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.
Because these technologies are not new, data leaders often take them for granted and overlook fresh business opportunities created by the market convergence of adjacent technologies and the emergence of better AI, data, and analytics practices. A similar trend unfolded with generative AI, another technology that was available for several years before rapid developments in the market led to an explosion of new opportunities that caught many organizations off guard. Data catalogs may have reached a similar inflexion point after recent developments in their critical role in enabling AI, data, and analytics governance.
Organizational plans for AI adoption are driving increased interest in data catalog technologies and data governance. Our research shows 52% of organizations experimenting with agentic AI in the second half of 2025, up from 23% in the first half of the same year. Despite such growing interest, organizations face two key concerns limiting wider adoption of AI: building trust and managing risk. Building trust requires a dynamic understanding of information assets (such as data, analytics, AI, and content), their condition across the enterprise, their lineage and curation, and their creation, consumption, and control within the information ecosystem. Combining this understanding with effective AI, data, and governance practices will enable data leaders to establish policies that codify baseline information behaviors. These policies in turn will help business areas execute appropriately and achieve strategic and tactical business objectives. Because AI policies must match a company’s risk appetite, effective risk management depends on good governance. Data catalogs make this possible by tracking data and enforcing rules.
Simply deploying data catalog technologies rarely creates business value. Although many organizations use these technologies primarily to support the data practices of central data teams (such as a BI competency center or center of excellence), they often forget the needs of key roles in the rest of the business, such as data stewards and business leaders.
Capabilities commonly available in many data catalog products include graphical workflow management, team collaboration, and AI-driven anomaly detection. However, data teams typically leverage these capabilities in their own center-oriented activities and processes, rather than in the business areas, where they could have the biggest business impact. To derive real business value from data catalog technologies, organizations must address this short-sighted view. Dresner Advisory Services’ AI, data, and analytics governance framework can help position data catalog technologies—along with people, processes, assets, and initiatives—in the wider context of business value (see the Research Insight “Is the AI, Data, and Analytics Governance Framework You Have the One You Need?”). Data leaders should re-evaluate the IT-centric manner by which data catalog technologies tend to be employed. Instead, they should target their use in the areas that will deliver the highest business impact.
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