Data governance is the framework of policies, processes, roles, and controls that ensure data is managed properly across an organization. Its purpose is to make data trustworthy, secure, compliant, and consistently defined so it can be used confidently for analytics and decision-making.

In BI and analytics, governance answers questions like:

• Who owns this data?

• Can this data be trusted?

• Who is allowed to access it?

• How is this metric defined?

• What happens if the data changes?

Data governance typically covers:

• Data ownership and stewardship

• Access control and permissions

• Data quality standards

• Metric definitions

• Compliance requirements (GDPR, SOC 2, HIPAA, etc.)

• Auditability and change management

Without governance, analytics systems tend to drift. Different teams define the same metrics differently, dashboards show conflicting numbers, and trust in data breaks down.

Technically, data governance is implemented through:

• Role-based access control (RBAC)

• Column- and row-level security

• Certified datasets and metrics

• Data catalogs and glossaries

• Lineage tracking

• Approval workflows

A common misconception is that governance slows teams down. In reality, good governance speeds analytics by reducing confusion, rework, and disputes over numbers.

Modern BI platforms increasingly embed governance directly into analytics workflows, making it easier to balance control with self-service.

Data governance is not a one-time project. It evolves as data, teams, and regulations change. When done well, it creates a foundation of trust that allows analytics and AI to scale safely.