Data Governance
Data governance defines the policies, roles, and processes that ensure organizational data is accurate, consistent, secure, and used appropriately. It establishes ownership, quality standards, access rules, and lifecycle management across the data estate.
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Intro
Data Governance
Data governance is how an organization sets and enforces priorities for managing and using data. It turns broad intent into assigned authority, policies, decisions, and evidence.
Think of governance as a decision system. It answers four recurring questions:
- Which data matters for this purpose?
- Who may decide how that data is defined, accessed, changed, and retired?
- What rules and quality expectations apply?
- How will the organization detect problems and hold decision-makers accountable?
Governance is not a catalog, committee, or software product. Those can support the decision system. None can replace clear authority and operating processes.
Why governance exists
Data crosses team and system boundaries. A customer identifier may appear in sales, billing, support, and analytics systems. Each team can make a locally sensible choice that creates an enterprise conflict.
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Sources
- https://resources.data.gov/assets/documents/fds-data-governance-playbook.pdf
Supports
- Data governance sets and enforces priorities for managing and using data as a strategic asset
- A governance body establishes policies, procedures, roles, oversight, and resource priorities
- Governance activities include inventory, metadata, policy, issue management, assessment, oversight, and communication
- Governance roles need explicit responsibilities, authority, accountability, and review processes
- Governance begins with a vision tied to organizational goals and stakeholder needs
- Maturity assessments identify capability gaps and investment priorities
- Architecture guidance includes common data elements, metadata, and authoritative systems
- https://strategy.data.gov/principles/
Supports
- Responsible governance includes ethics, security, privacy, confidentiality, appropriate access, and transparency
- Data quality includes relevance, accuracy, objectivity, accessibility, usefulness, understandability, and timeliness
- Accountability includes assigned responsibility, audits, documentation, learning, and corrective change
- Data practices should consider reuse, interoperability, stakeholder feedback, and continuous learning
- https://strategy.data.gov/practices/
Supports
- Governance needs authority, roles, structures, policies, and resources
- Organizations should inventory assets with sufficient metadata for discovery and collaboration
- Documentation should cover quality, utility, and provenance and remain current
- Maturity, value, stakeholder needs, privacy, confidentiality, integrity, access, and preservation guide priorities
- https://www.nist.gov/privacy-framework/privacy-framework
Supports
- The NIST Privacy Framework is a voluntary tool for managing privacy risk through enterprise risk management
- Privacy risk belongs in organizational decisions about data processing and beneficial data use
- The framework is maintained as a living resource through stakeholder engagement
- https://learn.microsoft.com/en-us/purview/unified-catalog
Supports
- Federated governance combines centralized safety, quality, and standards with distributed ownership and maintenance
- Distributing ownership across business domains reduces central bottlenecks and supports participation
- Governance domains align data organization with business context and responsible self-service use
- https://www.w3.org/TR/vocab-dcat-3/
Supports
- A data catalog is a curated collection of metadata about resources
- DCAT provides a standard vocabulary for datasets and data services in catalogs
- Standard catalog metadata supports discovery, aggregation, and interoperability across catalogs
- https://www.w3.org/TR/prov-o/
Supports
- PROV-O represents and exchanges provenance information across systems and contexts
- Provenance can describe entities, activities, agents, generation, derivation, and attribution
- A common provenance model supports traceability across application domains
- https://dama.org/learning-resources/dama-data-management-body-of-knowledge-dmbok/
Supports
- DAMA-DMBOK is a broad framework for data management principles, practices, and functions
- The framework includes governance, ethics, integration, interoperability, and emerging technologies
- The body of knowledge provides a wider professional map beyond a single governance course