Data Quality and Anomaly Detection Questions

Focuses on identifying, diagnosing, and preventing data issues that produce misleading or incorrect metrics. Topics include spotting duplicates, missing values, schema drift, logical inconsistencies, extreme outliers caused by instrumentation bugs, data latency and pipeline failures, and reconciliation differences between sources. Covers validation strategies such as data tests, checksums, row counts, data contracts, invariants, and automated alerting for quality metrics like completeness, accuracy, and timeliness. Also addresses investigation workflows to determine whether anomalies are data problems versus true business signals, documenting remediation steps, and collaborating with engineering and product teams to fix upstream causes.