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Data Anomaly Detection Basics

Catch weird data early

Detecting data anomalies involves tracking metrics (counts, distributions, nulls) and using rules or statistical models to flag sudden changes, with alerts, owners, and triage steps defined.

Select Signals

Monitor volume, freshness, nulls, ranges, and distribution shifts.

Detect

Use thresholds, seasonality-aware baselines, or models.

Respond

Alert owners; triage root cause; document incidents.

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