Data Quality and Health Checks

In the previous chapters, we have meticulously designed and built the core components of our experimentation platform. We have a robust statistical foundation, a fair and stable diversion engine, and a reliable data pipeline for collecting and processing results. However, all of this work rests on a fragile assumption: that the underlying data is correct and the experiment is running as designed. This assumption is frequently violated.