price in a transaction with the greatest likelihood of getting the business. They also use software to identify potential cost reductions or find ways of disaggregating offerings (for instance, separating handling and shipping charges). Profitability management uses an analytics-driven business discipline designed to enable enterprises to achieve superior market share and profitability objectives. At the heart of the challenge is the ability to quantify the profitability of specific products, customers and channels and every permutation of these. That does not happen in most companies today. Our Office of Finance benchmark research found that only 34% manage customer profitability, and just 30% of companies manage product profitability. Profitability is determined by revenues minus costs, so it is axiomatic that organizations must fully understand the true economic costs of products, customers and channels. The enterprise must then use these insights to make decisions about pricing, marketing, quotas, territories and incentive compensation. Both efforts rely on software to deal with large data sets and often complex analytics to achieve the best results in dynamic market conditions. Economic costing is a better approach than financial accounting measures for understanding profitability. Statutory accounting is designed for financial—not operational—management. Accounting costs can be abstract and divorced from economic costs and cash flow. This includes generally accepted accounting principles for revenue recognition and equity compensation. Financial accounting suffers from basic analytical issues such as the sunk-cost fallacy. Economic costing is often a better way to determine the profitability of serving a specific customer or class of customers, as well as the cost to serve a specific channel or geography. Enterprises with a deeper, more economically accurate understanding of all costs are better able to control them. In being able to control them, costs can be managed in a way that is consistent with organizational strategy, resources and market position. Revenues are a function of units sold times the price per unit. Setting that price can be challenging. The most straightforward and longstanding approaches to price setting are a cost-plus calculation or just charging what competitors are charging. More recently, though, demand-based pricing has achieved a following because technology makes this approach practical. Demand-based pricing uses an estimate of the perceived value to the buyer of a good or service as the central element in setting prices. Demand-based pricing enables an enterprise to reliably achieve higher revenues and higher margins than other approaches because it is based on the buyer’s willingness to pay. Charging more when the buyer is willing to pay produces higher profits without sacrificing revenue and market share. At the heart of demand-based pricing is a price and revenue-optimization technique that uses market segmentation to achieve strategic objectives such as increased profitability or higher market share. Price and revenue optimization has demonstrated repeatable results. It first came into wide use in the travel and hospitality industry in the 1980s. It enabled hotels and airlines to maximize returns from less flexible travelers, such as people on business trips, while minimizing the unsold inventory by selling incremental seats on flights or hotel rooms at discounted prices to vacationers. Ideally, cross-functional initiatives such as profitability management are led by the CEO, whose involvement and commitment are necessary to mediate with authority the inevitable differences between conflicting objectives and constraints. If the initiative does not spontaneously come from the CEO, I suggest the CFO should be a champion. This individual should make profit analytics a top three priority and advocate the use of demand-based, intelligent pricing in the leadership team. I recommend that every finance organization—and specifically the FP&A group—routinely measure customer, product and channel profitability using an economic costing method to do the analysis. This is most effective when done as part of a coordinated cross-functional effort. The sales organization, the sales operations team and/or a pricing group manage and review the pricing of products, services and contracts. Marketing creates campaigns and offers, which need to be tied to revenue and profitability objectives. And operations—whether manufacturing, services or fulfillment—must be involved to understand the nature of costs, constraints and other details. It’s common to hear it said that business has never been more challenging. It’s a marketing line designed to grab your attention. In reality, business challenges never fundamentally change. Enterprises must offer customers appealing products or services, attract and retain the best talent, market and sell effectively, maintain financial control and so on. What does change are the tools—especially technology—that businesses can use to enhance competitiveness and improve performance. Technology—and the ability of some clever enough to use it to gain an advantage—is the real challenge. In inflationary times, profitability management is just such a tool that enables enterprises to outperform rivals. Regards. Robert Kugel", "image": { "@type": "ImageObject", "url": "https://www.isg-research.net/hubfs/Adapting_to_Higher_Inflation_Longer_with_Profitability_Management.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "Robert Kugel", "url": "https://research.isg-one.com/analyst-perspectives/author/robert-kugel", "sameAs": "https://www.linkedin.com/in/robertkugel" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "research.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": "Redpanda Lowers the Complexity and Cost of Streaming and Event Data", "mainEntityOfPage": "https://research.isg-one.com/analyst-perspectives/redpanda-lowers-the-complexity-and-cost-of-streaming-and-event-data", "datePublished": "04/02/2025", "dateModified": "04/02/2025", "articleBody": "I recently wrote about the need for enterprises to harness events to process and act upon data at the speed of business. The core technologies that enable enterprises to process and analyze data in real time have been in existence for many years and are widely adopted. However, streaming and events technologies are also commonly seen as a niche requirement, separate from an enterprise’s primary focus on batch processing of data at rest. One of the reasons for this is an entrenched reliance on batch data processing products and workflows. Another is the high-level expertise that has been required to implement and maintain streaming and event processing technologies. In recent years, many software providers, including Redpanda, have set out to lower the barriers to working with streaming and event data to encourage wider adoption. Redpanda was founded in 2019 by CEO Alexander Gallego with the goal of making real-time data accessible to all developers, rather than just those with high levels of expertise related to streaming and event data processing systems and architecture. Based on their experience with existing streaming and event data processing products, Gallego and his team knew that the cost and complexity of implementing streaming and event technologies was high, presenting a barrier to adoption. They also knew that a higher number of use cases and greater data volumes resulted in increased cost and complexity, which put the brakes on real-time data processing playing more than niche role in many enterprise data strategies. ISG’s Analytics and Data Benchmark Research highlights the relatively low level of adoption of streaming data, with less than one-third (30%) of participants using streaming data in analytics. Redpanda’s goal was to create a streaming data platform that would be compatible with the Apache Kafka distributed messaging and event streaming platform without the operational complexity that Kafka was then known for. The product was initially introduced in 2021 at the