Anomaly Detection

Anomaly Detection: Identifying Unusual Patterns in Data

What Is Anomaly Detection?

Anomaly detection is the practice of automatically flagging data points or trends that deviate from what is normal. Instead of manually scanning dozens of charts every morning, anomaly detection algorithms monitor key metrics and raise an alert when something looks off.

Common Approaches

These systems compare current values to historical baselines, seasonal patterns, and expected ranges to spot unusual changes. Simple approaches use thresholds or standard deviations; more advanced methods use machine learning models that consider multiple variables and complex patterns.

Why It’s Important in BI

Good anomaly detection reduces noise while catching real issues early so teams can fix bugs, prevent revenue loss, and capitalize on positive surprises faster. Modern BI tools increasingly combine anomaly detection with root-cause hints, pointing analysts toward likely drivers of the change.