Data Governance Policies

Posted byMehari Lemlem

Data Governance:

Data governance establishes clear rules for data quality, security, access, and lifecycle management. It ensures data is accurate, compliant, and trustworthy by enforcing standards, ownership, lineage, and controls across the organization.

Data Governance Policy:

A data governance policy is a formal set of rules and guidelines that defines how an organization manages, protects, accesses, and uses its data. It establishes standards for data quality, security, privacy, ownership, lifecycle, and compliance, ensuring data is reliable, consistent, and handled responsibly across all systems and teams.

Steps to Establish and Enforce Data Governance Policies:

Step 1: Define Governance Objectives and Regulatory Requirements

  • Identify which regulations apply
    • Global & International Regulations:
      • ISO/IEC 27001 – Information security management
      • ISO/IEC 38505 – Governance of data
      • ISO/IEC 27701 – Privacy information management
      • OECD Privacy Guidelines
      • Basel III – Financial data and risk reporting (banking)
      • PCI-DSS – Payment card data security
      • ITIL – Data/IT service governance framework
    • United States Regulations:
      • HIPAA / HITECH – Health data privacy and security
      • SOX (Sarbanes-Oxley) – Financial data accuracy and controls
      • GLBA (Gramm-Leach-Bliley Act) – Protection of customer financial data
      • FISMA – Federal data security standards
      • FedRAMP – Cloud security controls for federal data
    • European Union Regulations:
      • GDPR – General Data Protection Regulation
      • DORA – Digital Operational Resilience Act (financial sector)
      • NIS2 Directive – Network & information systems security
      • EIDAS – Digital identity and trust services
  • Determine governance goals: data integrity, lineage, access control, retention, audit trails, data quality.

Outcome: A regulatory requirements matrix mapping each requirement to controls you must implement.

Step 2: Establish a Data Governance Framework

  • Choose a governance model (e.g., centralized, federated, or hybrid).
  • Define governance roles:
    • Data Owners (business accountability)
    • Data Stewards (quality + documentation)
    • Data Custodians (IT + engineering)
    • Governance Council (cross-functional oversight)
  • Set formal processes for approvals, standards, and escalations.

Outcome: A governance operating model that is repeatable and auditable.

Step 3: Implement Data Classification and Metadata Standards

  • Define data categories (PII, PHI, Confidential, Restricted, Public).
  • Create metadata standards covering:
    • schema naming
    • data definitions
    • business rules
    • quality thresholds
  • Use tools like:
    • Microsoft Purview (Fabric/ADF-native lineage & classification)
    • Azure Data Catalog
    • Collibra / Alation (if in enterprise environment)

Outcome: All sensitive data is discoverable and classified for Regulation controls.

Step 4. Design Technical Controls in Azure/Fabric

Access & Security

  • Implement RBAC and ABAC in Azure AD.
  • Enforce least privilege with managed identities.
  • Use column-level and row-level security for PII (GDPR).

Data Protection

  • Encryption:
    • At rest (Azure-managed keys or BYOK/HSM for SOX)
    • In transit (TLS 1.2+)
  • Masking:
    • Dynamic Data Masking (SQL/Fabric Warehouse)
    • Tokenization (for PCI/PHI)

Monitoring

  • Enable:
    • Azure Monitor
    • Defender for Cloud
    • Purview Data Loss Prevention
    • Audit logs for DML/DDL changes (SOX requirement)

Outcome: Technical enforcement is embedded in the data pipeline and platform.