same time as Redpanda’s announcement of $15.5 million in funding from Lightspeed Venture Partners and GV. Redpanda subsequently announced a further $50 million of Series B funding in 2022, followed by a $100 million Series C round in 2023. In addition to the development of the company’s streaming data platform and managed cloud services, Redpanda has also invested some of its funding in acquisitions, including the web user interface expertise of CloudHut in 2022 and streaming data integration connector developer Benthos in May 2024. I assert that by 2026, two-thirds of enterprises will require streaming and event data processes with low latency of seconds or sub-seconds to satisfy operational requirements. A key first step Gallego took to diminish the cost and complexity of streaming data technology was to reduce the resources required to deliver high-performance data processing. To achieve the desired performance gains, the Redpanda streaming data platform was written in C++, with each node in a cluster designed to be a self-sufficient single binary that natively implements the Raft consensus protocol for data management and control across the cluster. These design choices resulted in a reduced footprint compared to existing streaming and event systems and avoided dependency on Java virtual machines and external distributed coordination systems such as Apache ZooKeeper. Additionally, Redpanda implemented a thread-per-core programming model designed to take full advantage of available hardware resources (including processor, memory and disk) and avoid over-provisioning hardware to meet scalability requirements. Redpanda was also designed to take advantage of cloud object storage by default, with support for tiered storage combining cloud and local storage resources. The product also includes the Redpanda Console user interface, which is built on the capabilities acquired with CloudHut and the delivery of more than 280 pre-built integration connectors via Redpanda Connect (built on the Benthos acquisition). Redpanda is delivered as self-hosted software, as well as via a range of cloud managed services. Redpanda Cloud is available as dedicated clusters hosted and managed by Redpanda on single-tenant AWS, Google Cloud or Microsoft Azure cloud infrastructure, as well as serverless clusters hosted and managed by Redpanda on multi-tenant shared cloud infrastructure on AWS. Redpanda also supports bring-your-own cloud (BYOC) clusters, in which the data plane is hosted on the customer’s cloud infrastructure provider but provisioned, monitored and maintained by Redpanda via its control plane in Redpanda Cloud. The BYOC offering is designed to support low-latency data processing needs as well as compliance with data sovereignty, security and privacy regulations. While many cloud providers have policies that pertain to protecting data privacy, emerging data sovereignty requirements place additional burdens on enterprises to not only have reassurances about how the data is stored and processed, but control over the infrastructure used to store and process the data. Separating the control and data planes also enables an enterprise to assert more control over the performance of the data processing software and enforce multiple layers of security without ceding permissions to the software provider. BYOC can also enable enterprises to avoid cloud-egress costs that would otherwise be involved in moving data to the software provider’s environment. Sovereignty is also a concept that is associated with Redpanda’s nascent efforts to address requirements for artificial intelligence (AI), with the company looking to combine AI models with data streaming workloads in virtual private cloud environments to enable private inferencing, as well as AI lineage tracing and integration with role-based access control. Redpanda’s Sovereign AI approach is in early preview, and we anticipate further details and enhancements being rolled out during 2025. We also look forward to more details about Redpanda One, the company’s forthcoming multimodal streaming data engine, which is being designed to enable users to define data storage for each individual topic based on its unique requirements for availability, consistency, latency, safety and cost-effectiveness. In the interim, I recommend that any enterprise evaluating options for streaming data and event processing include Redpanda in its evaluation. Regards, Matt Aslett", "image": { "@type": "ImageObject", "url": "https://www.isg-research.net/hubfs/Redpanda_Lowers_the_Complexity_and_Cost_of_Streaming_and_Event_Data.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "Matt Aslett", "url": "https://research.isg-one.com/analyst-perspectives/author/matt-aslett", "sameAs": "https://www.linkedin.com/in/matt-aslett/" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "research.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": "Databricks Scores Massive Funding Round, Continues to Expand Its Offerings", "mainEntityOfPage": "https://research.isg-one.com/analyst-perspectives/databricks-scores-massive-funding-round-continues-to-expand-its-offerings", "datePublished": "29/01/2025", "dateModified": "29/01/2025", "articleBody": "Databricks recently announced its Series J funding round, successfully raising $10 billion at a valuation of $62 billion. Led by Thrive Capital alongside high-profile investors such as Andreessen Horowitz and Insight Partners, the company intends to invest this capital towards new artificial intelligence (AI) products, acquisitions and significant expansion of its international operations. In the announcement, Databricks reported that it expects to achieve an annual revenue run rate of $3 billion in the quarter ending January 31, 2025. Founded in 2013, Databricks initially gained prominence for its cloud-based Apache Spark services, aimed at enhancing big data processing and creating an alternative to MapReduce. Spark was initially started by Databricks co-founder Matei Zaharia at UC Berkeley's AMPLab in 2009. In 2013, the project was donated to the Apache Software Foundation. The Spark framework includes Spark SQL, DataFrames, Spark Streaming, MLlib and GraphX. By providing robust analytics over massive data sets, Spark became the de facto way to process big data. Although Databricks’ commercial operations focus exclusively on providing managed cloud services, it has also supported the broader open-source Spark community. Over time, the worlds of data lakes and data warehouses collided. Databricks introduced the concept of a data lakehouse, adding Databricks SQL as well as open table formats. My colleague Matt Aslett has written about the importance of open table formats and Databricks’ support for both Delta Lake and, more recently, Apache Iceberg, including its acquisition of Tabular. As the years progressed, Databricks evolved beyond its initial offerings. The company’s Data Intelligence Platform is now positioned as providing a lakehouse-based environment for data engineering, data warehousing, stream data processing, data governance, data sharing, business intelligence (BI), data science and AI. While MLlib provided machine learning (ML) capabilities, the company doubled down on its investment in AI with the acquisition of Mosaic ML. Databricks also set its sights on generative AI (GenAI) and large language models (LLMs) when it introduced DBRX, an open-source, general purpose LLM, as well as supporting enhancements, driving the future of intelligent data applications. Databricks’ Unity Catalog provides a single layer for governing structured and unstructured data, as well as ML models, notebooks, dashboards and files. As a result of both its own research and development efforts coupled with its acquisition strategy, Databricks is able to offer state-of-the-art tools for enterprises aiming to build, train and deploy AI/ML and LLMs using its Lakehouse Platform. As a result, Databricks has been rated Exemplary in our Analytic Data Platforms, AI Platforms and GenAI Platforms Buyers Guides. The initiatives underline a clear strategy to support enterprises