What does anomaly detection in analytics identify?

Anomaly detection in analytics is a powerful technique used to identify unusual patterns or outliers in data. This process involves analyzing data sets to find instances that deviate significantly from the expected behavior, which can indicate potential issues, fraud, or other noteworthy occurrences.

For instance, if a business typically sees steady sales growth, a sudden drop or spike in sales could be flagged as an anomaly. By identifying these outliers, organizations can investigate further to understand the underlying causes, whether they be operational failures, changes in market conditions, or external factors impacting performance. This capability is crucial for maintaining the health of business operations and making informed decisions based on accurate data interpretations.

Thus, anomaly detection focuses on detecting those significant deviations rather than providing insights into general trends, operational norms, or average metrics, which are other aspects of data analytics.