You need the Workspace Editor role to complete the procedures on this page, unless otherwise noted at the top of the procedure. However, if you have the Workspace Viewer role, you can open a metric configuration and run a preview of the metric.
After you set up a datasource and prepare its data assets, you can create and configure metrics on them to measure data quality. A metric continuously measures some dimension of data quality, aggregating and recording results at a chosen aggregation interval.
- The volume of data being brought into a table
- The lag of data being loaded into a table
- The percent of null values in a column
- The percent of values that have 10 digits in a string column type
Each data quality metric (including autometrics) measures one dimension of data quality: accuracy, completeness, timeliness, or (if none of these applies), custom. These dimensions provide a way to work with similar metrics across datasources, such as on the Dashboard. They also are available for sorting and filtering lists of metrics and monitors.
You can create your own metrics tailored to your specific data quality concerns.
- In the left pane, select the workspace where you want to create the new metric.
- On the top bar, select the Metrics tab, and then select Create Metric +.
- Under Custom Metric Info, select a metric type. The rest of the process depends on which metric type you select. For the remaining steps, see the subpage that corresponds to that metric type. For summary information about the custom metric types, see the following section.
To proceed, select the subpage for your metric type:
- Aggregation metrics measure changes in the result of an aggregate function that you define.
- Compare aggregate metrics let you compare existing metrics for a source table and a target table.
Before you begin to create a metric to compare aggregate metrics, consider reviewing the source and target data assets and the metrics for both, to ensure that the metrics you plan to compare are comparable and have the same aggregation settings.
- Conformity check metrics measure the validity of data by checking whether values meet a condition you specify.
- Data delay metrics measure delay in the arrival of expected data into data assets. Available for use with tables and schemas.
- Data volume metrics measure deviations from expected data volumes. Available for use with tables and schemas.
- Distribution metrics measure the distribution of values in a specified column.
- Null percent metrics measure the percent of a column's values that are null.
- Row by row metrics let you measure data value differences between a source table and a target table, by picking key fields (to match the rows) and which columns' values you want to compare.
- SQL metrics measure whatever you model with valid SQL, but must include a SELECT statement that meets basic metric query requirements (details available on the subpage).
- In the Explorer tree, select the data asset that the metric is based on.
- On the right, find the chart for the metric you want to edit. Then, in its top-right corner select the three vertical dots, and then select Edit.
- The metric configuration opens at Step 1 (Metric Info). Select the step you want to edit by (1) using the tabs at the top left or (2) selecting the corresponding pencil icon in the left nav.
- Make any changes in the main page, then select the Preview step.
- If desired, preview the metric. Select Save at the top right corner when you're done.
Cancel a preview
Some datasources let you cancel a preview, for example because you noticed that date range was wrong. If cancelling is supported, you'll see a Cancel button.
Updated about 9 hours ago