data-governance

# Data governance Governance has a reputation for bureaucracy because it is usually implemented as approval queues. Done properly it is the opposite: it makes data usable without asking anyone. ## Start with definitions, not policy The highest-value governance artifact is a metric dictionary. For each business metric: - The **plain-language definition** — what it counts, and what it deliberately excludes. - The **computation**, unambiguously: source table, filters, time grain, timezone. - The **owner** — a person who decides when it is disputed. - **Known caveats** — when it is misleading, and what changed historically. Most metric disputes dissolve once both parties read the same definition and discover they were measuring different things. Almost none require a policy. Watch the ones that look obvious. "Active customer," "revenue," and "signup" each have half a dozen defensible definitions, and the ambiguity surfaces at the worst moment. ## Ownership Every dataset has a named owner accountable for its quality and access — a person, not a team. Unowned datasets decay, and nobody notices until a decision is made on stale data. The owner should sit with the business meaning, not with the pipeline. The team that generates the data understands what it means; the platform team understands how it moves. ## Quality, measured rather than asserted Test data like code, continuously, and alert on failures: - **Freshness** — did it arrive when expected? - **Volume** — is the...