At InterSystems, we work hard to maintain active, ongoing engagement with the leading data management industry analysts. This ensures we remain aligned with their evolving perspectives on the market, allows us to share insights into the work we’re doing with our customers and partners, and keeps the analysts informed about our latest innovations and product advancements.
Amid the rapid transformation underway in data management, two themes have emerged as especially critical—and foundational—to delivering faster, more reliable, and more trustworthy AI: converged data platforms and AI-ready data.
Converged Data Platforms
Gartner and many other analysts, as well as many enterprise organizations that we work with, are recognizing the emergence and importance of what they’re calling converged data platforms. These platforms reduce complexity, increase performance and efficiency, and reduce costs by converging, or combining, multiple data management services that have historically been available as independent services into one single offering.
Converged data platforms can provide a wealth of benefits, especially as they relate to AI and Gen AI initiatives. Many vendors approach convergence by simply bundling different independent services, all built with different architectures and all requiring their own copy of the data. Our approach, of course, is to deliver all of these capabilities—including multi-model and multi-workload database management, integration and service orchestration, and various analytics capabilities—in a single engine, built from the ground up, leveraging one single representation of data: our “common data plane.” Our approach to convergence results in simpler architectures, higher ROI, reduced fragility, eliminates data duplication, and delivers more trustworthy data for AI initiatives since there is one, single, overarching model for security, governance, lineage, and semantics across all data.
Some key analyst reports that highlight this trend towards converged data platforms follow, with some of the more relevant extracts from each report. All of the reports mentioned in this blog post are available from InterSystems, free of charge, for your reading pleasure.
“Organizations are grappling with component data management service proliferation, often struggling to manage growing numbers of specialized systems while facing operational complexity, compliance obligations, and cost optimization challenges. Enterprises, particularly those constrained by limited resources or technical staffing shortages, increasingly recognize that best-of-breed strategies create unsustainable complexity, driving them toward converged platforms over time that offer streamlined management, operational simplicity, and integrated security. As a result, the market is experiencing a fundamental shift toward converged solutions that integrate document, graph, relational, and vector data models alongside data integration and data management capabilities.
The convergence of multi-model database requirements, real-time analytics demands, and stringent security needs creates a compelling case for InterSystems IRIS® adoption. To investigate the gap between first order data platform adoption and reevaluations, Nucleus interviewed multiple enterprise organizations who migrated to InterSystems IRIS after experiencing challenges with their initial data management strategy. Customers cited convergence, enterprise scale support, and component solution fatigue as key factors motivating the shift, with adoption driving up to 98 percent improvement in risk and claims filtering accuracy, and consolidation of hundreds to thousands of data management services. As data platform demands continue to rise in support of new workloads and growing data footprints, Nucleus expects InterSystems to become the destination for enterprises as they mature beyond component architectures to achieve greater efficiency and cost savings.”
IDC MarketScapes provide in-depth quantitative and qualitative technology market assessments of vendors for a wide range of technology markets. This worldwide analytical database MarketScape provides a comprehensive assessment of the top 14 vendors in the space, along with IDC’s observations on the market and advice for technology buyers.
“The analytical database market is entering a new phase of innovation as advances in storage and compute architectures redefine how enterprises extract value from data. For technology buyers, this research provides a strategic framework to evaluate analytical databases based on flexibility, interoperability, and AI readiness: the new drivers of competitive differentiation in enterprise analytics.
Consider converged workloads built on hybrid transactional and analytical processing (HTAP) architectures for real-time decisioning. Modern analytical database platforms increasingly support converged workloads that unify transactional and analytical processing within a single system. HTAP enables organizations to analyze live operational data without replication or delay, providing the foundation for real-time decisioning. Buyers should assess whether these capabilities meet requirements such as fraud detection, personalization, or supply chain visibility while maintaining transactional integrity and analytical performance on the same data. Platforms that support both workloads improve responsiveness, reduce data movement, and strengthen governance. HTAP architectures are also becoming essential for agentic AI, where real-time, context-rich data enables autonomous, data-driven decisioning across the enterprise.
After a thorough evaluation of InterSystems' strategies and capabilities, IDC has positioned InterSystems in the Leaders category in this 2025 IDC MarketScape for worldwide analytical databases. InterSystems IRIS unifies relational, document, key value, object, vector, and time-series models in a single engine. This multimodel approach allows enterprises to support operational, analytical, and emerging AI-driven workloads without relying on multiple systems. InterSystems IRIS reduces duplication and accelerates insight.”
