A data health audit should not be a vague strategy document. If it does not tell you where the data breaks and what to fix first, it has missed the point.
The audit should cover
- Core systems and what each one owns.
- Duplicate records and matching risks.
- Metric definitions used by each team.
- Manual exports, spreadsheets, and hidden workflows.
- Freshness requirements for important data.
- Permission and access risks.
- Reports people trust and reports people avoid.
- The first three fixes with commercial value.
What the output should look like
The best output is a map of the data estate with a short list of practical next moves. Not a 70-page document nobody reads. Not a vendor shopping list. A clear view of where trust breaks and what should be repaired first.
The audit should also say what not to build yet. If the source layer is weak, starting with AI or dashboards may create more noise than value.
A good audit turns a vague data problem into a prioritized build plan.
Lucendata runs Data Health Audits for companies that need clarity before committing to a larger data or AI project.