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Transcript

In this video, we’ll show you how to label data fields in a schema as identities, including creating identity namespaces as needed and selecting a primary identity. We’ll also show how to verify the identity data that’s been ingested has been captured in the identity graph. In this example, we’ll return to our Luma Loyalty schema, which models the data we’ll be bringing in from the loyalty system of our fictional brand Luma. In this data, loyalty ID is the primary way we identify the records for individual loyalty members. However, any data field that could identify a customer as an individual, for example, email address or phone number can be labeled as an identity to build a more extensive graph. It’s this selection of identities that powers the joining of datasets. You’ll want to be thoughtful about labeling identities however. Potential identities such as email addresses, phone numbers or cookie IDs can sometimes be shared across individuals or recycled over time. For example, if two household members share an email address, an email address is labeled as an identity, then their records would be merged into a single profile on the system. So make sure that that’s the behavior you want before labeling the field as an identity. Also be sure to work with your legal and privacy teams to determine which identities should be collected and use dual labeling to protect the consumers privacy. When you select any field, that’s a string type you’ll see an option on the right hand side to label it as an identity. Once you label a field as an identity, you need to choose a namespace. The namespace is what type of identity this is, where the system of record from which the identity originated. For example, if you have a driver’s license and the system of record for it is the department of motor vehicles. For an email address there isn’t really a system of record. So you’d use the type, which is email. A bunch of standard namespaces are provided by default. If you don’t find the namespace you need, you can create one. We need to create a namespace for loyalty ID. So I’ll go to the identities page and click on create identity namespace. This opens up a widget where I’ll enter a display name, a shorthand code, and specify what type of an identity this is. Which in this case is a cross device ID. Since the loyalty number is something that can be used to identify a person across different devices.

Now that we’ve created our new identity namespace, we can associate this schema field for loyalty ID with this newly created identity namespace.

And note this other option I’m given when I check the identity box, should this identity be considered a primary identity? You can only choose one primary identity per dataset. You can think of it like the primary key in a database table. The primary identity plays a special role in the real-time customer profile. You can use it to both update and book up a profile. On our example, the loyalty ID should be the primary identity, so I’m going to check that box. And once I’m done and go back out to the schema tree view, you’ll see that loyalty ID has a fingerprint icon next to it, which indicates that this is an identity field. I can use the same process to mark any other fields in the schema that can serve as identity as identity fields. And once I’m done, I want to make sure that the scheme is enabled for profile, to tell the system that this data sets should be integrated into the real-time customer profile.

Now that I’ve labeled the identities in my schema and enabled it for profile, I can load some sample data into a corresponding dataset, making sure that the data set is also enabled for profile.

When we add data to a data set, based on the schema, the identity fields are ingested into the identity graph, populating your graph with more and more identities. We demonstrate creating data sets and loading data into them in a separate video. But once we have some data ingested, we can go to the identities tab to look up one of the profiles and confirm that the identities from our schema have been incorporated into the identity graph.

You can see here that we have the loyalty ID and CRM IDs, which were included in this data from the loyalty system. And these identities are also associated with a few other identities, such as email and phone number that were part of an existing data set that was identified by CRM ID.

So this shows us the graph or cluster of identities that can be used to identify this person across different data sets from different sources. To stitch that data together into a single profile view of the customer. And those are the simple steps to label identities, adjust identity data and populate the identity graph. -