A data catalog is an organized, searchable inventory of an organization’s data assets. It gathers metadata such as descriptions, schemas, ownership, lineage, and usage, so teams can find and understand datasets that live across many different systems.
Most catalogs build this inventory by scanning connected sources and indexing their metadata automatically. On top of that index they add search, tags, and a shared business glossary. For example, an analyst looking for active customer records can locate the right table, see who owns it, and check how recently it was updated before using it.
A catalog answers where the data is and what it means. Whether that data is actually usable in a given AI run, and whether the result can be reproduced later, is a separate readiness question that a catalog alone does not resolve.