in the various stages and types of AI investments, combining multiple models and components to create high-quality AI applications supported by and integrated with the necessary underlying data infrastructure. This trend is expected to be instrumental as organizations focus on transitioning from pilot projects to scalable production systems. Our research shows that only 15% of enterprises are fully in production with their GenAI efforts. One of the company's hallmark events, the Data + AI Summit, has established itself as a nexus for industry stakeholders, offering insight into cutting-edge applications of data and AI technologies. Recently, the Summit focused on GenAI integrations, including functionalities that allow users to streamline the development of AI-based applications within the Lakehouse environment. This year, the emphasis was on announcing new capabilities like Mosaic AI Agent Framework and enhancements to its Lakehouse Platform for AI and data. The launch of Databricks AI/BI also represents the company’s entry into the self-service analytics market with two new AI-powered capabilities: low-code dashboarding and a conversational interface. Databricks also unveiled LakeFlow to bring together all types of data engineering processes into a unified platform. Building on Databricks’ acquisition of Arcion as well as its existing Delta Live Tables and Databricks Workflows functionality, LakeFlow automates ingestion, transformation and monitoring, simplifying the overall experience for data teams. Such innovations are vital as businesses continue to grapple with the complexities of managing and optimizing data pipelines across various environments. Databricks was also rated Exemplary in our Data Intelligence, Data Integration and Data Governance Buyers Guides. Shortly after the event, Databricks announced general availability of Databricks Assistant. Databricks Assistant can be used to generate code, provide help and troubleshoot errors. It can also be used to create visualizations and dashboards, although this feature is still in preview mode. In conclusion, Databricks has made significant strides from its initial focus on Spark processing for big data, becoming an industry leader that unifies data and AI under a single platform. The current push for GenAI and the substantial funding acquired provide the resources for Databricks’ continued investments. As enterprises consider their data and AI strategies, I recommend they include Databricks in their evaluations. Regards, David Menninger", "image": { "@type": "ImageObject", "url": "https://www.isg-research.net/hubfs/Databricks_Scores_Massive_Funding_Round_Continues_to_Expand_Its_Offerings.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "David Menninger", "url": "https://research.isg-one.com/analyst-perspectives/author/david-menninger", "sameAs": "http://www.linkedin.com/in/davidmenninger" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "research.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": "Data Catalogs Serve Multiple Roles and Use Cases", "mainEntityOfPage": "https://research.isg-one.com/analyst-perspectives/data-catalogs-serve-multiple-roles-and-use-cases", "datePublished": "29/01/2025", "dateModified": "29/01/2025", "articleBody": "Metadata management has played a role in data governance and analytics for many years. It wasn’t until the emergence of the data catalog as a product category just over a decade ago that enterprises had a platform for metadata-driven data management that could span multiple departments and use cases across an entire enterprise. ISG defines a data catalog as an inventory of data assets that surfaces metadata from data platforms, tools and applications that can be used to understand how and when data is produced and consumed. In theory, a single data catalog can provide an inventory of data that spans multiple departments, use cases and initiatives, and ISG’s Data Governance Benchmark Research shows a correlation between a higher proportion of data catalog users and improved confidence in the organization’s ability to govern and manage data across the business. Data catalog functionality has been incorporated into numerous data management, data governance and data platform products to the extent that enterprises have multiple catalogs of data across numerous domains and repositories, perhaps with a “catalog of catalogs” providing higher-level insight. There is a danger that enterprises with multiple catalogs are repeating past mistakes of creating data silos. However, it is important to recognize that the term data catalog applies to various products that serve discreet use cases and user roles. As such, it may be desirable for an enterprise to have multiple connected catalogs, or at least numerous interfaces to a catalog, that serve the requirements of different users. In our view, there are four main types of data catalogs, including: Technical data catalogs, representing the original and fundamental data catalog functionality of a metadata repository that scans the enterprise’s data estate and extracts technical metadata to provide an inventory of the data’s location, structure and schema. This inventory can be used by data administrators and data engineers to discover, manage and optimize the data while also providing insights on data usage, data lineage and data quality, as well as security and access control. While there are standalone technical data catalog products, this technology also forms the base layer of functionality used by other types of data catalogs and represents the core capabilities embedded in other data management products. Business data catalogs, providing functionality that relies upon and builds on the underlying technical metadata and access control capabilities with an additional layer that provides business metadata related to the context, meaning and relevance of the data to business domains and applications. This layer of capability enables self-service discovery and access to data by business users and data analysts using natural language search. In addition to technical metadata, business data catalogs also provide business metadata, such as glossaries, descriptions and classification, as well as information related to data lineage and data quality. Data governance catalogs, providing an interface for data stewards, data quality and data governance professionals focused on ensuring the enterprise fulfills its data governance and regulatory requirements. In addition to the data usage, data lineage, data quality, data security and access control capabilities of the underlying technical catalog, these users require additional functionality to define and manage data usage policies, view and manage data profiles, determine and administer data quality rules and define and administer data models and master data definitions. While there are standalone data governance catalog products, this functionality is also delivered by providers of technical and business data catalogs with dedicated interfaces for data stewards, data quality and data governance professionals. Data intelligence catalogs, representing the evolution of business data catalogs and combined technical metadata, business metadata and data governance capabilities with knowledge graph functionality to deliver a holistic, business-level view of data production and consumption. Data intelligence catalogs help data administrators understand the use of data in reports and dashboards and provide chief analytics and chief data officers with key metrics on data production and consumption, including the value generated by data initiatives. Data intelligence catalogs also increasingly provide functionality to enable the development, sharing and management of data products. Additionally, we sometimes see the term data catalog used in relation to a variety of other products, including analytics catalogs used to provide a portal for business analysts to discover and access business intelligence reports and dashboards; metrics stores used to provide a consistent layer of metrics for consumption by business