Data Quality Control

Data quality control is the set of checks run at ingestion and afterwards so that malformed, incomplete or duplicated events never reach a report.

Data quality control usually has three layers: validating each event against its own schema at ingestion, removing duplicates by event identifier, and monitoring volume and freshness against an expected baseline so a sudden drop or a stalled source becomes visible. The key design choice is to quarantine an invalid event with a reason code rather than drop it silently; a silent drop leaves the dashboard looking healthy while part of the data never arrived. The layer missed most often is drift monitoring: a property that gradually stops being sent raises no error and only surfaces months later as an empty report.