AI-Ready Data
There are numerous analyst quotes, reports, and anecdotes that qualify and quantify the importance of AI ready data on the success of AI projects. For example, one Gartner survey of IT executives finds that only 4% of firms report their data is AI ready (Gartner, 2024, Top Trends in Data and Analytics, 2024) and BARC’s survey data reports that data issues are among the top obstacles to AI success. Our customers tell us that our approach to delivering AI ready data through a non-disruptive overarching smart data fabric layer leveraging a converged architecture, with common security/lineage/governance/semantics is a requirement for successful AI and GenAI outcomes.
In addition, providing embedded AI capabilities supporting multiple forms of AI, including predictive and prescriptive analytics, and Gen AI/RAG/Agents/Vector functionality eliminates the need to copy data to separate environments for analytics processing, improving efficiencies, lowering latency, and reducing the opportunity for error.
Key reports that highlight the importance of AI-ready data are below.
This Gartner report is loaded with interesting survey results and guidance on the critical nature of AI ready data, and required changes to data architectures, platforms, and processes to enable successful AI initiatives.
“Data and analytics (D&A) leaders are under extreme pressure to support their organization’s urgent and mission-critical AI efforts. While most organizations have invested over the years in traditional data management architectures and practices, those that fail to realize the differences between AI-ready data requirements and traditional data management will endanger the success of their AI efforts.”
Gartner research has shown:
- Lack of data is one of the top three barriers to implementing AI techniques for close to 40% of respondents to the 2023 Gartner AI in the Enterprise Survey.
- Over 75% of organizations state that AI-ready data remains one of their top five investment areas in the next two to three years, according to Gartner’s 2024 Evolution of Data Management as a Dedicated Function Survey
Gartner, Inc. (2024). Evolution of Data Management In A Journey Guide to Deliver AI Success Through AI‑Ready Data. Gartner.
According to BARC’s recent survey of 421 global organizations, three of the top six obstacles to successful AI projects are data related, including data quality (#1), integration issues (#3), and insufficient data/data access, reinforcing that AI ready data is critical for successful AI and is still an unsolved challenge.
“Data quality has jumped ahead of all other obstacles. The 2024 results ranked data quality at 19%, in the bottom third of listed challenges. As more projects were delivered, data quality rose to the number one obstacle in 2025 for success in AI projects with 44% of respondents listing it as the top challenge. The old adage of "garbage in equals garbage out" holds true for AI just as it does for traditional analytics. Poor data quality impacts context of outputs and certainly accuracy. The lesson here is that failure to address ongoing data quality issues will limit your company’s ability to deliver impactful AI projects.”
Another important thing to note from the survey is the need to manage real-time data and various forms of unstructured data, which can be a challenge for organizations since many data management providers still focus primarily on managing structured, batch data.
Celent is a well-regarded analyst firm covering the banking industry. This report documents the results of a survey (sponsored by InterSystems) of more than 100 banking executives in North America and the UK. The key takeaways from the report are that banks are investing in improving the performance of their AI (decision intelligence) models, e.g. for fraud, payment processing, credit risk, customer intelligence, loan origination, etc., and the top challenges reported are all around the need for better data. Note that the top five barriers to enhancing decision intelligence (AI models) reported by the banks are all related to data issues.
The ISG Buyers Guide™ for Data Platforms evaluates data platform software providers and products in eight key areas including data management, data query, data engineering functionality, and others. This report ties together the two themes: how AI readiness demands a converged data architecture.
“The data platforms sector has traditionally been segmented between operational data platforms deployed to support applications targeted at business users and decision-makers to run the business and analytic data platforms typically supporting applications used by data and business analysts to analyze the business. The increasing importance of intelligent operational applications driven by AI is blurring the lines that have traditionally divided the requirements for operational and analytic data platforms. Consumers are increasingly engaged with data-driven services that are differentiated by personalization and contextually relevant recommendations. Worker-facing applications are following suit, targeting users based on their roles and responsibilities. The shift to more agile business processes requires ML for more responsive data platforms and applications. 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, generative AI and agents.
We assert that through 2027, data platform providers will prioritize the development of hybrid operational and analytic processing functionality to meet the requirements of intelligent applications driven by GenAI.”
Of course, here at InterSystems we have been championing this converged approach to data management for many years, and we could not be more honored to be recognized as a Leader with the second highest overall score from among the top 24 data platform vendors by ISG in their latest Buyer’s Guide.
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There’s a lot to read in these reports, and I hope they provide value that you can apply to your respective organizations as you navigate this new, rapidly evolving world of agentic and generative AI.
But at the end of the day, for InterSystems, making sure we stay on the leading edge of technology isn’t about analyst recognition, it’s about ensuring our customers’ success. As our customers continue to innovate, deliver new business value for their customers, and differentiate in increasingly competitive markets, we continue to work hard to advance the data and analytics foundation that makes their successes possible.
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