| name | data-governance-and-quality |
| description | Defines ownership, policies, quality rules, controls, monitoring and remediation. Use when establishing data governance, quality SLAs, or remediation workflows. |
Data Governance and Quality
When to use
Use for governance design, quality frameworks, stewardship, issue management and controls.
Objective
Produce a practical, concise, traceable architecture artefact that a coding agent can use to guide implementation or review.
Procedure
- Identify domain, critical data and consumers.
- Assign owner, steward and decision rights.
- Define policies and standards.
- Define quality dimensions/rules.
- Define thresholds and severity.
- Define validation and monitoring.
- Define issue/remediation workflow.
- Define evidence requirements.
Required outputs
- Owners and decision rights
- Policies/standards
- Quality rules and thresholds
- Issue/remediation workflow
- Evidence requirements
Best-practice alignment
Apply DAMA-DMBOK2-style separation of data governance, architecture, modelling, security, integration/interoperability, master/reference data, metadata and quality. For cloud/shared data, apply CDMC-style expectations: ownership, classification, entitlement/access evidence, lineage/provenance, lifecycle/retention, quality controls and auditable evidence.
Quality checks
- Quality rules are testable.
- Owners are named by role.
- Remediation has workflow and status.
- Evidence is retained.
Avoid
Do not define governance as committees without enforceable controls.
References
Verification
