Data Governance
Data governance is how an organization sets decision rights, accountability, and rules for data, so it is created, used, shared, and retained properly and safely. Authoritative sources describe it as (a) the exercise of authority and control over the management of data assets, (b) a subset of corporate/IT governance guided by standards, and (c) a framework of decision rights, roles, policies, standards, and metrics.
Public-sector and security guidance adds that governance sets who decides what, and how, and establishes a governing body to create and enforce policies across the enterprise.
Why It Matters
Trustworthy data: Governance raises data quality, consistency, and findability so teams can rely on numbers.
Risk & compliance: Clear rules for privacy, classification, and retention help you meet laws and audits.
Faster decisions: Shared definitions, owners, and standards reduce debate and rework.
Business alignment: Modern guidance stresses outcomes first, governance exists to achieve business goals, not just control data.
Examples
Policies: Data classification, access, retention & deletion, data sharing, AI model training data rules.
Roles: Data Owners set rules; Data Stewards maintain quality and definitions for a domain.
Processes & tools: Issue intake/RACI, data catalog & business glossary, lineage capture, DQ rules & monitors, exception reviews.
Best Practices
Start with outcomes: Tie governance work to revenue, risk, or customer goals (not “governance for governance’s sake”).
Define a framework: Document policies, standards, roles, owners, processes, and metrics; track Key Data Elements and quality thresholds.
Stand up a governance body: Charter a cross-functional council/committee with authority to approve policies and resolve issues.
Assign stewardship: Name Data Stewards for critical domains to own definitions, lineage, and quality checks.
Classify & protect data: Use a classification scheme (public/internal/confidential, etc.), map controls, and monitor access.
Measure & iterate: Track DQ KPIs (accuracy, completeness, timeliness), policy adoption, and data issue MTTR; report to executives.
Distinguish from data management: Governance sets the rules; data management executes them (pipelines, storage, models). Keep oversight and execution separate.
Related Terms
Data Management — operational execution guided by governance.
Data Stewardship — roles and practices that implement governance in domains.
Information Governance / IT Governance — broader enterprise oversight; data governance sits within these.
Data Quality / Data Catalog / Metadata Management
FAQs
Q1. Data governance vs. data management, what’s the difference?
Governance defines the rules, roles, and decision rights; management runs the day-to-day collection, storage, and delivery. Think oversight vs. execution.
Q2. Who owns data governance?
A cross-functional data governance body (business + IT + legal/security) sets policy; data owners and stewards apply it in their domains.
Q3. What’s in a governance framework?
Policies & standards, roles & RACI, quality rules/KDEs, metrics, business glossary, lineage, risk & privacy controls, and tools to manage them.
Q4. Is there a standard?
Yes. ISO/IEC 38505-1 gives guidance on the governance of data as part of corporate/IT governance; it covers the current and future use of data.
Q5. How do we keep governance “lightweight”?
Start with a few critical policies and high-value data domains, automate checks in your catalog/ETL, and review quarterly against business outcomes.