---
title: Set Up Anomaly Detection in Collate for Data Quality
slug: /how-to-guides/data-quality-observability/anomaly-detection/setting-up
---

# 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 Collate 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.

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  src="/images/v1.8/how-to-guides/anomaly-detection/set-up-anomaly-detection-1.png"
  alt="Manual Configuration of Tests"
  caption="Manual Configuration of Tests"
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### 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**. Collate 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.

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  src="/images/v1.8/how-to-guides/anomaly-detection/set-up-anomaly-detection-3.png"
  alt="Manual Configuration of Tests"
  caption="Manual Configuration of Tests"
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### 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.

{% image
  src="/images/v1.8/how-to-guides/anomaly-detection/set-up-anomaly-detection-4.png"
  alt="Manual Configuration of Tests"
  caption="Manual Configuration of Tests"
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## 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.