Traditional metadata describes data and waits to be read, for example a column description in a data catalog. Active metadata is collected continuously from query logs, pipelines, and quality checks, then shared with other tools so it can drive behavior. Usage counts can rank which tables are trusted, schema changes can warn the owners of downstream dashboards, and sensitivity tags can apply access rules automatically.
For example, if a pipeline that feeds a revenue table fails overnight, active metadata can mark the table as stale in the catalog and notify the people whose reports read it. The idea turns metadata from documentation into a working signal. For AI, the same signals can tell a team which inputs a model or agent depends on changed, which narrows what needs to be checked again.