detecting-data-anomalies

Detects unusual data points using automated algorithms; identifies fraud, errors, or deviations.

• Automates anomaly detection in datasets
• Identifies deviations from normal patterns
• Uses plugins to analyze data for outliers
• Supports fraud, security, and quality monitoring

• Applies machine learning algorithms to detect anomalies
• Identifies deviations in financial, network, or production data
• Provides context on unusual data points
• Supports real-time or batch data analysis
• Integrates with alerting and visualization tools
• Requires clean, properly formatted data

1. 1.Ensure data is clean and properly formatted
2. 2.Specify the type of anomaly to detect (e.g., fraud, security threats)
3. 3.Use the skill with relevant data inputs
4. 4.Review detected anomalies and their characteristics
5. 5.Integrate with alerting systems if needed