Data usage labels user guide
This user guide covers steps for working with data usage labels within the Experience Platform user interface. Before using the guide, please see the Data Governance overview for a more robust introduction to the Data Governance framework.
Managing data usage labels at the dataset level
In order to manage data usage labels at the dataset level, you must select an existing dataset or create a new one. After logging into Adobe Experience Platform, select Datasets on the left-navigation to open the Datasets workspace. This page lists all created datasets belonging to your organization, along with useful details related to each dataset.
The next section provides steps for creating a new dataset to apply labels to. If you wish to edit labels for an existing dataset, select the dataset from the list and skip ahead to adding data usage labels to the dataset .
Create a new dataset
To create a new dataset, click Create Dataset in the top-right corner of the Datasets workspace.
The Create Dataset screen appears. From here, click Create Dataset from Schema .
The Select Schema screen appears, which lists all available schemas that you can use for creating a dataset. Click the radio button next to a schema to select it. The Schemas section on the right-hand side displays additional details about the selected schema. Once you have selected a schema, click Next .
The Configure Dataset screen appears. Provide a name (required) and description (optional, but recommended) for your new dataset, then click Finish .
The Dataset Activity page appears, displaying information about the newly created dataset. In this example, the dataset is named "Loyalty Members", therefore the top-navigation shows Datasets > Loyalty Members .
Add data usage labels to the dataset
After creating a new dataset or selecting an existing dataset from the list in the Datasets workspace, click Data Governance to open the Data Governance workspace. The workspace allows you to manage data usage labels at the dataset level and field level.
To edit data usage labels at the dataset level, start by clicking the pencil icon next to the dataset name.
The Edit Governance Labels dialog opens. Within the dialog, check the boxes next to the labels you wish to apply to the dataset. Remember that these labels will be inherited by all fields within the dataset. The Applied Labels header updates as you check each box, showing the labels you have chosen. Once you have selected the desired labels, click Save Changes .
The Data Governance workspace reappears, showing the labels that you have applied at the dataset level. You can also see that the labels are inherited down to each of the fields within the dataset.
Notice that an "x" appears next to the labels at the dataset level, allowing you to remove the labels. The inherited labels beside each field do not have an "x" next to them and appear "greyed out" with no ability to remove or edit. This is because inherited fields are read-only , meaning they cannot be removed at the field level.
The Show Inherited Labels toggle is on by default, which allows you to see any labels inherited down from the dataset to its fields. Switching the toggle off hides any inherited labels within the dataset.
Managing data usage labels at the dataset field level
Continuing the workflow for adding and editing data usage labels at the dataset level , you can also manage field-level labels within the Data Governance workspace for that dataset.
To apply data usage labels to an individual field, select the checkbox next to the field name, then click Edit Governance Labels .
The Edit Governance Labels dialog appears. The dialog displays headers showing selected fields, applied labels, and inherited labels. Notice that the inherited labels (C2 and C5) are greyed out in the dialog. They are read-only labels inherited from the dataset level and are therefore only editable at the dataset level.
Select field-level labels by clicking the checkbox next to each label you wish to use. As you select labels, the Applied Labels header updates to show labels applied to the fields shown in the Selected Fields header. Once you have finished selecting field-level labels, click Save Changes .
The Data Governance workspace reappears, which now displays the selected field-level label(s) in the row next to the field name. Notice that the field-level label has an "x" next to it, allowing you to remove the label.
You can repeat these steps to continue adding and editing field-level labels for additional fields, including selecting multiple fields to apply field-level labels simultaneously.
It is important to remember that inheritance moves from the top-level down only (dataset → fields), meaning that labels applied at the field level are not propagated to other fields or datasets.
Managing custom labels
You can create your own custom usage labels within the Policies workspace in the Experience Platform UI. Click Policies in the left-navigation, then click Labels to view a list of existing labels. From here, click Create label .
The Create label dialog appears. From here, provide the following information for the new label:
- Identifier : A unique identifier for the label. This value is used for lookup purposes and should therefore be short and concise.
- Name : A friendly display name for the label.
- Description : (Optional) A description for the label to provide further context.
When finished, click Create .
The dialog closes, and the newly created custom label appears in the list under the Labels tab.
The label can now be selected under Custom Labels when editing usage labels for datasets and fields, or when creating data usage policies.
Now that you have added data usage labels at the dataset and field level, you can begin to ingest data into Experience Platform. To learn more, start by reading the data ingestion documentation .
You can also now define data usage policies based on the labels you have applied. For more information, see the data usage policies overview .
The following video is intended to support your understanding of Data Governance, and outlines how to apply labels to a dataset and individual fields.