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What are the common methods for detecting anomalies in business data?
Common methods include threshold rules, comparison with the same period in the past, statistical limits based on standard deviation or median absolute deviation, forecast-based checks that flag actuals far outside the predicted range, and machine-learning methods like isolation forests. Start simple and add complexity only if needed.
Forecast-based detection is especially practical: fit a model that accounts for seasonality and holidays, then flag points where the residual is unusually large. This avoids false alarms from normal patterns.
Tips:
- Define what counts as unusual in business terms and in money.
- Tune alert sensitivity against past known incidents.
- Group related alerts so teams are not flooded.
- Record whether each alert was real, so the system can be tuned.
Only alert on things someone can act on.
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