## Enable anomaly detection

URL: https://docs.atlan.com/product/capabilities/governance/data-quality/snowflake/how-tos/enable-anomaly-detection

> Enable ML-based anomaly detection on a Snowflake table to automatically monitor row count and freshness without manual thresholds.

Enable anomaly detection on a Snowflake table to automatically monitor row count and freshness. Once enabled, Snowflake's ML model learns your data's normal patterns and flags deviations—no manual thresholds required.

## Prerequisites

- Data Quality Studio must be [set up for Snowflake](https://docs.atlan.com/llms/governance/data-quality/set-up-snowflake/llms.txt) and [enabled](https://docs.atlan.com/llms/governance/data-quality/enable-data-quality/llms.txt) on the connection.
- The `MANAGE_DMF` stored procedure must include anomaly detection support (the `ANOMALY_DETECTION` parameter). If you set up Snowflake before anomaly detection was available, [upgrade your stored procedure](https://docs.atlan.com/llms/governance/data-quality/upgrade-snowflake/llms.txt) first.
- You need permission to create rules on the table.
- The table must be a Snowflake-connected asset.

## Enable anomaly detection

1. Navigate to the table where you want to enable anomaly detection.

2. Open the **Data Quality** tab.

3. In the summary section, locate the **Anomaly Detection** toggle.

4. Turn the toggle **on**. A confirmation dialog appears explaining that anomaly detection requires approximately two weeks of training before results become available.

5. Click **Confirm** to enable.

Atlan creates two rules automatically:
- **Anomaly Detection - Row Count**: monitors the number of rows in the table
- **Anomaly Detection - Freshness**: monitors how recently data was updated

Both rules appear in your rules list alongside any existing rules.

## Monitor training status

After enabling, the rules enter a **Training** state while Snowflake's ML model collects data and learns your table's patterns.

| What you see | What it means |
|--------------|---------------|
| **Training (~2 weeks)** | The model is learning your data patterns. No results yet. |
| **Active** | Training is complete. Results are flowing and anomalies are being detected. |
| **Error** | Snowflake failed to enable anomaly detection. Toggle off and on again to retry. |

You don't need to take any action during training. The status transitions to **Active** automatically once the first anomaly detection results arrive from Snowflake.

## View anomaly detection results

Once active, anomaly detection results appear in the same places as other DQ rule results:

- **Rules list**: the two anomaly detection rules show pass or fail status with the latest metric value.
- **Rule detail popover**: shows the measured value alongside the ML-predicted **forecast** and **expected range** (upper bound and lower bound). If the actual value falls outside this range, the rule is marked as failed.
- **Alerts**: if you have [alerts configured](https://docs.atlan.com/llms/governance/data-quality/configure-alerts/llms.txt), anomaly detection failures trigger notifications through your configured channels.

For details on each result field—including forecast, upper bound, lower bound, and the is-anomaly flag—see [Anomaly detection results](https://docs.atlan.com/llms/governance/data-quality/anomaly-detection-results/llms.txt).

## Disable anomaly detection

1. Navigate to the table's **Data Quality** tab.

2. Turn the **Anomaly Detection** toggle **off**.

This removes both anomaly detection rules and disables ML-based monitoring on the table's DMFs in Snowflake. Historical results are preserved.

## Need help

If you have questions or need assistance, reach out to Atlan Support by [submitting a support request](https://docs.atlan.com/support/submit-request).

## See also

- [What's anomaly detection](https://docs.atlan.com/llms/governance/data-quality/anomaly-detection/llms.txt): Understand how anomaly detection works and when to use it
- [Anomaly detection results](https://docs.atlan.com/llms/governance/data-quality/anomaly-detection-results/llms.txt): Reference for result fields, status values, and data flow
- [Set up Snowflake for data quality](https://docs.atlan.com/llms/governance/data-quality/set-up-snowflake/llms.txt): Initial Snowflake setup guide
- [Configure alerts](https://docs.atlan.com/llms/governance/data-quality/configure-alerts/llms.txt): Set up notifications for rule failures

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