Data QAReporting qualityAgency reporting
Data QA for marketing reports: catch bad numbers before clients do
By The MB Data Automation team··5 min read
TL;DR
- →One wrong number in a client report costs more trust than ten right ones build.
- →A short, repeatable QA checklist catches most reporting errors.
- →Make QA a step in the weekly SOP, not an afterthought.
Clients forgive a flat week. They do not forgive numbers that are wrong - or that contradict last week's report. A lightweight QA step before sending is the cheapest insurance for the relationship.
The core QA checks
| Check | What you are confirming |
|---|---|
| Reconciliation | Platform totals match the dashboard totals |
| Joins | Data from different sources lined up on the right keys |
| Date ranges | Every widget uses the same, correct period |
| Completeness | No missing days or dropped sources |
| Anomalies | Big swings are real, not a tracking break |
Make it a step, not a hope
QA only works if it is a defined step in the weekly process with an owner - not something done when there is time. Put it in the reporting SOP, right before send.
QA is also what makes AI safe
The same checks that protect client trust protect AI output: clean, reconciled data is what lets AI summaries be accurate. QA is a prerequisite for the AI-ready stage.
The Pro Kit includes a data QA checklist and rules plus a dashboard audit scorecard, so you can validate data quality systematically before client presentations.