> ## Documentation Index > Fetch the complete documentation index at: https://docs.open-metadata.org/llms.txt > Use this file to discover all available pages before exploring further. # Set Up Anomaly Detection in OpenMetadata for Data Quality # Steps to Set Up Anomaly Detection ### 1. Create a Test from the UI * First, select the dataset and navigate to the **Tests** section in the OpenMetadata UI. * Define your test parameters. You can either create a **static test** (e.g., "no null values" or "data should not exceed a certain range") or configure **dynamic assertions** to let the system learn from the data. Manual Configuration of Tests Manual Configuration of Tests ### 2. Configure Manual Tests * For more controlled monitoring, set up **manual thresholds** (e.g., sales should not exceed a maximum value of 100). This provides specific control over data validation criteria. ### 3. Enable Dynamic Assertions * For data that naturally fluctuates or evolves, enable **dynamic assertions**. OpenMetadata will start profiling your data regularly to learn its normal behavior. * Over time (e.g., five weeks), the system will establish expected value ranges and detect any deviations from these patterns. Manual Configuration of Tests ### 4. Monitor Incidents * After configuring tests, monitor for any **incidents** triggered by anomalies detected in the system. * Investigate significant spikes, drops, or unusual behaviors in the data, which may indicate system errors, backend failures, or unexpected external factors. Manual Configuration of Tests ## Best Practices * **Use Static Assertions for Simple Rules**: For basic data validation, such as preventing null values or enforcing a minimum threshold, static assertions are effective and straightforward to configure. * **Leverage Dynamic Assertions for Evolving Data**: When dealing with datasets that naturally fluctuate (e.g., sales or user activity), dynamic assertions can save time and ensure incidents are only triggered when significant anomalies occur. * **Regularly Review Incidents**: Stay on top of incidents generated by anomaly detection to promptly identify and address data quality issues. * **Combine Manual and Dynamic Methods**: For datasets with well-defined boundaries and evolving characteristics, combining manual thresholds and dynamic assertions provides comprehensive anomaly detection coverage. This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.