In this exercise, the goal is to onboard external data like CRM Data in Platform.
For this you need 1000 sample lines of CRM Data.
Open the Mockaroo Template by going to https://www.mockaroo.com/12674210.
On the template, you’ll notice the following fields:
All these fields have been defined to produce data that is compatible with Platform.
To generate your CSV-file, click the Download Data button which will give you a CSV-file with 1000 lines of demo-data.
Open your CSV-file in Microsoft Excel to visualize its contents.
With your CSV-file ready, you can proceed with mapping it against XDM.
Open Adobe Experience Platform and go to Datasets.
Before you continue, you need to select a sandbox. The sandbox to select is named
--module2sandbox--. You can do this by clicking the text Production Prod in the blue line on top of your screen.
From the list of available sandboxes, select the sandbox that matches the number you’ve been assigned.
After selecting the appropriate sandbox, you’ll see the screen change and now you’re in your dedicated sandbox.
In Adobe Experience Platform, click on Datasets in the menu on the left side of your screen.
You’re going to use a shared dataset based in this enablement. The shared dataset has been created already and is called Demo System - Profile Dataset for CRM (Global v1.1).
Open the dataset Demo System - Profile Dataset for CRM (Global v1.1).
On the overview screen, you can see 3 main pieces of information.
First of all, the Dataset Activity dashboard shows the total number of CRM records in the dataset and the ingested batches and their status
Second, by scrolling down on the page you can check when batches of data were ingested, how many records were onboarded and also, whether or not the batch was successfully onboarded. The Batch ID is the identifier for a specific batch job, and the Batch ID is important as it can be used for troubleshooting why a specific batch was not successfully onboarded.
Lastly, the Dataset Info tab shows important information like the Dataset ID (again, important from a troubleshooting perspective), the Dataset’s Name and whether the dataset was enabled for Profile.
The most important setting here is the link between the dataset and the Schema. The Schema defines what data can be ingested and how that data should look like.
In this case, we’re using the Demo System - Profile Schema for CRM (Global v1.1), which is mapped against the class of Profile and has implemented extensions, also called field groups.
By clicking on the name of the schema, you’re taken to the Schema overview were you can see all the fields that have been activated for this schema.
Every schema needs to have a custom, primary descriptor defined. In the case of our CRM dataset, the schema has defined that the field crmId should be the primary identifier. If you want to create a schema and link it to the Real-time Customer Profile, you need to define a custom Field Group that refers to your primary descriptor.
In the above screenshot, you can see that our descriptor is located in
--aepTenantId--.identification.core.crmId, which is set as the Primary Identifier, linked to the namespace of Demo System - CRMID.
Every schema and as such, every dataset that should be used in the Real-time Customer Profile should have one Primary identifier. This Primary Identifier is the identifier user by the brand for a customer in that dataset. In the case of a CRM dataset it might be the email-address or the CRM ID, in the case of a Call Center dataset it might be the mobile number of a customer.
It is best practice to create a separate, specific schema for every dataset and to set the descriptor for every dataset specifically to match how the current solutions used by the brand operate.
The goal of this is to onboard CRM data in Platform. All the data that is ingested in Platform should be mapped against the specific XDM Schema. What you currently have is a CSV dataset with 1000 lines on the one side, and a dataset that is linked to a schema on the other side. To load that CSV file in that dataset, a mapping needs to take place. To facilitate this mapping exercise, we have Workflows available in Adobe Experience Platform.
The workflow that we’ll use here, is the workflow named Map CSV to XDM Schema in the Data Ingestion menu.
Click the Map CSV to XDM Schema button.
Click Launch to start the process.
On the next screen, you need to select a dataset to ingest your file in. You have the choice between selecting an already existing dataset or creating a new one. For this exercise, we’ll reuse an existing one: please select Demo System - Profile Dataset for CRM (Global v1.1) as indicated below and leave the other settings set to default.
Click Next to go to the next step.
Drag & Drop your CSV-file or click Browse and navigate on your computer to your desktop and select your CSV-file.
After selecting your CSV-file it will upload immediately and you will see a preview of your file within seconds.
Click Next to go to the next step. It can take a few seconds while the file is processed completely.
It’s now time to map your CSV Column Headers with an XDM-property in your Demo System - Profile Dataset for CRM.
Adobe Experience Platform has already done some guessing for you, by trying to link the Source Attributes with the Target Schema Fields.
For the Schema Mappings, Adobe Experience Platform has tried to link fields together already. However, not all proposals of mapping are correct. You now need to Accept target fields one-by-one.
The Source Schema field birthDate should be linked to the target field person.birthDate. Check the checkbox.
The Source Schema field city should be linked to the target field homeAddress.city. Check the checkbox.
The Source Schema field country should be linked to the target field homeAddress.country. Check the checkbox.
The Source Schema field country_code should be linked to the target field homeAddress.countryCode. Check the checkbox.
The Source Schema field email should be linked to the target field
--aepTenantId--.identification.core.crmId. Hover over the line email and click the + icon.
You’ll then see this:
Navigate to and select the field
--aepTenantId--.identification.core.crmId. Click Save.
You’ll then see this:
The Source Schema field first_name should be linked to the target field person.name.firstName. Check the checkbox.
The Source Schema field gender should be linked to the target field person.gender. Check the checkbox.
The Source Schema field home_latitude should be linked to the target field homeAddress._schema.latitude. Check the checkbox.
The Source Schema field home_longitude should be linked to the target field homeAddress._schema.longitude. Check the checkbox.
The Source Schema field id should be linked to the target field _id. Hover over the line id and click the + icon.
You’ll then see this:
Navigate to and select the field _id. Click Save.
You’ll then see this:
The Source Schema field last_name should be linked to the target field person.name.lastName. Check the checkbox.
You should now have this:
Click the Finish button to finish the workflow.
After clicking Finish, you’ll then see the Dataflow overview, and after a couple of minutes you can refresh your screen to see if your workflow completed successfully. Click your Target dataset name.
You’ll then see the dataset where your ingestion has processed.
On the dataset, you’ll see a Batch ID that has been ingested just now, with 1000 records ingested and a status of Success.
Click on the Preview Dataset- button to get a quick view of a small sample of the dataset to ensure that the loaded data is correct.
Once data is loaded, you can define the correct data governance approach for our dataset.
Now that your customer data is ingested, you need to make sure that this dataset is properly governed for usage and export control. Click on the Data Governance tab and observe that you can set three types of restrictions: Contractual, Identity, and Sensitive Data.
You can find more info on the different labels and how they will be enforced in the future through the policy framework on this link: https://www.adobe.io/apis/experienceplatform/home/dule/duleservices.html
Let’s restrict identity data for the entire dataset. Hover over your dataset name, and click the Pencil icon to edit the settings.
Go to Identity Data and you’ll see that the I2 option is checked - this will assume that all pieces of information in this dataset are at least indirectly identifiable to the person.
Click Save Changes and observe that I2 is now set for all data fields in the dataset.
You can also set these flags for individual data fields - for example, the firstName field is likely to be classified as an I1 level for directly identifiable information.
Select the field firstName by checking the checkbox and click on Edit Governance Labels in the upper right corner of your screen.
Go to Identity Data and you’ll see that the I2 option is already checked (inherited from the dataset). The field firstName also has a field-specific configuration and is set as I1 - Directly Identifiable Data.
With this, you’ve now successfully ingested and classified CRM Data in Adobe Experience Platform.
Next Step: Summary and benefits