Absolutely! To get started with freshness and volume tests in Elementary, here are some helpful links and resources: - Freshness Anomalies Test: This test monitors the freshness of your table over time, detecting delays in data updates. You can find configuration details and examples here: [Freshness Anomalies Test Documentation](https://docs.elementary-data.com/data-tests/anomaly-detection-tests/freshness-anomalies). - Volume Anomalies Test: This test monitors the row count of your table over time to detect unexpected drops or spikes. Configuration and usage details are available here: [Volume Anomalies Test Documentation](https://docs.elementary-data.com/data-tests/anomaly-detection-tests/volume-anomalies). - How to Add Elementary Tests: For step-by-step instructions on adding these anomaly detection tests to your dbt project, see: [Add Anomaly Detection Tests](https://docs.elementary-data.com/data-tests/add-elementary-tests). - Best Practices and Use Cases: For recommendations on when and how to use these tests, check out: [Detection and Coverage Best Practices](https://docs.elementary-data.com/best-practices/detection-and-coverage#common-testing-use-cases). If you’re using Elementary Cloud, you also get out-of-the-box automated freshness and volume monitors with zero configuration. Learn more here: [Automated Freshness & Volume Monitors](https://docs.elementary-data.com/features/anomaly-detection/automated-monitors). These resources should help you get started quickly and effectively!

Based on the information in the knowledge sources, here are some possible reasons why the

column_name

in the

metrics_anomaly_score

table is empty and why you might not see the expected results in your report: 1. Volume and Freshness Tests Are Table-Level The

elementary.volume_anomalies

and

elementary.freshness_anomalies

tests are table-level tests, not column-level. For these tests, the metrics are associated with the table as a whole, so the

column_name

field in the metrics tables (including

metrics_anomaly_score

) will be empty or null. This is expected behavior for these types of tests. Only column-level anomaly tests (like

elementary.column_anomalies

) will populate the

column_name

field with a value [see this discussion](https://slack.com/archives/C02CTC89LAX/p1708533386888479). 2. Not Enough Training Data If you see messages like "Not enough data to calculate anomaly score" or if the report is missing expected results, it could be due to insufficient training data. By default, Elementary requires a minimum number of historical data points (typically 14) to calculate anomaly scores. If your table does not have enough historical data in the

CREATED_TIME

column, or if the test hasn't run enough times to collect sufficient data, the anomaly score will not be calculated [see troubleshooting guide](https://docs.elementary-data.com/data-tests/anomaly-detection-tests/Anomaly-troubleshooting-guide). 3. Timestamp Column Issues If the

CREATED_TIME

column has missing, null, or incorrectly formatted values, metrics may not be collected as expected. Ensure that

CREATED_TIME

is populated and in the correct format for all relevant rows [see troubleshooting guide](https://docs.elementary-data.com/data-tests/anomaly-detection-tests/Anomaly-troubleshooting-guide). 4. Test Configuration and Execution Make sure that: - The tests are configured correctly in your YAML. - You are running

dbt test

and the Elementary on-run-end hooks are completing successfully. - The Elementary tables in your warehouse are up to date and compatible with your package version [see this answer](https://slack.com/archives/C02CTC89LAX/p1688995519653729). Summary: For table-level anomaly tests, it is normal for

column_name

to be empty in

metrics_anomaly_score

. If you are not seeing results, check for sufficient training data, correct timestamp column values, and successful test execution. If you want to monitor anomalies at the column level, use

elementary.column_anomalies

or

elementary.all_columns_anomalies

tests. If you have confirmed all the above and still do not see results, please provide more details about your data and test run history for further troubleshooting.