intelligence reports and dashboards; feature stores used to store curated features for developing machine learning applications; and model catalogs or model gardens which store curated models for the development of Generative AI applications. These capabilities could be addressed by standalone products but also incorporated into data intelligence catalogs. ISG’s 2024 Buyers Guide for Data Intelligence illustrates the relative maturity of the functionality available across the various data catalog products. Almost all (96%) software providers evaluated were graded at A- or above for data source connectivity, creating and maintaining profiles of data assets and creating a search-based inventory of data assets. While this core data catalog functionality has become pervasive, a second category of capabilities has also become widely adopted by data catalog providers. For example, more than three-quarters (78%) of software providers evaluated were graded at A- or above for both viewing and managing technical data lineage and creating, modifying and deleting a business glossary, while 77% achieved A- or above for automated generation of metadata from data sources and 74% for self-service data discovery by data consumers. I assert that through 2027, enterprises will increase strategic focus on data catalogs as the intersection of data production and consumption, enabling the self-service creation and sharing of data products based on trusted and governed data sources. As a result, capabilities that enable the creation of data products and a more holistic view of data production and consumption will be in high demand. ISG’s Buyers Guide for Data Intelligence also highlighted that these provide significant opportunities for differentiation as data catalogs evolve towards providing data intelligence. For example, only 57% of software providers evaluated were graded at A- or above for applying product thinking to the creation of data products, 52% for metrics and scorecards related to data usage and 44% for the incorporation of knowledge graph capabilities to map the relationships between data and entities. Additionally, the use of artificial intelligence for data cataloging continues to provide a significant opportunity for differentiation. Less than one-third (30%) of the software providers evaluated ISG’s Buyers Guide for Data Intelligence graded at A- or above for the use of AI for data asset descriptions. Only 26% did so for the use of AI for metadata generation and only 17% for the use of AI for data usage recommendations. I recommend that all enterprises evaluate the current use of data catalogs based on the various user requirements and assess potential providers with a view to ensuring governed data sharing across the organization and a holistic, business-level view of data production and consumption. Regards, Matt Aslett", "image": { "@type": "ImageObject", "url": "https://www.isg-research.net/hubfs/Data_Catalogs_Serve_Multiple_Roles_and_Use_Cases.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "Matt Aslett", "url": "https://research.isg-one.com/analyst-perspectives/author/matt-aslett", "sameAs": "https://www.linkedin.com/in/matt-aslett/" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "research.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": "ESG Management Software is Essential for Efficient Compliance", "mainEntityOfPage": "https://research.isg-one.com/analyst-perspectives/esg-management-software-is-essential-for-efficient-compliance", "datePublished": "28/01/2025", "dateModified": "28/01/2025", "articleBody": "Enterprises have been investing in software to manage their environmental, social and governance (ESG) objectives and compliance requirements. The need to account for these considerations in parallel with financial accounting began growing early in the century and accelerated as governments and regulatory authorities began to require companies to measure and document activities and outcomes. Meanwhile, software providers have been offering products to address the needs of those subject to these requirements. To assist, ISG Software Research will inaugurate an ESG Software Buyers Guide later this year. I’m focusing here on the environmental aspects of ESG compliance because they are the most challenging, especially in the data collection and analysis. Most of the data for the social elements are brought together and can be reported in existing systems, especially human capital management. Corporate governance software has been a fixture since the passage of the Sarbanes-Oxley Act in 2002, which mandated more reliable corporate disclosures on governance issues. General purpose legal and regulatory compliance software has been around for decades and is capable of dealing with paperwork necessary for social and governance requirements. Software designed specifically to support compliance with environmental reporting laws and regulations includes one or more of the following five categories: Sustainability Management: Applications used to manage sustainability practices to achieve compliance and objectives, such as reducing carbon footprint or improving resource efficiency. Compliance Management: Tools that assist in achieving efficient compliance, manage related processes, track status and generate mandated reports. Risk Management: Software to help identify, assess and mitigate ESG-related risks. Benchmarking: These tools allow organizations to compare their ESG performance against industry standards or competitors. They provide insights into areas for improvement and best practices. Analytics: Software that helps enterprises to collect, analyze and report data related to their ESG activities. Compliance with environmental reporting laws is challenging for at least four reasons. At the top of the list is data collection, because gathering accurate data on various environmental metrics, such as carbon emissions, water usage and waste management, can be quite difficult. In the case of emissions, simply capturing and gathering accurate and verifiable data that are owned or controlled by an enterprise (referred to as Scope 1 metrics) requires that the sources of those emissions are properly instrumented to measure them. This is not a given. Scope 2 metrics, that refer to indirect sources of emissions (such as electricity, supply chain transportation and business travel), are more difficult to gather. For example, while airlines provide GHG emission data to customers, electrical utilities do not always make this information available, often because they themselves are not set up to track emissions data. The power supplied to a customer may be sourced from multiple suppliers that can vary hour to hour, each with their own energy source or sources. Scope 3 emissions from the supply chain are harder still because they are that much more removed from objective calculation. Moreover, to avoid double counting, a novel form of emissions accounting is likely necessary. In addition, because different industries have their own flavors of metrics and standards, there is an ongoing build-out of this aspect of the capabilities of each software provider. Another challenge is the regulatory landscape, which has been constantly changing, with new laws and standards being introduced regularly. Keeping up with these changes and ensuring compliance can be difficult and costly. A third, related challenge for enterprises operating across multiple regions and countries is dealing with different environmental regulations and reporting frameworks across jurisdictions, which adds another layer of difficulty. And beyond the technical requirements, there are serious organizational ones. Enterprise-level environmental objectives are not by themselves actionable, so responsibility for meeting them must devolve to individual business units, executives and managers. Moreover, there can be trade-offs between environmental and financial goals, and yet few, if any, ESG applications can optimize these trade-offs where they exist. ISG Software Research asserts that by 2027, almost all providers of ESG management software will incorporate cost, as well as environmental impact data, to enable enterprises to optimize trade-offs in achieving sustainability goals. Thousands of midsize and larger enterprises need to assemble and report accurate and verifiable environmental data to be compliant with relevant laws and regulations. Those that operate in multiple countries or legal jurisdictions have an even more complex compliance task. I recommend that enterprises that are affected by ESG mandates use dedicated software to reduce the time spent by staff on this administrative task, as well as ensure full compliance and verifiability with the least amount of effort. Regards, Robert Kugel", "image": { "@type": "ImageObject", "url": "https://www.isg-research.net/hubfs/ESG_Management_Software_is_Essential_for_Efficient_Compliance.