Data quality often degrades because data disappears. Data, including from important fact tables, may disappear when issues arise at any stage in the data pipeline – from logging to data modeling. However, detecting disappearing data is challenging. In this article, Jeremy Stanley describes why relying on infrastructure and data engineering monitoring is insufficient to address this problem. He discusses how Anomalo ensures that data is available and complete by testing data quality independently from the systems producing it.