5 things to know right now about modern data catalogs
As data catalogs shift from passive registries to platforms that drive AI and analytics, utilizing them to improve data use and governance is a priority for data leaders.
Data catalogs are a cornerstone of data management and governance initiatives, providing a unified inventory of enterprise data assets. But as organizations expand AI deployments and increasingly broaden data access for analytics, catalogs are evolving from basic data directories to full-fledged platforms with built-in metadata management, data stewardship, access control and governance policy enforcement capabilities.
"Traditional catalogs focused primarily on documenting data assets for discovery purposes," said Anne Marie Smith, principal consultant at Alabama Yankee Systems. That helped data analysts and business users find relevant data for applications. But Smith said today's catalogs more fully empower both human and AI users with trusted datasets and required context, while also enabling data teams to implement automated controls and compliance monitoring mechanisms.
Here are five characteristics of modern data catalogs that are changing the requirements for planning and managing catalog deployments in organizations.
1. They're at the center of governance programs that prioritize effective data use.
Smith said data governance programs increasingly emphasize enabling responsible data access and use rather than simply enforcing controls -- and data catalogs are the primary tool for connecting users with trusted, well-governed data and making it visible across an organization.
That's the case at Children's Hospital of Philadelphia. CHOP's data governance team -- renamed the Data Trust Office (DTO) in 2024 to signal its primary focus on creating a centralized repository of high-quality data for broader use within the hospital -- built a data catalog before developing a formal governance policy.
Samantha Reilly, enterprise data governance portfolio lead at CHOP, said the Alation-based catalog is the face of the governance program to hospital employees. It provides a wide range of users -- 5,500 as of May 2026, up from 500 three years ago -- with access to data that previously wasn't available to many of them, Reilly said during Dataversity's AI Governance Online conference last month.
2. Catalogs are shifting from passive documentation of datasets to active governance orchestration.
Donald Farmer, principal of consultancy TreeHive Strategy, said modern data catalogs are no longer passive registries that require "countless hours" of manual data stewardship work to input data definitions, tag sensitive data and map data lineage documentation to assets. Those functions can now be automated along with data governance actions in both upstream sources and downstream applications, Farmer said.
Data catalogs are increasingly becoming governed "intelligence platforms" in their own right, said Kevin Petrie, a data management analyst at BARC U.S. Through automated capabilities for managing metadata and applying data quality and governance controls, they provide users with needed semantics and context in a trustworthy, secure and machine-readable format, he said.
3. They're becoming a foundational component of successful AI applications.
ROI is on everyone's mind, and good AI economics starts at the data layer.
Stephen CatanzanoSenior analyst, Omdia
"There was a misconception early on that you can point AI at a mountain of data and it will pull out the most relevant information," said Stephen Catanzano, a senior analyst at Informa TechTarget's Omdia research and advisory division. "That not only doesn't work -- it's also costly in token usage and time. ROI is on everyone's mind, and good AI economics starts at the data layer."
Catanzano added that access permissions and governance guardrails can also be incorporated into catalogs to control AI's use of data.
Martin Obiozor, a data management solution architect at ExxonMobil, said during an AI Governance Online presentation that the governance team there is working to relate the oil and gas company's data assets to AI use cases in its Collibra data catalog. For example, the team plans to document the data used to train an AI model and the applicable governance policies.
"One of the headwinds in this is that not all our data is cataloged," Obiozor said. But he added that the ongoing effort -- which is expected to take six to nine months to complete -- will require cataloging of needed data to get planned AI uses approved.
4. Catalogs increasingly serve as internal marketplaces for data products.
Farmer said modern data catalogs inventory not only individual data assets but also reusable data products, such as dashboards, reports and curated datasets. That's more user-friendly than simply "loading tens of thousands of tables into a search index," he said, adding that a data marketplace might be organized by business domain and include data quality certifications, trust scores, usage histories, user ratings and deprecation notices about out-of-date data products.
The governance team at CHOP incorporates some of those elements into its data catalog. For example, Reilly said it applies a "High Quality Data" badge to certify that data assets are clean and accurate. In some cases, the DTO also notes when the quality of items in the catalog was last checked and when the next review is scheduled to help users assess their trustworthiness.
5. Platform-native vs. enterprise catalogs is now a common buying decision to consider.
Catalog tools built into data platforms by Databricks, Snowflake and the major cloud hyperscalers have become capable alternatives to Alation, Atlan, Collibra, Informatica Data Catalog and other enterprise catalog technologies from a functionality standpoint. But Farmer said the platform-native tools typically "know little or nothing about data not on that platform."
That's good enough for building a data catalog for an individual platform, he said, noting that tools like Databricks Unity Catalog and Snowflake Horizon Catalog provide fast, integrated access control along with auditing and data lineage capabilities. However, organizations commonly also need an enterprise data catalog to support cross-platform data discovery, stewardship and governance. "In practice, most large enterprises end up with both," Farmer said.
Craig Stedman is a senior reporter on TechTarget's Data Technologies news team, covering analytics, data management and data backup topics. He has more than 40 years of experience as an enterprise IT writer and editor.
