Data governance

Data governance is the set of policies, roles and processes that determine how an organization's data is defined, who owns it, who can access it, and how it is kept accurate and compliant with legal and internal requirements. It covers decisions like who can approve a new metric definition, which fields count as sensitive, and how long data is retained.

Governance is a management discipline, not a specific tool, though it is usually supported by technical infrastructure: a data catalog for discovery and documentation, data lineage to trace impact, access controls at the data warehouse level, and quality monitoring to enforce standards. It differs from data quality and master data management in scope: those are specific practices governance coordinates and holds accountable, rather than substitutes for it.

Governance matters most as organizations grow past the point where a handful of people can informally agree on what data means and who can touch it; without it, definitions drift, sensitive data gets exposed, and compliance risk accumulates quietly. Common pitfalls include designing governance as a purely defensive, restrictive function that slows analysts down without adding value, and writing policies that are never operationalized in the tools people actually use day to day.