Data Governance Audit: Practical Checklist

1) Define the audit objective: align on expected outcomes (risk reduction, better data quality, more reliable reporting, faster AI adoption) and set measurable success criteria.

2) Scope the audit clearly: covered processes, priority data domains, entities involved, regulatory constraints, and in/out systems so the audit stays focused and useful.

3) Assign key roles: executive sponsor (decisions), data owner (business accountability), data steward (quality and definitions), IT/security lead (technical controls), legal/DPO (compliance), PM (coordination).

4) Map critical data flows: sources, transformations, consumers, interfaces, and transfers. Capture flow criticality and failure points (quality, access, latency, traceability).

5) Assess maturity on a simple 1-5 scale: governance, quality, security, compliance, tooling, and monitoring. For each dimension, record evidence, gaps, and risk level.

6) Produce action-ready deliverables: prioritized diagnosis, risk register, quick-win plan (4-8 weeks), structural roadmap (3-12 months), target RACI, and monitoring KPIs.

7) Set realistic timing expectations: quick audit (2-3 weeks), standard audit (4-6 weeks), multi-entity audit (8-12 weeks), depending on stakeholder availability and current maturity.

8) Plan the readout and follow-through: review findings with teams, validate priorities with leadership, and launch an implementation plan with monthly governance checkpoints.