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "Robert Kugel", "url": "https://research.isg-one.com/analyst-perspectives/author/robert-kugel", "sameAs": "https://www.linkedin.com/in/robertkugel" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "research.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 Impact of the Cloud and AI on Evolving Data Platform Requirements", "mainEntityOfPage": "https://research.isg-one.com/analyst-perspectives/the-impact-of-the-cloud-and-ai-on-evolving-data-platform-requirements", "datePublished": "23/01/2025", "dateModified": "23/01/2025", "articleBody": "The degree to which data platforms are critical to efficient business operations cannot be overstated. Without data platforms, enterprises would be reliant on a combination of paper records, time-consuming manual processes and huge libraries of physical files to record, process and store business information. The extent to which that is unthinkable highlights the level at which today’s enterprises and society as a whole rely on data platforms. The core persistence, management, processing and query requirements of data platforms have been well established since the emergence of the database market in the early 1970s. The data platforms market continues to evolve to keep pace with emerging capabilities and requirements. Two of the most significant trends driving the data platforms sector in recent years are cloud computing and artificial intelligence. ISG Research defines data platforms as software products that provide an environment for organizing and managing the storage, processing, analysis and presentation of data across an enterprise. Data platforms support and enable operational applications used to run the business, as well as analytic applications used to evaluate the business, including AI, machine learning and generative AI. The data platforms software market encompasses relational and non-relational operational databases, as well as data warehouses, data marts, data lakes, data lakehouses and other analytic data platform products. Many other categories of analytics and data products rely on the processing engine capabilities of data platforms, including data operations platforms and tools and data intelligence platforms and tools. Although the primary users of data platforms are workers in technical roles, including database administrators, application developers, data engineers and data architects, data platforms must support a range of workloads aimed at users with differentiated responsibilities and functional requirements, including business users and managers as well as data analysts and data scientists. At the heart of any data platform is the storage and management of a collection of data. This is typically provided by a database management system—more commonly referred to simply as a database—that provides the data persistence, data management, data processing and data query functionality that enables access to and interaction with stored data. The data platforms market has traditionally been dominated by the relational data model and relational database management systems. However, one approach does not suit all use cases, and enterprises use a variety of data platforms to fulfill the spectrum of requirements for myriad applications. Non-relational data models that pre-date relational, such as the hierarchical model, remain in use today. However, the use of non-relational data models has become widespread in recent years through the adoption of NoSQL databases and the integration of key-value, document and graph model capabilities into relational databases. Data platforms were traditionally deployed on-premises, but in recent years, many enterprises have adopted cloud-based data platforms with a view to improving operational efficiencies by reducing the need for upfront investment in physical infrastructure as well as the ability to scale cloud services up and down to match fluctuating requirements. ISG’s 2024 Market Lens Cloud Study illustrates the extent to which the database market is now dominated by the cloud, with 58% of participants using cloud environments for more than half of database and data platform workloads. Adoption of cloud computing environments has also led to the widespread use of object stores as a data persistence layer, particularly for data lakehouse environments, with query engines such as Apache Spark, Apache Presto and Trino adding the data management, data processing and data query functionality required of a data platform. Additionally, support for open table formats is now a critical feature for providers of analytic data platforms to enable the persistence and analysis of structured and unstructured data in object storage. There have always been general-purpose databases used for both analytic and operational workloads, but data processing architectures have traditionally involved the extraction, transformation and loading of data from the operational data platform into a separate external analytic data platform. This enables operational and analytic workloads to run concurrently without adversely impacting each other, protecting the performance of both. Over time, dedicated analytic data platforms have also evolved differentiated architectural approaches designed to improve query performance, while specialist analytic data platform providers have accelerated the development of in-database AI/ML functionality to address the growing demand for AI and avoid the cost and complexity of moving data to an external environment. The increased focus on AI-driven intelligent applications is significantly impacting how software providers approach the data platforms market. Analytic data platform workloads are typically targeted at data and business analysts and include decision support, business intelligence, data science and AI and ML. Operational data platform workloads typically target business users and decision-makers. Examples include finance, operations and supply chain, sales, human capital management, customer experience and marketing applications. The need for real-time applications driven by online predictions and recommendations has increased the requirements for operational data platforms to support real-time AI/ML inferencing. Consumers increasingly engage with data-driven services differentiated by personalization and contextually relevant recommendations. Additionally, worker-facing applications are following suit, targeting users based on their roles and responsibilities. These intelligent applications, while operational in nature, rely on real-time analytic processing to deliver functionality, including contextually relevant recommendations, predictions and forecasting driven by ML and GenAI. I assert that through 2027, the development of intelligent applications providing personalized experiences driven by GenAI will increase demand for data platforms capable of supporting hybrid operational and analytic processing. The popularization of GenAI has also had a significant impact on the requirements for data platforms to support storing and processing vector embeddings. These are multi-dimensional mathematical representations of features or attributes of raw data that are used to support GenAI-based natural language processing and recommendation systems. Vector search can also improve accuracy and trust with GenAI via retrieval-augmented generation, which is the process of retrieving vector embeddings representing factually accurate and up-to-date information from a database and combining it with text automatically generated by a large language model. Cloud and AI are two of the most significant factors driving the evolution of data platform capabilities, but I recommend that all enterprises considering options for new data platform deployments also evaluate other product experience functionality addressing adaptability, manageability, reliability and usability as well as the customer experience provided by the software provider. Those were the criteria ISG used to evaluate data platforms software providers in the 2024 Data Platforms Buyers Guides and will once again provide the framework for our assessment of data platform providers in 2025. Regards, Matt Aslett", "image": { "@type": "ImageObject", "url": "https://www.isg-research.net/hubfs/The_Impact_of_the_Cloud_and_AI_on_Evolving_Data_Platform_Requirements.