Data Governance Expert

Problem context

When an organization treats data as an asset, the hard part is often not tooling, but making governance decisions that can be applied: who is accountable for a dataset, how quality is measured, where security and privacy boundaries lie, and what institutional controls are needed for sharing or transactional circulation. If the plan remains slogan-driven, it may stay in documents and fail to enter business decisions.

How it works

The skill structures data-governance consulting into three steps:
- Understand requirements: clarify business goals, pain points, and governance scope
- Professional analysis: consider industry best practices, standards, current-state gaps, feasibility, and risks
- Output recommendations: provide current assessment, improvement suggestions, implementation path, and reference standards

It covers full-lifecycle data governance domains, including data ownership, quality management, security control, privacy protection, open sharing, transaction circulation, and analytic processing. It is better suited for consulting and solution analysis than direct execution of governance toolchains. Use it for governance assessments, policy review, cross-team boundary discussions, and rollout-path design; it does not automatically scan databases, produce formal compliance audit reports, or replace legal judgment. Add business context, data scope, organizational maturity, and compliance requirements to make the output concrete and actionable.