The Record monitor detects rows of data that drop out of or are added to a dataset and down-score any observations. To see record findings, select at least one numeric data type column in the Outliers layer, a Key column, and a Date column. If you do not select at least one numeric column, a Key column, and a Date column, Collibra DQ skips the record check activity. Alternatively, you can exclude Outlier findings and detect only Records by selecting a Key column and a Date column but excluding the numeric data type column from your Outlier configuration.
The following table describes the information available for the Record tab on the Findings page.
| Column | Description |
|---|---|
| Observation | The type of record anomaly detected. |
| Status | Allows you to validate or resolve an observation and, when applicable, assign it to a user for further analysis. |
| Profile |
The user account that is assigned to this record finding. When the Status is Assigned, a user profile displays in this column. Note When a record finding is unassigned, the profile column is empty. |
Retrain
Updates the DQ models used for automated data-quality checks. The Retrain process consumes current training data (and optionally labels/config), builds a new model or rule-set, validates it, and publishes a new version that will be used to score incoming data and compute DQ metrics. Use Retrain when data distributions change, new labels/ground truth are available, business rules change, or on a scheduled cadence to prevent model drift.