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "ISG Software Research", "url": "https://research.isg-one.com/analyst-perspectives/author/isg-software-research", "sameAs": "https://www.linkedin.com/company/ventana-research" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "research.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": "Intercompany Financial Management is Essential for Multinationals", "mainEntityOfPage": "https://research.isg-one.com/analyst-perspectives/intercompany-financial-management-is-essential-for-multinationals", "datePublished": "22/01/2025", "dateModified": "22/01/2025", "articleBody": "ISG Software Research coined the term intercompany financial management to define a discipline for structuring and handling transactions within an enterprise and between its legal entities. IFM is designed to maximize staff efficiency and accounting accuracy while optimizing tax exposure, minimizing tax leakage and ensuring consistent tax and regulatory compliance. Technology has advanced to a point where this approach is feasible and cost-effective. For that reason, ISG Software Research asserts that by 2027, one-half of organizations with 10,000 or more workers will implement IFM for its tax, risk-management and accelerated financial close benefits. Besides previous technology constraints, a major reason IFM is such an obscure topic is that its impact is lost in the weeds of detail and, therefore, invisible to senior executives. Those working at a departmental level rarely see issues that occur between entities. Moreover, each instance of an IFM issue is typically relatively small, but in larger multinational enterprises, the money involved adds up to a meaningful annual cost. Enterprises are made up of multiple entities that frequently buy and sell goods and services from each other. For example, a manufacturer might source parts from different divisions located in various states or countries, export the finished product to a country where it has a distribution center, and then install and service the product in yet another country with its local business unit. There may be recurring payments for the use of intellectual property or parts provided under warranty. These transactions give rise to a mass of paperwork as one corporate entity invoices and the other pays. The transactions also can create tax liabilities arising from those sales and purchases. When tallying up all that bookkeeping to produce the enterprise’s financial statements, all those intercompany transactions are supposed to cancel each other out. Usually, they do, but because each side of the transaction is recorded separately, it is also common for there to be errors, slight discrepancies in volume and price, currency translation differences and other adjustments that must be identified and corrected. And where these transactions cross national borders, multiple complications arise. The transactions may involve different currencies and be subject to disparate import and export regulations as well as tariffs and taxes. When IFM is not properly managed, value-added tax leakage can occur in a cross-border transaction when a corporate entity fails to claim a refund that it is owed. Or a company may be deemed to have a “permanent establishment” in a country and, therefore, have a taxable presence if it is not able to document that the volume of its work in that country is below the legal threshold. These issues can be addressed by centralizing the intercompany accounting function that provides an automated intercompany billing and invoice-management service designed to improve administrative productivity and lower costs while reducing global tax and compliance risks. IFM addresses problems common to enterprises operating in multiple tax jurisdictions with numerous ERP systems. Our Next-Generation ERP Benchmark Research finds that two-thirds of organizations with more than 1,000 workers have more than one ERP system, and 27% have four or more. All those internal buy-and-sell transactions create workloads in each local accounting department, including dealing with errors, reconciling differences in the details of invoices and payments, and filing regulatory and tax documents. These workload issues are compounded when there are multiple countries involved, especially if there are intermediate stops from source to destination, for logistical, regulatory or tax considerations. Larger enterprises often establish a shared service organization to manage intercompany transactions, including presentment and payment of invoices. This approach can be more efficient by centralizing the accounting for the transaction, which often goes beyond simple economies of scale. However, unless the process is well managed, an SSO can cause delays in payments and official filings (which can result in fees and penalty assessments) as well as create unnecessary tax or regulatory issues. In theory, the selling and buying events are simply mirror images, but that is not always the case. The transactions might be booked by their respective accounting departments on different dates, at different prices, with different terms and conditions. The items on the sales and purchase orders also may not agree exactly, or there might be errors in the harmonized system export codes. Enterprises can avoid these potential issues through effective process and data management that scales to volume and geographic requirements. Large enterprises need a system that captures the required attributes of intercompany agreements and streamlines intercompany dispute resolution, as well as handling taxes with an integrated tax engine to maximize tax deductibility by reducing stranded costs. Controlled and consistent tax calculations diminish the possibility of errors and make tax defense faster and easier. Moreover, all multinational activities require filings, and increasingly, national governments are demanding tax-compliant invoices be submitted to tax authorities to limit VAT fraud and errors that reduce revenue. For enterprises domiciled in the U.S., there are complications caused by the base erosion and anti-abuse tax that apply to otherwise deductible payments for interest, royalties and some services used to shift profits to lower-tax jurisdictions. In most areas of business, being strategic means not getting lost in the details. Being strategic in accounting is all about flawlessly managing the details, especially in areas of hair-curling complexity. Being strategic in accounting processes means managing data continuously without human intervention from the start of a process to its completion, one of the pillars of what ISG Software Research calls continuous accounting. Enterprises with large volumes of intercompany transactions should assess the handling of IFM, consider ways to deal with issues and investigate how internal processes, data management and systems can be altered to address problems. It is unlikely that, given the highly technical nature of IFM, organizations will find that internal IT teams can create and maintain effective systems in a cost-effective manner. Therefore, enterprises should consider third-party offerings to manage the workloads and complexity of processes required to match and reconcile these transactions, report on the transactions internally and externally and file documents for regulatory and tax purposes. Regards, Robert Kugel", "image": { "@type": "ImageObject", "url": "https://www.isg-research.net/hubfs/Intercompany_Financial_Management_is_Essential_for_Multinationals.