What is outlier detection and why does it matter?

< 1 min read

Outlier detection is the process of identifying data points that significantly differ from normal patterns.
In one project, we noticed a sudden spike in refund amounts that stood out from historical trends.
Outliers may indicate fraud, operational errors, or exceptional business events.
They can distort averages, KPIs, and forecasting results if not handled properly.
We used statistical techniques like standard deviation and interquartile range (IQR) to detect them.
After validation, we either corrected data errors or treated valid extreme cases separately.
As a BA, I ensure outliers are investigated before drawing conclusions.
Overall, outlier detection improves data accuracy, model reliability, and decision quality.

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