Picture a new analyst asked for “active customers last quarter.” The company has three tables with “customer” in the name, two dashboards that disagree and a spreadsheet someone emailed in 2023. A catalog answers the questions they would otherwise ask around the office: which table is the official one, what “active” means, when it was last updated, who owns it and whether they are allowed to see it.
Metadata comes in a few flavors. Technical metadata describes schemas, column types and file locations. Business metadata adds plain-language descriptions, definitions and owners. Operational metadata records freshness, usage and data quality checks, and lineage shows which upstream sources and pipelines produced a table. Catalogs usually also hold access policies, which makes them part of data governance.
AI agents make catalogs more important, not less. An agent that writes SQL against a data warehouse has no office to walk around; descriptions, definitions and permissions in the catalog are the context it gets. That overlaps with a semantic layer, which pins down metric definitions, and with a broader context layer. Data lineage for AI covers the history side in more depth.
In episode 27 at 15:51, Cohesity’s Greg Statton describes “a global data catalog” combining traditional metadata, security enhancements and newer semantic metadata, his description of the direction his company’s backup data is taking. In episode 26 at 37:10, Databricks’ Carly Taylor points to products that integrate “directly into your data catalogs, like with Unity Catalog,” her employer’s product. Both are Season 2 episodes sponsored by Galileo; in episode 27 Conor mentions working with Galileo’s enterprise customers (31:17). Episode 26 was sponsored by Galileo.
Hear it from the guest
“Think of it like a global data catalog that encompasses traditional metadata, kind of augmented traditional metadata with security enhancements.”
“There are tons of products that are integrating directly into your data warehouse, directly into your data catalogs, like with Unity Catalog and Databricks”
Quotes lightly edited to remove filler words.
Sources
- IBM Think: What is a data catalog? — Defines a data catalog as a detailed inventory of an organization's data assets that uses metadata and data management tools to help users discover, understand, manage, curate and access data; checked October 24, 2026.
- Databricks: What is Unity Catalog? — Vendor documentation for a catalog that acts as a governance layer for data and AI assets, enforcing access control when tables are queried; used as an example of a product, not as a neutral comparison; checked October 24, 2026.
