Data.
Not opinion.

Special Report: Cloud Computing

This is the 15th edition of our cloud computing and business intelligence (BI) study. This year, we are publishing our analysis as a Special Report rather than a full-length market study. Although the format shift reflects the market’s maturity, it should not lead our readers to make the mistaken conclusion that the conversation around cloud BI is no longer relevant.

During the nearly a decade and a half we have spent tracking this space, cloud BI adoption has reached record levels. Nevertheless, cloud adoption is still not a foregone conclusion for all organizations. A meaningful share continues to want or need on-premises deployment—whether for regulatory, security, cost, cultural, or architectural reasons. These organizations are not lagging behind; many have made deliberate, well-informed decisions about where their data and applications should reside. Unlike other analyst organizations, we have not  simply written off the on-premises market. The cloud “refuseniks,” as some might call them, are not going away.

2026-04-13

Special Report: Analytical Data Infrastructure

The 2026 ADI market is defined by systemic interdependence, in which the primary technical challenge has shifted from individual tool adoption to the seamless integration of security, logic, and cost predictability across the entire analytic data supply and consumption chain. Since organizations now manage an average of 79 disparate data sources, the “integration tax” has become a foundational hurdle rather than a peripheral one. In this complex landscape, the ability to manage the coexistence of legacy stability and modern innovation determines organizational success, particularly for those hitting the three- to five-year ADI architectural age “sweet spot,” where business intelligence (BI) achievement is highest.

2026-02-18

Special Report: Generative and Agentic AI

Artificial intelligence (AI) adoption remains uneven across organizations, with overall penetration, regardless of technique, still below 50%. This includes the following AI disciplines: data science and machine learning (DSML), generative AI, and the emerging class of agentic AI. Despite the hype, a majority of organizations are still early in their AI journeys, experimenting selectively rather than deploying AI at scale throughout their core business processes.

2026-01-20

Semantic Layer and Data Virtualization Market Study

This year’s study represents a significant evolution from our inaugural coverage of the semantic layer. We’ve completely updated our research criteria and expanded the scope to include data virtualization, reflecting how closely these two capabilities are aligned in practice. We also see both as key components of the broader analytical data infrastructure (ADI) ecosystem, and how central they’re becoming to modern data architecture strategies. Many technology providers now position their capabilities as supportive of these goals, and in some cases are solely focused on their delivery.

2026-03-25

Supply Chain Planning and Analysis Market Study

In this fourth edition of the Supply Chain Planning and Analysis Market Study report, we underscore the expanding role of performance management and analytics across various business functions beyond the traditional scope of finance. In the face of an array of external challenges, enterprises must take a more holistic approach to performance management to respond adaptively, make informed decisions, and execute strategies with precision.

2026-03-16

Data Engineering Market Study

Data engineering is a foundational component of analytical data infrastructure (ADI), providing the capabilities required to design, build, and operate data workflows and pipelines across operational and analytic environments. It encompasses requirements for data orchestration, integration, and transformation, including support for advanced analytics within the data engineering workflow.

This report examines the development and deployment capabilities used to design, debug, schedule, secure, govern, and run data workflows across analytical and operational use cases. It also evaluates the role of data engineering within the broader ADI ecosystem, which supports the ingestion, persistence, and transformation of data for downstream analytic, application, and AI-driven consumption.

2026-02-24

Data Leaders Need to “Hunker Up” for an External Forces Challenge

Many business issues that organizations and data leaders must grapple with connect directly or indirectly to the impact of external forces on their companies, customers, and suppliers. These external impacts must be understood in the context of the business, mitigated where necessary or possible, and leveraged to exploit opportunities or minimize negative effects.

Data leaders can use their understanding of external forces to guide data and analytics investment assumptions. It is best to start with the four external forces most frequently associated with negative impacts—economic uncertainty, staffing and recruitment issues, geopolitical instability, and cost of capital.

2026-04-09

Is Your MDM Capability Ready for Your Organization’s Future?

Many data leaders’ waking hours are consumed by the relentless sprint toward adoption of artificial intelligence (AI) within their organizations. They’re not being asked whether AI should be adopted, but when, how, and where agentic and generative AI can enable and improve business operations. Yet organizations pursuing rapid business value and technology innovation often fail to address a key limiting factor: their data maturity. Without the right data foundations in place, organizations are unable to meet the expectations that their AI, data, and analytics initiatives have promised in return for their investment. Consistent master data is one such foundational cornerstone, and is critical for building data trust across the enterprise.

2026-03-31

Is the AI, Data, and Analytics Governance Framework You Have the One You Need?

Achieving successful business outcomes depends on getting the right information to the right people at the right time to enable them to make the best-possible business decisions. Business leaders expect the data, analytics, and artificial intelligence (AI) assets they use to be consistent, trusted, and of good quality. Without these three features, decision makers cannot effectively identify market opportunities, manage operational complexity and cost, or understand and address business risk. However, a gap exists between what business leaders need to operate successfully, and what data and analytics leaders typically provide. The root cause of this gap is the lack of effective data and analytics governance.

2026-03-03