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 row count within its normal range? A silent drop to zero is the classic

failure.

• Uniqueness and nullity on key fields.
• Referential integrity across joins.
• Distribution — has the shape shifted in a way nothing explains?

The point is finding breakage before a decision is made on it. A pipeline that fails loudly is

better than one that silently produces yesterday's numbers.

Access

Default to open for internal, non-personal data. Restrictive-by-default drives the shadow spreadsheet

layer, which is genuinely less safe than a governed warehouse.

Personal, financial, and regulated data are the exception: least privilege, purpose stated, reviewed

periodically, with Legal & Risk involved on anything with a lawful-basis question.

Lineage

Know where a number came from and what feeds it. Without lineage, you cannot answer the two

questions that matter during an incident: what broke upstream, and what downstream is now wrong.

Sources

references/sources.md in this skill lists the outside authorities that settle the questions

here — what each one is authoritative for, and what you may do with it. Check them before

answering on anything they cover, and cite what you used. Most are free to read and not free

to reproduce; the use note on each is binding.

Never

• Let two systems each claim to be the source of truth for the same fact.
• Fix a data-quality issue in a dashboard. Fix it upstream or it recurs in every other consumer.
• Retire a dataset because it looks unused — you cannot see every consumer. Deprecate, announce,

then remove.