Governance Organizational Structure
Effective data governance requires a dedicated organizational structure that bridges business and IT. The three-tier model—Data Governance Council (strategic), Data Domain Stewards (tactical), and Data Custodians (operational)—provides clear accountability at every level. The council sets policies, stewards enforce them within their domains, and custodians execute daily data management tasks.
- Data Governance Council: C-suite sponsored committee meeting monthly to set policy, resolve disputes, and allocate resources
- Data Domain Stewards: business-side owners per domain (customer, vendor, item, financial) with quality authority and accountability
- Data Custodians: IT-side administrators who implement technical controls, monitoring, and data quality tooling
- Chief Data Officer or Data Governance Lead: dedicated role coordinating across domains and reporting to the council
- RACI matrix: clear responsibility assignment for data creation, modification, quality monitoring, and retirement per domain
Core Governance Policies and Standards
Governance policies define the rules that protect data quality. Critical policies include data creation standards (what constitutes a complete record), modification controls (who can change what and with what approvals), quality thresholds (minimum acceptable quality scores), and retirement procedures (how records are deactivated without breaking references). Policies must be enforceable through system controls, not just documented procedures.
- Data creation policy: mandatory fields, duplicate check requirements, naming conventions, and approval workflows per object
- Modification policy: field-level permissions, change history requirements, and approval thresholds for critical attributes
- Quality standards: defined quality scores per domain (e.g., customer completeness >95%, item accuracy >98%)
- Retention and archival: data lifecycle rules aligned with regulatory requirements and operational needs
- Integration standards: data exchange formats, validation rules, and error handling for all system-to-system data flows
Data Quality Monitoring and Metrics
Governance without measurement is governance without teeth. Implement automated data quality monitoring that continuously measures completeness, accuracy, consistency, timeliness, and uniqueness across all governed domains. Dashboard these metrics for steward review and establish escalation triggers when quality scores drop below defined thresholds.
- Automated quality scoring: daily quality score calculation per domain using weighted dimension scores (completeness, accuracy, etc.)
- Trend analysis: track quality scores over time to detect degradation trends before they become critical issues
- Exception management: automated alerts when quality scores breach thresholds, with escalation to stewards and council
- Root cause analysis: investigate quality score drops to identify systemic causes (training gaps, process changes, integration failures)
- Benchmarking: compare quality metrics against DAMA or DGPO industry benchmarks to assess governance maturity
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Data Governance Maturity Assessment
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