What is data governance?

Data governance sets the responsibilities, policies and standards for how an organization manages and uses its data. It helps people understand who owns information, what it means, who may access it and how its quality is maintained.

For a distributor, governance makes shared reporting more dependable. Sales, purchasing and finance need agreed definitions before they can confidently compare customer activity, available stock and business performance. A dashboard cannot resolve a disagreement about the meaning of its inputs by itself.

A practical approach identifies data owners, agrees important definitions and sets rules for quality and access. It also defines how issues are reported, how changes are approved and how teams check that standards remain useful.

Ownership means someone is accountable for a data area, such as customer or product information. Access rules determine which users and applications can see or use it. Quality rules help teams detect missing, inconsistent or unsuitable values. These responsibilities support each other; no single tool or permission setting is the whole governance program.

Illustrative example: purchasing and finance report different values for available stock. They establish that one report includes committed orders and the other does not. The teams agree the definition required for replenishment, assign an owner and document which sources and update times support the measure.

They then restrict sensitive cost data to the appropriate roles and create a process for reporting exceptions. The outcome is a clearer rule and responsibility for the information, not simply another report. This approach can be applied to customer groups, product classifications and financial account mappings too.

Data quality describes whether data is suitable for its intended use. Governance establishes the policies and responsibilities that help teams define, maintain and improve that quality. Role-based access control is a particular mechanism for controlling access; it is one part of the wider picture.

A clean dataset can still be poorly governed if nobody owns its definitions or knows who should access it. Equally, a governance policy needs working processes and controls if it is to improve day-to-day decisions.

An AI feature still needs appropriate data, business context and access controls. Users should be able to understand the information available to it and check the resulting answer or action.

The Phocas Trust Layer describes how permissions, business context and security controls support AI within Phocas. It explains that adding AI does not create a route around a user’s existing data access. These controls support AI use within Phocas. Each organization still needs its own data ownership, policies and review processes.