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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.
- anomaly detection
- data quality
- alerts
- thresholds
- drift
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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