Audit Analytics Complex

Data Quality Audit

Your GA4 numbers don't match Meta. Your CRM says different conversion numbers than Google Ads. Events fire inconsistently across pages. We audit your data quality end-to-end, find every discrepancy, and trace it to its root cause.

10-12 hours

Bad Data Is Worse Than No Data

When your data is clearly broken, people stop trusting it. That's bad. But when your data is subtly wrong off by 15%, miscounting in specific scenarios, attributing to the wrong channel people trust it and make bad decisions. That's worse.

Data quality problems are hard to find because they're usually not one big error. They're dozens of small issues compounding: a conversion event that double-fires on certain browsers, a data layer push that arrives too late on slow connections, a UTM parameter that gets stripped by a redirect, a consent configuration that blocks events it shouldn't.

We audit your data quality across the entire stack from user interaction on the site, through the tag management layer, to the analytics and advertising platforms receiving the data. We compare numbers between systems, identify discrepancies, and trace each one back to its root cause. You get a complete picture of your data quality and a plan to fix it.

Audit Process

Data Source Inventory

We map every system collecting data analytics platforms, ad platforms, CRM, backend systems. We document what each system is supposed to track and how it connects to other systems.

Cross-Platform Comparison

We pull equivalent metrics from multiple systems and compare them. GA4 transactions vs Meta purchases vs CRM orders vs backend database. The deltas reveal where data is being lost, duplicated, or miscategorized.

Event-Level Validation

We test critical events across multiple scenarios different devices, browsers, user paths, consent states. We verify that events fire when they should, with correct parameters, and that data arrives in destination platforms accurately.

Root Cause Analysis

For every discrepancy found, we trace it back to the technical root cause. Timing issues, consent blocking, race conditions, redirect chain problems, deduplication failures we find the source, not just the symptom.

Remediation Plan

Each issue gets a documented fix with estimated impact on data quality. The plan is prioritized by the magnitude of data impact fixing a conversion double-counting comes before fixing a page view discrepancy.

Deliverables

Data Quality Report Comprehensive findings with severity classification and quantified data impact
Cross-Platform Discrepancy Matrix Side-by-side comparison of key metrics across all platforms
Root Cause Documentation Technical explanation of each discrepancy source with evidence
Event Accuracy Report Validation results for every critical event across tested scenarios
Prioritized Remediation Plan Fixes ordered by data impact with estimated improvement per fix

Frequently Asked Questions

Some discrepancy between platforms is normal, right?

Yes. GA4, Meta, and Google Ads will never match exactly due to different attribution models, different counting methodologies, and different data collection mechanisms. We distinguish between expected discrepancies (5-15% is typical) and problematic ones that indicate real data quality issues.

Do you need access to all our platforms?

Ideally, yes read access to analytics, ad platforms, and CRM. The more systems we can compare, the more complete the picture. But we can scope the audit to specific platforms if access is limited.

How is this different from the GDPR audit?

The GDPR audit focuses on whether tracking respects consent. The data quality audit focuses on whether tracking produces accurate data. They're complementary a consent misconfiguration can cause data quality issues, and a data quality audit may reveal consent-related problems.

Can you set up ongoing data quality monitoring?

AssertionHub provides continuous event validation. For cross-platform metric comparison, we can set up automated reporting that flags discrepancies beyond expected thresholds.

Prerequisites

• TMS access (read)
• Analytics platform access (GA4, Adobe, etc.)
• Ad platform access (Meta, Google Ads, etc.)
• Clear KPIs and conversion definitions

Assumptions (for 10-12 hours)

• Existing tracking setup in place
• At least 2 platforms to compare
• Sufficient data volume for meaningful comparison