Data Governance in the Modern Enterprise

The Importance of Data Governance

As organisations scale their analytics capabilities with Power Platform, data governance becomes critical. Poor governance leads to data quality issues, security risks, compliance violations, and lack of trust in analytics.

Establishing Data Ownership

Assign clear ownership for data domains. Each dataset should have a designated owner responsible for quality, access control, and documentation. Create a RACI matrix defining who is Responsible, Accountable, Consulted, and Informed for data decisions.

Data Quality Standards

Define what "quality" means for your organisation: completeness, accuracy, consistency, timeliness, and validity. Implement automated quality checks in your data pipelines. Use Power Automate to alert data owners when quality thresholds are breached.

Access Control and Security

Implement row-level security in Power BI to ensure users only see data they're authorised to view. Use Azure AD groups for access management. Regularly audit access permissions and remove unnecessary privileges.

Data Classification and Sensitivity

Classify data based on sensitivity: public, internal, confidential, and restricted. Apply appropriate security controls for each classification level. Use Microsoft Information Protection labels to classify and protect sensitive data automatically.

Compliance and Regulatory Requirements

Understand regulations affecting your data: POPIA, GDPR, and industry-specific requirements. Implement controls for data retention, deletion, and user rights. Document your compliance measures and conduct regular audits.

Metadata Management

Create a data catalogue documenting available datasets, their sources, refresh schedules, and business definitions. Good metadata helps users discover and understand data, reducing duplicate work and improving trust.