Guides · Power BI audit
Power BI audit: what to check before rebuilding your reporting
The report refreshes successfully, but its figures disagree with the source. This guide explains how to scope a Power BI audit, from KPI definitions and data modelling to security and operations.
Start with the decisions your reports must support
Choose a critical process: month-end reporting, margin control or project profitability. Identify the audience, decision frequency and consequences of errors. Agree whether the audit covers the tenant or a single model.
Record definitions, units, recognition dates, owners and reference sources for essential KPIs. If departments define revenue differently, redesigning a report will not resolve the disagreement.
Trace the path from each source to the indicator
Map ERP, Excel and SharePoint inputs, transformations, semantic models and reports. Identify connection owners, scope, schedules, authentication and gateway dependencies. Look for manual steps, file copies and undocumented rules.
Verify input completeness and expected data freshness, not just the last successful refresh. A technically successful refresh can still process an incomplete file.
Review the data model and validate KPIs
Establish table grain, keys and relationships. Separate facts from dimensions in a clear star schema. Examine joins that can multiply amounts and distinct date roles: document, posting and delivery dates.
Compare measures with an agreed reference using identical filters, representative periods and edge cases. Record expected results and explain differences; a reasonable-looking total is not a repeatable test.
Separate report performance from data-loading performance
Measure page loading, measure execution and model refresh. Locate delays in the source, transformation, model or visual. Optimising a convenient component may not improve the user's experience.
Repeat measurements under comparable conditions. Record data scope, filters and test context to distinguish genuine improvements from temporarily lower demand.
Verify access and accountability
Test the needs of a board member, project manager and accountant. Power BI RLS filters data rows; within a workspace it applies to Viewers, not to Admin, Member or Contributor roles. A test performed only as the report author therefore does not validate the audience's security experience.
Identify owners of models, permissions, incidents and business definitions. Assess access to exports and other data locations separately against actual distribution methods.
Turn findings into an achievable plan
Document evidence, business impact, priority, ownership and action for each finding. Separate quick fixes from architecture changes. Define tests and safe deployment without interrupting existing reporting.
Consistent definitions and one improved model may suffice; a shared platform or Microsoft Fabric may also be justified. Technology choices should follow the diagnosis, scale and operating model, not a predetermined conclusion.
What should you prepare for a Power BI audit?
- A list of critical reports, their audiences and the decisions they support.
- Definitions of the main KPIs and their business owners.
- A map of sources, transformations, models and refresh schedules.
- Examples of discrepancies, including periods and applied filters.
- Access roles and test scenarios for real report consumers.
- Evidence of refresh duration, failures and report usage.
- The approach to versioning, testing, deployment and incident response.
- Audit acceptance criteria: a risk map, priorities and an action plan.
Keep the checklist by printing this guide to PDF from your browser.
Questions and answers
Does an audit mean rebuilding every report?
No. First establish the causes and dependencies. The result may be a change to KPIs, a model or permissions without a complete rebuild. The decision should have a clear business and technical rationale.
Does an audit require access to production data?
Access is agreed for each engagement. Some analysis can use metadata and a controlled sample. Validating figures still requires an agreed reference dataset and a secure basis for comparison.
Can an audit cover AI data readiness?
It can assess quality, definitions, lineage and access for a specific AI use case. That does not replace testing the use case itself or guarantee that generated answers will be correct.