GA4 is not broken. It is biased by defaults nobody on most marketing teams has ever audited. This hub is the operating reference for the cleanup — the architecture argument, the 8-point audit checklist, the FAQ, and the path to a measurement layer your finance partners will actually believe.
None of the issues uncovered by a real GA4 audit are bugs. They are defaults — designed reasonably for the median GA4 user, which is not an enterprise marketing organization spending eight figures a year inside Google. Data-driven attribution that nobody can audit, modeled conversions filling in for consent-mode gaps, enhanced measurement counting scrolls as engagement, cross-domain stitching that quietly over-credits Direct: each one is a sensible default at small scale that becomes a structural measurement bias at enterprise scale.
The fix isn't replacing GA4. It's treating measurement as an ongoing engineering discipline — with a budget, a roadmap, and an executive owner — and starting with an audit that names every place the data has been biased before anyone in leadership saw it. This hub is built around that audit.
Read the full essay: Your GA4 Setup Is Quietly Lying to You — A Teardown →
Each of these is a reasonable default in isolation. Stacked, they compound into a measurement environment where the answer to "is the data trustworthy?" has to be "it depends what you're using it for."
Run this before the next quarterly business review. If the team can't answer all eight items in writing, you're making decisions on a dataset you haven't audited.
Designed to be completed in one focused 90-minute analytics session, with the GA4 admin and the channel lead in the room.
The expanded version of this audit — with the specific GA4 reports, settings paths, and decision thresholds — is included in the 2026 Signal Architecture Framework. Designed to be walked into a quarterly analytics review.
Embedded inside the full Signal Architecture Framework, alongside the Signal Flow Map, the Meridian Readiness Checklist, and the Cost Per Decision Worksheet.
GA4 measurement biases don't just live in the dashboard. They feed Google Ads audiences. They train Performance Max's bidding. They influence the conversion values your team optimizes against. A scroll event being counted as engagement at the GA4 layer becomes a low-quality audience signal at the activation layer. A modeled conversion ratio nobody verified becomes a misallocated budget at the planning layer. The bias compounds, and the compounding shows up in the P&L months after the original setup decision.
The cleanest way to put a dollar value on this is the Cost Per Decision calculator. The decisions made on bad data take just as long, cost just as much, and unwind in the same way — only the direction of the unwind is unpredictable. Cleaning up the GA4 layer is the cheapest decision-quality investment most marketing teams can make in 2026.