📈 Data Governance Maturity Model (Self-Assessment)
Use this model to assess a client's governance maturity before and during implementation. Score each dimension 1-5.
Maturity Levels
Level 1: Initial / Ad Hoc
No formal governance. Data management is reactive and individual-dependent. No defined roles, no policies, no standards. Data quality issues are common but not tracked.
Level 2: Repeatable
Some governance practices exist in isolated pockets. Individual departments may have informal data stewards or quality checks. Processes are documented but not standardized across the organization.
Level 3: Defined
Enterprise-wide governance framework is in place. Roles are defined, policies are published, a DGC exists. Business glossary is established. Data quality is monitored but not consistently managed.
Level 4: Managed
Governance is measured with KPIs and metrics. Data quality is actively managed and improving. Governance is embedded in business processes. Stewardship is well-established. Cross-domain coordination is effective.
Level 5: Optimized
Governance is fully embedded in organizational culture. Data is treated as a strategic asset with quantified business value. Continuous improvement is self-sustaining. Governance adapts proactively to new challenges. Industry-leading practices.
Assessment Dimensions
Score each dimension 1-5 using the level descriptions above.
People & Organization
- ☐ Are governance roles formally defined?
- ☐ Is there a dedicated DGO?
- ☐ Are data stewards active in each domain?
- ☐ Is there executive sponsorship?
Policies & Standards
- ☐ Do enterprise data policies exist?
- ☐ Are policies actively enforced?
- ☐ Is there a policy review cycle?
- ☐ Do data standards exist for naming, quality, security?
Processes & Workflows
- ☐ Is there a governance issue resolution process?
- ☐ Are data change requests formally managed?
- ☐ Is governance embedded in the SDLC?
- ☐ Are DQ incidents tracked and resolved?
Technology & Tools
- ☐ Is there a data catalog?
- ☐ Is data quality automatically monitored?
- ☐ Is there a business glossary tool?
- ☐ Is metadata management automated?
Data Quality
- ☐ Are DQ dimensions defined and measured?
- ☐ Are DQ rules documented and automated?
- ☐ Is DQ trending upward?
- ☐ Is the cost of poor quality tracked?
Metadata & Documentation
- ☐ Is there an enterprise business glossary?
- ☐ Is data lineage documented?
- ☐ Is technical metadata harvested automatically?
- ☐ Can users find and understand data assets?
Culture & Awareness
- ☐ Do employees understand their role in data quality?
- ☐ Is there a data literacy program?
- ☐ Is governance seen as enabling (not blocking)?
- ☐ Are data champions recognized?