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "Robert Kugel", "url": "https://research.isg-one.com/analyst-perspectives/author/robert-kugel", "sameAs": "https://www.linkedin.com/in/robertkugel" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "research.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": "MongoDB Enhances Developer Data Platform", "mainEntityOfPage": "https://research.isg-one.com/analyst-perspectives/mongodb-enhances-developer-data-platform", "datePublished": "21/01/2025", "dateModified": "21/01/2025", "articleBody": "Despite all the interest in artificial intelligence (AI) and generative AI (GenAI), ISG’s Buyers Guide for Data Platforms serves as a reminder of the ongoing importance of product experience functionality to address adaptability, manageability, reliability and usability. While new and emerging capabilities might catch the eye, features that address data platform security, performance and availability remain some of the most significant deal-breakers when enterprises are considering potential data platform providers. This is especially true for mission-critical workloads. Adaptability, manageability, reliability and usability are also typically areas in which mature, incumbent software providers tend to have an advantage over emerging rivals. Although well-established as a developer data platform provider, MongoDB continues to add product experience functionality to compete with more established rivals. The launch of MongoDB 8.0 highlighted the recent advances the company has made in terms of performance, security, availability and resilience. MongoDB was founded in 2007 and has established itself as one of the most prominent NoSQL database providers with its document-oriented database and associated cloud services. The MongoDB Atlas managed service is available on Amazon Web Services, Google Cloud and Microsoft Azure. While the company continues to make its software available for self-managed deployment on premises or in the cloud via MongoDB Enterprise Advanced and the MongoDB Community Edition free download, the proportion of MongoDB’s revenue associated with Atlas has been steadily increasing since it was launched in 2016 and accounted for 71% of MongoDB’s $478.1 million revenue in the second quarter of fiscal 2025. ISG’s Market Lens Cloud Study illustrates the extent to which the database market is now dominated by cloud, with 58% of participants deploying more than one-half of database and data platform workloads on cloud. MongoDB has benefited from a focus on the needs of development teams to deliver innovation through the development of data-driven applications. It continues to position its document database product as a developer data platform which is primarily used to support the development and deployment of net-new applications rather than as a direct replacement for relational databases. MongoDB is nevertheless keen to position MongoDB Atlas as a replacement for more traditional databases as part of application modernization strategies and has invested in a variety of capabilities to assist potential customers to refactor applications developed for relational databases. The recent launch of MongoDB 8.0 is a reminder that while new developer-centric features might be key to developer-led adoption, enhancements related to adaptability, manageability and reliability are of critical importance to enterprise use cases. The latest version of the core database that underpins MongoDB Atlas, MongoDB Enterprise Advanced and MongoDB Community Edition features enhancements aimed at improving security, performance and availability. These include architectural optimizations to reduce memory usage and query times with more efficient batch processing to deliver better throughput, faster bulk writes and accelerated concurrent writes during data replication. MongoDB 8.0 also extends MongoDB’s Queryable Encryption capability, which was introduced in 2023. Designed to enable querying of sensitive data without the need to decrypt it, MongoDB Queryable Encryption now supports range queries. The latest version also adds data sharding improvements to accelerate horizontal scaling, as well as new controls that enable administrators to optimize database performance during spikes in demand. MongoDB 8.0 also delivers enhanced developer-centric features focused on the development of AI applications. The company added support for vector search in 2023, which can help organizations improve trust in the output of GenAI by providing access to factually accurate and up-to-date information. I assert that through 2026, almost all enterprises developing applications based on GenAI will explore vector search and retrieval-augmented generation (RAG) to complement foundation models with proprietary data and content. MongoDB 8.0 adds support for vector quantization, which takes advantage of compression to reduce memory requirements for vector processing, leading to lower costs and improved performance. Earlier in 2024, the company also announced the launch of the MongoDB AI Applications Program (MAAP), which is designed to assist customers in developing and deploying applications enriched with GenAI. MAAP combines strategic advisory and professional services with MongoDB Atlas and services from partners including Anthropic, Amazon Web Services, Cohere, Google Cloud, LangChain and Microsoft Azure. Improved support for the modernization of existing applications was also delivered in early 2024 as the company added functionality to its MongoDB Relational Migrator tool that enables GenAI-powered conversion of SQL queries, triggers and stored procedures to the MongoDB Query API syntax. While MongoDB continues to add new capabilities to its data platform, existing and potential customers should also be aware that the company recently announced plans to deprecate several previously heralded features. These include the Atlas Data Lake managed storage offering for analytics and the various components (Atlas Edge Server, Atlas Device Sync and Atlas Device SDK) that were designed to enable local data processing on mobile and IoT devices as well as in remote data centers or disconnected infrastructure. This will have come as a surprise to many, but the recent announcements highlight that the decision was a matter of development prioritization, rather than anything more significant. Unless directly impacted, I recommend that all enterprises evaluating potential data platform providers for new development and transformation projects should include MongoDB Atlas in their evaluations. Regards, Matt Aslett", "image": { "@type": "ImageObject", "url": "https://www.isg-research.net/hubfs/MongoDB_Enhances_Developer_Data_Platform.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "Matt Aslett", "url": "https://research.isg-one.com/analyst-perspectives/author/matt-aslett", "sameAs": "https://www.linkedin.com/in/matt-aslett/" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "research.