Data Governance

Short answer: Data governance sets the rules for how a company manages its data. It names who decides, who checks and what happens when data is wrong. In practice, it answers questions such as who owns the customer data and who may see salary data. It also settles what counts as an active customer and how long the company keeps records.

Governance covers ownership, definitions, quality standards, access and privacy, retention and how people raise and resolve data issues. It is distinct from data management, which is the day-to-day work of collecting, storing and preparing data. It also differs from data quality, which concerns how fit data is for use. Governance provides the decision-making structure that both data management and data quality rely on. Two roles are common. A data owner is a senior business person accountable for one area of data, such as customers or products. A data steward handles day-to-day definitions and issues in that area. The right level of formality depends on company size, sector and regulation.

A practical start is to pick the few areas of data that matter most, such as customers, products and suppliers, and name an owner for each. The company writes a short set of policies on access, definitions, quality and retention. It sets up a simple log for data issues and a regular review meeting. The team adds governance checks to existing processes, such as approving new software or changing a report definition. That way the checks happen as part of normal work. The owner tracks progress with a few measures, such as the number of open issues or the share of key fields that meet a quality standard.

A company with 120 staff finds that 15 people can export the full customer list, including personal contact details. The sales director becomes owner of customer data and reviews access. The review cuts access to 5 people with a business need. The owner also approves a written definition: an active customer is one that ordered in the last 12 months. A monthly 30-minute review logs 18 data issues in the first quarter and closes 14, leaving 4 open.

Governance affects trust in reports, compliance with data protection law and readiness for AI. Automations and reports are only as trustworthy as the data rules behind them. Clear owners and definitions mean an automation acts on the right records and a report shows the agreed figure. Governance also gives leaders a clear answer to who is accountable when data is wrong or misused. It reduces time lost to arguments over definitions.

Governance can turn into paperwork that nobody follows if it is too heavy for the size of the company. It needs a senior sponsor and time from busy business people, not only from the technology team. Benefits are often indirect and hard to measure. The company should check its policies on personal data against the laws that apply in each market, with legal advice where needed.