Silent Data Errors That Survive Every Cleaning Pipeline

Many organizations incur millions annually due to poor data quality that standard cleaning misses. Four subtle data bugs—entity duplication, contextually invalid values, future-leaking timestamps, and denominator issues—evade typical checks and cause downstream errors. These issues highlight gaps beyond syntax checks, requiring semantic and pragmatic validation tailored to business context.