Data check
Data Check safeguards data quality before it flows into other modules. It automatically scans your Google Analytics 4 configuration, checks the completeness of e-commerce events, and flags areas that need attention. This ensures your decisions are based on reliable numbers – not data distorted by system errors.
Quick GA4 health check
Test
“(not set)” Share
E-commerce Item Completeness
Category Mapping
pseudoUser ID Coverage
What do we analyze?
Percentage of sessions/events missing source or campaign ID
Whether item_id, item_category, price, and quantity are sent for 100% of transactions
Consistency of product categories with the store taxonomy
Share of events with a User ID linking multiple devices
Alert when…
> the market average for your industry
< 95% completeness
5% of SKUs classified as “Other/None”
< X% (industry benchmark)
Why is this important?
You can’t see where your traffic comes from – ad budgets may be misattributed
Missing data prevents accurate LTV, basket, and product reports
Distorted category top lists and product recommendations
Inflated user counts and underestimated purchase frequency
Dashboard „Pass / Watch / Fail”
Pass – configuration within norms → green indicator
Watch – minor deviations → orange indicator + recommendation “check when possible”
Fail – critical errors → red alert + list of corrective steps (link to GA4 documentation + code snippet)
Interactive tiles consolidate statuses across: Traffic Sources, E-commerce, Custom Conversions, and User_ID.
Data quality trends over time
Lines show how the share of “(not set)”, missing Item Data, or User ID coverage changes after fixes are implemented.
You can set up email or Slack alerts when any metric exceeds a critical threshold.
One-click fix recommendations
QA checklists – step-by-step testing guide (GA Debug, transaction test, GTM preview).
Industry benchmark links – see how your data quality after fixes compares to the market average.
Business benefits of the module
Trusted analytics – eliminate errors before they distort LTV, attribution, or RFM insights.
Less manual work – automated alerts replace tedious tag audits.
Faster fixes – QA checklists shorten time to resolution.
Data under control – data-quality trend lines track the impact of changes and flag new issues fast.
Book a free consultation
Let’s talk about your challenges 
and how to overcome them 
.