Define the number and the decision.
Choose an important report and describe the decision it supports, the period it covers and what each measure includes. Agree the unit of analysis, such as one customer, one transaction or one service request, so totals are interpreted consistently. Record exclusions and adjustments in language the report's users understand, with a named owner who can resolve competing definitions.
Trace the path from source to report.
Identify the systems that create each field, the transformations applied and the joins used to connect records. Check how identifiers behave when a record is updated, merged or removed, and how late-arriving information affects a reporting period. Source links and a documented refresh process let a reviewer investigate a change without relying on the memory of the person who built the dashboard.
Check completeness and consistency.
Look for missing required fields, duplicate records, unexpected values and totals that disagree with an approved source or reconciliation. Sample important records as well as aggregate figures, because an apparently reasonable total can conceal a broken join or offsetting errors. Explain which checks stop publication and which create a visible exception for the report owner to review.
Make freshness and uncertainty visible.
Show when data was last refreshed and which period or source is incomplete so readers can judge whether a result is suitable for their decision. Keep material exceptions close to the affected metric and assign an owner to investigate them. For Canadian organizations with multiple regions or languages, align the meaning of labels, dates and reporting periods before combining their information.
Build reporting and AI on the checked dataset.
Once the definitions and checks are agreed, use a shared data layer for dashboards, exports and any agent-assisted reporting workflow. Ask generated explanations to cite the underlying records or metrics and distinguish observed changes from possible causes. Active K Digital can help assess data quality, repair integrations and build reporting that includes refresh, reconciliation and review responsibilities in its operating design.
Your starting checklist.
- Define each measure, period and unit of analysis.
- Trace source fields, transformations and record identifiers.
- Check missing data, duplicates, joins and reconciliations.
- Display refresh timing and material exceptions.
- Assign metric, source and publication review owners.