Vibeship-spawner-skills data-governance

id: data-governance name: Data Governance category: enterprise description: Use when implementing data governance frameworks, building data catalogs, establishing data lineage, defining data quality rules, or setting up data stewardship programs - covers metadata management, data quality, and compliance

patterns: golden_rules: - rule: "Data is a business asset" reason: "Treat it with same rigor as financial assets" - rule: "Accountability must be clear" reason: "Every data element needs an owner" - rule: "Quality is everyone's job" reason: "Not just a technical problem" - rule: "Automate where possible" reason: "Manual governance doesn't scale" - rule: "Measure outcomes" reason: "Track data quality, usage, value"

governance_roles: cdo: description: "Executive sponsor for data governance" accountability: "Overall program success" data_owner: description: "Business leader accountable for data domain" accountability: "Data quality and compliance in domain" data_steward: description: "Day-to-day data quality and metadata" accountability: "Operational data quality" data_custodian: description: "Technical management of data storage" accountability: "Technical data security and availability"

quality_dimensions: completeness: "Are all required values present?" accuracy: "Are values correct?" consistency: "Are values consistent across systems?" timeliness: "Is data current and updated timely?" validity: "Do values conform to rules?" uniqueness: "Are duplicates managed?"

data_classification: public: "Freely available" internal: "Internal use only" confidential: "Restricted access" restricted: "Highly sensitive" pii: "Personally identifiable information" phi: "Protected health information"

anti_patterns:

• pattern: "No ownership" problem: "Nobody accountable for data issues" solution: "Assign clear data owners"
• pattern: "Documentation only" problem: "Policies exist but not enforced" solution: "Automate enforcement"
• pattern: "IT-only governance" problem: "Business not engaged" solution: "Embed in business processes"
• pattern: "Big bang rollout" problem: "Try to govern everything at once" solution: "Start with critical data domains"
• pattern: "Quality as afterthought" problem: "Check quality at consumption" solution: "Shift left, check at source"
• pattern: "Manual lineage" problem: "Lineage outdated immediately" solution: "Auto-capture from pipelines"

implementation_checklist: governance_foundation: - "Data governance council established" - "Roles and responsibilities defined" - "Data policies documented" - "Operating model approved" data_catalog: - "Critical data assets inventoried" - "Metadata captured and maintained" - "Business glossary populated" - "Data classification applied" - "Search and discovery enabled" data_quality: - "Quality dimensions defined" - "Quality rules implemented" - "Monitoring dashboards active" - "Issue remediation workflow" - "Quality SLAs established" data_lineage: - "Upstream/downstream tracked" - "Transformation logic documented" - "Impact analysis enabled" - "Lineage auto-captured" access_control: - "Access request workflow" - "Role-based permissions" - "Audit logging enabled" - "Periodic access reviews"

handoffs:

• skill: enterprise-architecture trigger: "enterprise data architecture"
• skill: gdpr-privacy trigger: "data privacy requirements"

ecosystem: catalog: - "Alation" - "Collibra" - "Atlan" - "DataHub" quality: - "Great Expectations" - "dbt tests" - "Soda" lineage: - "OpenLineage" - "Marquez" - "Apache Atlas"

sources: frameworks: - "DAMA DMBOK" - "DCAM" references: - "Alation Data Governance Framework" - "Collibra Best Practices"