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1. What Is Data Governance?
Data governance is the system of policies, roles, and processes that ensures data is managed as a strategic asset. It answers: Who owns this data? How fresh is it? Who can access it? What happens when it breaks?
Governance ≠ Bureaucracy
Good governance enables speed. It's not about blocking access: it's about making data trustworthy so teams can self-serve confidently.
Three of those four questions are answered by artefacts a governance programme has to produce anyway. Ownership and expectations get written down as data contracts between producing and consuming teams. Freshness and breakage are answered by data quality checks that run on a schedule instead of on complaint. And "what breaks if this changes" is an impact-analysis question, which is what data lineage exists to answer. In a domain-owned organisation the same policy set gets applied federally rather than centrally, which is the governance pillar of data mesh.
2. Six Pillars of Data Governance
Data Quality
Measure completeness, accuracy, freshness, and consistency. Automate monitoring with tools like Great Expectations or Monte Carlo.
Stewardship & Ownership
Every dataset needs a named owner. Stewards are accountable for quality, documentation, and access policies within their domain.
Metadata Management
Centralize metadata in a catalog. Automate ingestion. Maintain a business glossary so terms like "revenue" mean the same thing everywhere.
Security & Privacy
Role-based access, encryption, PII classification, and audit logs. Essential for GDPR, CCPA, HIPAA, and SOX compliance.
Compliance
Map regulatory requirements to data assets. Automate data retention policies. Maintain lineage for audit trails.
Lifecycle Management
Define how data is created, stored, archived, and deleted. Prevent unbounded storage costs and stale data accumulation.
3. Roles & Responsibilities
| Role | Responsibility | Reports To |
|---|---|---|
| Chief Data Officer | Strategy, budget, executive sponsorship | CEO / CTO |
| Data Governance Lead | Policies, standards, council facilitation | CDO |
| Data Steward | Domain-level quality, documentation, access | Domain Lead |
| Data Owner | Accountable for dataset quality and usage | Business Unit |
| Data Engineer | Implement pipelines, quality checks, lineage | Platform Team |
4. Implementation Roadmap
Quarter 1: Foundation
- Get executive sponsorship and define governance charter
- Identify top 5 critical data domains
- Assign data owners and stewards
- Deploy a data catalog (DataHub, Atlan, or similar)
Quarter 2: Quality & Access
- Implement data quality monitoring for critical datasets
- Define access policies and role-based controls
- Classify PII and sensitive data
- Build lineage for top pipelines
Quarter 3-4: Scale & Automate
- Expand governance to all data domains
- Automate compliance checks in CI/CD
- Launch self-serve data marketplace
- Report governance KPIs to leadership quarterly
5. Measuring Success
Data Quality Score
> 95% across critical datasets
Policy Compliance Rate
> 90% of datasets compliant
Time-to-Access
< 24h for standard data requests
Catalog Adoption
> 70% of analysts use catalog weekly
Data Incidents
50% reduction quarter-over-quarter
Documentation Coverage
> 80% of tables documented
6. FAQ
What is a data governance framework?
What are the key pillars of data governance?
How do you measure data governance success?
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