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": "Preparing for Q-Day: Safeguarding Enterprises Against Quantum Threats", "mainEntityOfPage": "https://research.isg-one.com/analyst-perspectives/preparing-for-q-day-safeguarding-enterprises-against-quantum-threats", "datePublished": "16/01/2025", "dateModified": "16/01/2025", "articleBody": "As quantum computing advances at an unprecedented pace, the concept of Q-Day—a day when quantum computers can fundamentally undermine our current encryption methods—has entered discussions among cybersecurity professionals and business leaders alike. While there is no definitive date set for Q-Day, we are approaching a critical juncture where traditional cryptographic techniques may no longer suffice to protect sensitive data, digital communications and transactions. This impending shift not only poses significant risks for individuals but also presents a high-stakes event that every enterprise must anticipate and prepare for; inadequate preparation could lead to substantial data breaches, compromised systems and irrevocable damage to customer trust and organizational reputation. Drawing parallels between Q-Day and the Y2K crisis provides insights into the urgency of preparing for technological shifts. The turn of the millennium posed a significant threat to IT systems due to inadequate date representation in software—an issue that many organizations underestimated. Similarly, Q-Day represents a systemic risk to digital security, eroding the foundation on which secure communication is built. While Y2K presented a clear and predictable deadline, Q-Day’s precipice remains uncertain and unpredictable. However, both scenarios necessitate a proactive approach that prioritizes risk management strategies and cross-departmental collaboration. Just as Y2K prompted widespread audits and remediation efforts across industries, Q-Day should galvanize organizations to assess their cryptographic practices and prepare for a transformed security landscape. The ideal outcome in addressing Q-Day involves a strategic shift in enterprise security, characterized by the widespread adoption of quantum-safe algorithms, the development of innovative quantum cyber tools and the implementation of automated detection and response systems that can swiftly mitigate risks. The use of cryptography algorithms across enterprise applications has grown in recent years. ISG Research asserts that by 2026, over one-half of enterprises will require password encryption in digital security to reduce the risk of unauthorized access to corporate systems. Unfortunately, the reality is stark: many enterprises today still operate under outdated encryption standards, such as DES (Data Encryption Standard) and 3DES (Triple Data Encryption Standard), which are vulnerable to quantum attacks. Moreover, organizations are likely to face cybersecurity threats in the interim before quantum-safe algorithms become widely available. This means that enterprises could experience significant vulnerabilities—not only in terms of their data security but also with respect to operational integrity. As organizations cling to legacy encryption methods, they expose themselves to risks that could manifest as severe data breaches, compliance violations and reputational damage. Proactive measures are essential for enterprises aiming to safeguard against the impending Q-Day risks. Here are steps organizations can take today: Inventory Current Encryption Uses: Conduct a comprehensive audit that identifies all applications and services utilizing encryption protocols. This inventory serves as a critical first step to understand vulnerability exposure and prioritize remediation efforts. Build a Cryptographic Bill of Materials (CBOM): Taking inspiration from the Software Bill of Materials (SBOM), a CBOM will help organizations catalog all cryptographic algorithms, libraries and protocols in use. This structured inventory facilitates a deeper understanding of current dependencies and potential risks. Assess Risk to Q-Day Vulnerabilities: Evaluate the organization’s encryption landscape to identify any weaknesses stemming from outdated cryptographic methods. This risk assessment should include a thorough analysis of potential impacts on operations and data integrity. Develop a Business Case for Risk Mitigation: Communicate the significance of reducing risk exposure due to cryptographic vulnerabilities to stakeholders across different departments. Highlighting the consequences of inaction and the benefits of transitioning to quantum-safe practices will foster organization-wide support for necessary changes. The act of maintaining the current state of encryption poses a considerable risk to business operations. Outdated encryption methods like DES and 3DES invite breaches, regulatory scrutiny and potentially reputational damage. A desirable outcome is to implement quantum-safe encryption algorithms. However, they do not yet exist nor could they be tested. Until there are known quantum threats, the typical threat scanning methods serve no purpose. This reality leads to documenting enterprise encryption inventory as a step every organization can take today. As a Q-day preparation roundtable participant said, “Taking an inventory of encryption usage allows for knowing where all the fire exits are in the building without having to set the building on fire.” As quantum computing technologies evolve, the pressure on organizations to update their security protocols will only escalate. Failure to adapt could result in operational disruptions that hinder day-to-day activities, compromise customer data and damage stakeholder relationships. The longer that enterprises postpone necessary updates, the more significant the ramifications they will face when confronted with the quantum threat. To build a robust cybersecurity business case and prepare for Q-Day, enterprise leaders should prioritize the following steps: Establish Cross-Functional Teams: Create collaboration among IT, compliance, legal and operational departments to collectively address the impending quantum threats. Fostering collaboration will lead to comprehensive approaches that integrate various perspectives and concerns. Invest in Education and Awareness: Provide ongoing training and resources for key personnel to understand the ramifications of Q-Day and the critical need for transitioning to quantum-safe algorithms. Building awareness within the organization will enable everyone to contribute to a culture of security. Set Clear Milestones for Transition: Develop a roadmap that outlines clear action items, timelines and responsibilities for upgrading cryptographic systems. This structured approach will help organizations remain accountable and focused on their transition efforts. Engage with Cryptographic Providers: Partner with leading cryptographic solution providers that are actively researching and developing tools for quantum resilience. These partnerships can assist organizations in navigating the complexities of the transition toward quantum-safe infrastructures. As we stand on the brink of a new era in computing, it is imperative for enterprises to take proactive measures today to prepare for Q-Day. By evaluating current cryptographic practices, implementing a CBOM framework and communicating the importance of risk mitigation, businesses can enhance their security posture and ensure resilience in the face of emerging quantum threats. The time to act is now—organizations must lay the groundwork for a future where quantum resilience is not merely an option but an essential component of their cybersecurity strategy. Regards, Jeff Orr", "image": { "@type": "ImageObject", "url": "https://www.isg-research.net/hubfs/Preparing_for_Q-Day_Safeguarding_Enterprise_Against_Quantum_Threats.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "Jeff Orr", "url": "https://research.isg-one.com/analyst-perspectives/author/jeff-orr", "sameAs": "https://www.linkedin.com/in/jefforr" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "research.isg-one.com", "logo": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/ISG_Software_Research_Logo_Color@2x.png" } } }</script>  <script data-hs-allowed="true" src="/_hcms/forms/v2.js"></script> <script data-hs-allowed="true"> var options = { portalId: '2035844', formId: 'fa276fc9-9a16-4673-a644-1fcbedcc3c24', formInstanceId: '5255', pageId: '3884418273', region: 'na1', inlineMessage: "Thanks for subscribing!", rawInlineMessage: "Thanks for subscribing!", hsFormKey: "c1201f8981dddf542694db4ddabf69ca", 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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