data-governance-and-quality

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

1. Identify domain, critical data and consumers.
2. Assign owner, steward and decision rights.
3. Define policies and standards.
4. Define quality dimensions/rules.
5. Define thresholds and severity.
6. Define validation and monitoring.
7. Define issue/remediation workflow.
8. 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

• DAMA-DMBOK (DAMA International)
• EDM Council CDMC (Cloud Data Management Capabilities)

Verification

• Required artefacts produced and linked to scope.
• Decisions, assumptions and risks stated explicitly.
• Quality checks or validation performed.
• Files changed reported with traceability preserved.