Data.
Not opinion.

Data Catalogs in 2026

For many years, data catalog technologies have been valued by organizations for their ability to enhance data discovery, enable data governance and compliance, foster collaboration, support data integration and analytics, and facilitate data-driven decision making. As a foundational technology, especially when combined with data governance capabilities, data catalog technologies underpin effective data management and utilization of information assets within organizations.

2026-05-11

Data Observability in 2026—Crucial for Modern BI, Analytics, and AI

Use cases for business intelligence (BI), analytics, and artificial intelligence (AI) are growing more complex, involving more data sources, platforms, locations, and a greater variety and number of data producers and consumers. This means data flows are becoming exponentially more complex, transformation logic is much more sophisticated, and more opportunities exist for errors and leaks. At the same time, organizations’ emphasis on data governance—of quality, security, privacy, lifecycle management, and costs—is also increasing, as data leaders, business leaders, and management teams recognize the importance of leveraging and protecting data assets.

2026-05-05

Data Security and Privacy in 2026

Cybersecurity has evolved in distinct stages. It began with firewalls that maintained system integrity and excluded unauthorized users. This “castle and moat” model worked reasonably well as long as there was a clear inside and outside. As landscapes shifted, the era of perimeter defense gave way to access control and identity management, focusing on verifying who could enter and what they could touch. But as threats grew more complex and data became more distributed, protecting systems solely by limiting access proved insufficient for data security or data privacy.

2026-04-16

Agentic AI-Assisted Analytics

While still early, the signal is clear: agentic AI is poised to fundamentally re-imagine what self-service BI means. Vanguard users are already beginning to redefine the category, not as a set of tools which users query, but as an intelligent layer that works on their behalf. The impact will touch every dimension of the BI workflow, from agents that discover and surface relevant data for consumers, to agents that autonomously apply analytical models to analyze and predict outcomes.

Particularly significant for BI is the emergence of agents that anticipate rather than just respond. These include agents that predict context and deliver answers before a question is even asked, as well as agents capable of taking autonomous action on insights. Taken together, the trajectory is becoming clear: agents will find the data, determine the context, apply the reasoning, and close the loop, transforming self-service from a user-driven search into an intelligence-driven conversation.

2026-05-18

Self-Service Business Intelligence

In the 15th edition of this report, self-service BI is at a turning point. It emerged as the speed of business accelerated and management needed to move beyond the machine-age model in which decisions were pushed to the top of the organization. As the machine age model broke down, domain experts who were closer to the action began to make decisions. In this world, even 30-day-old reports or dashboards became immediately obsolete. The implication was clear: distribute both data and decision-making. This shift gave rise to self-service BI aimed at democratizing access to data at scale.

Momentum is likely to accelerate next year as agentic solutions enter broader adoption. Still, near-term growth will be concentrated among the roughly 32% of organizations that have succeeded with BI.

2026-05-18

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

Data Leaders Have Their Priorities Backwards When Evaluating Data Engineering Capabilities

When considering data engineering tools as part of an overall portfolio for analytical data infrastructure (ADI), three primary categories of functionality are relevant. Teams selecting data engineering tools tend to emphasize features for their speed (ease of development and deployment) and reach (range of data source and environments supported). But they often don’t pay enough attention to aspects of control (governance functionality, metadata management capabilities, and features to manage reliability and cost of data flows). The lack of emphasis on control could cause poorly governed, opaque, inconsistent, and unreliable data flows to overrun an environment, adding risk, cost, and far too much work for data leaders and their teams.

2026-04-30

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