Governance, in the context of business intelligence and data analytics, refers to the policies, processes, roles, and controls that ensure data is managed responsibly, consistently, and securely across an organization. While analytics focuses on extracting insights, governance ensures those insights are trustworthy and compliant.

Data governance covers several key areas:

• Ownership: Who is responsible for specific datasets and metrics

• Access control: Who can view, edit, or export data

• Quality standards: How accuracy and completeness are maintained

• Definitions: How metrics and business terms are defined

• Compliance: How regulatory requirements are met

From a BI perspective, governance prevents common problems such as conflicting dashboards, duplicated metrics, and data misuse. Without governance, teams may calculate the same KPI differently, leading to confusion and loss of trust.

Technically, governance is implemented through tools and systems such as:

• Role-based access control (RBAC)

• Row-level and column-level security

• Certified datasets and metrics

• Data catalogs and business glossaries

• Lineage and audit logs

A key challenge is balancing governance with agility. Overly strict governance can slow down analytics, while too little governance leads to chaos. Modern BI platforms aim to embed governance directly into workflows so users can explore data freely within safe boundaries.

Governance is not just an IT responsibility. It requires collaboration between business teams, analytics teams, and leadership. When governance is done well, analytics scales confidently across the organization.