Data governance & trust

Data governance is effective when it is visible in daily work, not when it remains a collection of policies and responsibility charts. This service starts with critical data and decisions where poor quality, unclear access, or conflicting definitions create cost and risk. We examine sources, consumers, current ownership, control points, and semantic disagreements to establish a limited and executable scope.

For each domain, we define a business owner, steward, quality expectations, access level, and issue-resolution path. The business glossary and data catalogue are written for the people who use them and remain connected to real systems, reports, and data products. A quality control is not merely a rule: it needs a threshold, alert, accountable responder, and evidence that the problem was resolved. Privacy and access controls follow genuine need, least privilege, and auditable change.

Typical outputs include an ownership and stewardship model, glossary and catalogue, quality rules, access matrix, sensitivity classification, and risk framework. The first implementation covers a small set of priority data so that the operating process can be tested before it expands. Success is not measured by the number of documents produced. It is reflected in less ambiguity, faster detection and correction of defects, decisions tied to a known source, and safer access. This service suits organisations without a trusted source of truth, facing regulatory or privacy pressure, or preparing data for analytics and AI.