Data Governance Maturity Assessment

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🏛️ Data Governance Foundation

Assess your organization's data governance structure, roles, and overall strategy

1. Does your organization have a formal data governance program?

No formal program exists

Program is being planned or initiated

Program exists but is not fully operational

Fully operational program with clear processes

Mature, optimized program with continuous improvement

2. Is there executive sponsorship for data governance initiatives?

No executive awareness or support

Limited awareness, minimal support

Moderate support from one or two executives

Strong C-level sponsorship with allocated budget

Board-level commitment with strategic priority

3. Are data ownership and stewardship roles clearly defined?

No defined roles or responsibilities

Informal roles without clear accountability

Roles defined for critical data domains

Comprehensive RACI matrix with active stewards

Well-established roles with performance metrics

✅ Data Quality Management

Evaluate your data quality processes, monitoring, and improvement initiatives

4. How is data quality measured and monitored?

No measurement or monitoring in place

Ad-hoc manual quality checks

Basic automated quality rules for critical data

Comprehensive quality framework with dashboards

Real-time monitoring with ML-based anomaly detection

5. Is there a formal data quality improvement process?

No formal process for improvement

Reactive fixes when issues are discovered

Defined process with prioritization framework

Proactive root cause analysis and prevention

Continuous improvement with Six Sigma/Lean practices

6. How is data profiling performed?

No data profiling capabilities

Manual profiling for specific projects

Automated profiling for new data sources

Regular profiling with trend analysis

Continuous profiling with AI-powered insights

🏗️ Data Architecture

Assess your data architecture design, standards, and integration with business processes

7. Is there a documented enterprise data architecture?

No architecture documentation

Fragmented documentation for individual systems

Enterprise architecture with major data flows

Comprehensive architecture with as-is and to-be models

Living architecture with automated discovery

8. Are data architecture standards enforced?

No standards exist

Standards documented but not followed

Standards enforced for new projects

Enterprise-wide enforcement with review process

Automated compliance checking in CI/CD pipeline

9. How well is data architecture integrated with SDLC?

No integration with development lifecycle

Ad-hoc architecture reviews

Required review at key project milestones

Integrated throughout SDLC with templates

Shift-left with automated architecture validation

📚 Metadata Management

Evaluate your metadata catalog, lineage tracking, and business glossary

10. Is there a centralized metadata repository?

No metadata management

Spreadsheets and documents scattered

Basic catalog for critical data assets

Enterprise data catalog with search capability

Automated catalog with AI recommendations

11. Is data lineage tracked end-to-end?

No lineage tracking

Manual documentation for critical flows

Partial automated lineage for key systems

Comprehensive lineage across enterprise

Real-time lineage with impact analysis

12. Does a business glossary exist and is it maintained?

No business glossary

Informal definitions in various documents

Basic glossary for critical terms

Comprehensive glossary with governance workflow

Active glossary with AI suggestions and validation

🗄️ Primary Data Management (MDM)

Assess your master data management for customer, product, and reference data

13. Is there a Master Data Management (MDM) system in place?

No MDM capabilities

Manual consolidation in spreadsheets

MDM for one domain (customer or product)

Multi-domain MDM with data quality rules

Enterprise MDM with ML-based matching and golden records

14. How are duplicate records identified and resolved?

No deduplication process

Manual identification when issues arise

Rule-based matching for exact duplicates

Fuzzy matching with steward review

AI-powered matching with automated resolution

15. Are data hierarchies managed and published?

No hierarchy management

Static hierarchies in individual systems

Centralized hierarchies for reporting

Multiple hierarchy versions with effective dating

Dynamic hierarchies with real-time updates

⚙️ Data Operations

Evaluate your data operations capabilities including monitoring, incident management, and SLAs

16. Are data pipelines monitored and maintained?

No monitoring in place

Manual checks when failures occur

Basic monitoring with email alerts

Comprehensive monitoring with dashboards

Proactive monitoring with auto-remediation

17. Are data SLAs defined and tracked?

No SLAs defined

Informal expectations, no tracking

SLAs defined for critical data processes

Comprehensive SLAs with monthly reporting

Real-time SLA tracking with predictive alerts

18. Is there a formal incident management process for data issues?

No formal process

Ad-hoc email-based issue reporting

Ticketing system with basic triage

Full ITIL-based incident management

Automated incident detection with root cause analysis

🔒 Data Security & Privacy

Assess your data security controls, access management, and privacy compliance

19. How is sensitive data identified and classified?

No classification system

Manual classification, incomplete coverage

Classification for regulated data only

Enterprise-wide classification with policies

Automated discovery and classification with AI

20. Are access controls implemented based on data sensitivity?

Minimal or no access controls

Basic database-level permissions

Role-based access for critical systems

Fine-grained ABAC with regular reviews

Dynamic access with continuous monitoring

21. How is compliance with privacy regulations managed?

No formal privacy program

Reactive compliance efforts

Basic GDPR/CCPA compliance

Comprehensive privacy program with DPO

Privacy by design with automated compliance

🤖 Data Automation & Analytics

Evaluate automation capabilities and analytics maturity for data governance

22. To what extent are data processes automated?

All manual processes

Basic scripting for repetitive tasks

Orchestrated workflows for key processes

End-to-end automation with exception handling

Intelligent automation with ML/AI optimization

23. How mature is your data analytics capability?

Limited to basic reporting

Descriptive analytics with dashboards

Diagnostic analytics with root cause analysis

Predictive analytics with forecasting

Prescriptive analytics with AI-driven recommendations

24. Are data governance metrics tracked and reported?

No metrics or KPIs defined

Ad-hoc metrics collection

Regular reporting on key metrics

Comprehensive KPI dashboard with trends

Real-time metrics with predictive insights

💭 Additional Context

Provide additional information about your organization's data governance challenges and goals

What are your top 3 data governance challenges?

What are your primary data governance goals for the next 12 months?

Organization Size (approximate number of employees)

1-100

101-500

501-1000

1001-5000

5000+

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🎯 Data Governance Maturity Assessment