Data Catalogs in the AI Era: The Core Concepts You Actually Need to Understand First
There's a particular kind of pain that every data team eventually hits. A data scientist spends three days hunting for the "right" customer table. An analyst builds a dashboard on a column that was deprecated six months ago. A new hire asks where the revenue data lives, and four people give four different answers. Everyone has the data. Nobody can find it.
This is the problem data catalogs are built to solve, and in 2026, with AI agents now reading from the same warehouses humans do, solving it has gone from "nice to have" to "you cannot ship AI safely without it."