Data governance best practices

A governance model that scales starts with the organization, not the technology. Split the data landscape into domains that match how teams work (finance, supply chain, customer) and assign each domain to an owner with a mandate. Whoever configures the tool first and thinks about ownership later builds a catalog that fills up but that nobody maintains.

Technical column names mean little to an analyst. Link assets to a business glossary with clear definitions, so that "revenue" means the same thing everywhere and discussions are about numbers instead of definitions. The glossary is the heart of the catalog and deserves as much maintenance as the data itself.

Data stewards guard quality, definitions, and access within their domain. Name them explicitly, give them time, and make their work visible. Governance that hangs off the side of the real work waters down within a quarter.

Manual cataloging keeps up with no growing platform. Schedule automated scans that discover assets in Microsoft Fabric and Azure, classify them, and enrich them with sensitivity labels. That keeps the catalog current without anyone having to type into it every day.

Measure coverage, quality, and ownership, and put those numbers on a dashboard that management actually looks at. A governance program with no visible KPIs disappears the moment attention fades. Numbers keep the conversation alive and the budget standing.