Description

• Detection of extreme, unrealistic, or logically impossible values

• Identification of anomalies across numeric, categorical, and temporal data

• Removal or correction of outliers depending on business rules

• Clear reporting that explains where anomalies appeared and why

• Stabilization of dataset distributions for analytics, ML models, and dashboards

• Continuous monitoring to prevent recurring outlier issues