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What is anomaly detection and when is it useful in business?
Anomaly detection flags data points that differ markedly from normal patterns, such as a sudden drop in orders, an unusual refund, a spike in costs or a sensor reading out of range. It is useful for catching errors, fraud, system failures and emerging problems earlier than manual review would.
Methods range from simple rules (alert if daily sales are more than three standard deviations from the same weekday average) to machine-learning models that learn normal behaviour from many variables.
The hard part is not the algorithm but tuning. Too sensitive and people ignore constant false alarms; too lax and you miss real events. Always account for known patterns such as weekends, holidays and promotions first, otherwise they are flagged as anomalies. Each alert should have an owner and a defined response.
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