Data governance & quality checklist

Data governance & quality checklist

This concise checklist helps teams turn high-level data governance goals into concrete, testable actions. For each item below, use the suggested owners and acceptance criteria as a starting point and adapt them to your context.

1. Define data owners and stewards
   • Why it matters: Clear ownership prevents ‘no one is responsible’ gaps and speeds resolution when problems appear.
   • Typical owners: Product or domain owner (data owner); data platform, ETL, or analytics engineer (data steward).
   • Acceptance criteria / quick checks:
     • Every critical dataset has a named owner and a steward listed in the data catalog.
     • Owners have documented responsibilities (quality, access approvals, lifecycle).
2. Document schemas and lineage for critical datasets
   • Why it matters: Schema and lineage make data discoverable, explainable, and easier to trust.
   • Typical owners: Data steward, analytics lead, or data architect.
   • Acceptance criteria / quick checks:
     • Schemas (field names, types, business meaning) are published in the catalog for each critical dataset.
     • Lineage shows upstream sources and key transforms for each dataset used in reporting or models.
3. Set quality SLAs and error-handling rules
   • Why it matters: SLAs set expectations and drive automation for monitoring and alerts.
   • Typical owners: Data owner with operations/engineering support.
   • Acceptance criteria / quick checks:
     • Quality KPIs (completeness, accuracy, freshness, uniqueness) defined per dataset.
     • Thresholds and automated alerts are configured; error-handling steps are documented (retry, quarantine, notify).
4. Establish access controls and privacy guardrails
   • Why it matters: Protects sensitive information and ensures the right people can use the right data.
   • Typical owners: Security or privacy lead, data steward, IT/cloud admin.
   • Acceptance criteria / quick checks:
     • Access policies are documented (who can read, who can edit) and enforced via role-based controls.
     • Sensitive fields are classified and appropriate masking or encryption applied.
5. Monitor data usage and freshness
   • Why it matters: Monitoring surfaces stale or unused datasets and helps prioritize maintenance.
   • Typical owners: Data platform team, analytics team, or data steward.
   • Acceptance criteria / quick checks:
     • Automated freshness checks run on critical datasets; failures generate alerts.
     • Usage metrics (who queries what, frequency) are available to help retire or improve datasets.

Next steps: Tag the critical datasets (e.g., reporting, compliance, ML) and run this checklist for those first. Consider turning the acceptance criteria into simple dashboard tiles or an interactive audit so owners can demonstrate compliance. Revisit SLAs and ownership at regular intervals or when business processes change.