Data Validation for Analytics Questions

Covers techniques and practices for ensuring the correctness and reliability of analytical outputs, metrics, and reports. Topics include designing and implementing sanity checks and reconciliations, comparing totals across different calculation methods, validating metrics against known baselines or prior periods, testing edge cases and boundary conditions, and detecting and flagging data quality anomalies such as missing expected data, unexplained spikes or drops, and inconsistent values. Includes methods for designing queries and monitoring checks that surface data quality issues, debugging analytical queries and calculation logic to identify errors and root causes, tracing problems back through data lineage and ingestion pipelines, creating representative test datasets and fixtures, establishing metric definitions and versioning, and automating validation and alerting for metrics in production.