To handle duplicate data for customers who are both loan applicants and high net worth individuals, the consultant should ingest the data into two separateData Lake Objects (DLOs)and map them to theIndividualandContact Point EmailData Model Objects (DMOs). Here’s why and how this works:

Understanding the Problem:

Customers may exist in both datasets (loan applicants and high net worth individuals), leading to potential duplication.

To avoid redundancy while maintaining data integrity, the data must be ingested and mapped carefully.

Why Two DLOs?

By ingesting the data into two DLOs, you can maintain separation between the two datasets while still leveraging shared attributes (e.g., email addresses).

Mapping both DLOs to theIndividualandContact Point EmailDMOs ensures that identity resolution can consolidate duplicate records based on shared identifiers like email.

Steps to Implement This Solution:

Step 1:Create two DLOs—one for loan applicants and another for high net worth customers.

Step 2:Map both DLOs to theIndividualDMO to consolidate customer profiles.

Step 3:Map the email fields from both DLOs to theContact Point EmailDMO to enable identity resolution based on email addresses.

Step 4:Configure identity resolution rules to merge duplicate records based on shared attributes like email.

Why Not Other Options?

A. Use a data transform to consolidate the data into one DLO:Consolidating into a single DLO before mapping would lose the distinction between the two datasets and make it harder to manage updates or changes.

C. Ingest the data into two DLOs and then map to two custom DMOs:Creating custom DMOs is unnecessary complexity when the standard Individual and Contact Point Email DMOs can handle this scenario.

D. Ingest the data into one DLO and then map to one custom DMO:Using a single DLO would result in data loss or confusion, as the distinction between loan applicants and high net worth customers would be lost.

By using two DLOs and mapping them to the standard DMOs, the consultant ensures clean data ingestion and effective identity resolution.

The most efficient option to allow for this capability is to create a reusable container block with common criteria. A container block is a segment component that can be reused across multiple segments. A container block can contain any combination of filters, nested segments, and exclusion criteria. A consultant can create a container block with the exclusion criteria that apply to all the segments managed by multiple brand teams, and then add the container block to each segment. This way, the consultant can update the exclusion criteria in one place and have them reflected in all the segments that use the container block.

The other options are not the most efficient options to allow for this capability. Creating, publishing, and deploying a data kit is a way to share data and segments across different data spaces, but it does not allow for updating the exclusion criteria on a monthly basis. Creating a nested segment is a way to combine segments using logical operators, but it does not allow for excluding individuals based on specific criteria. Creating a segment and copying it for each brand is a way to create multiple segments with the same exclusion criteria, but it does not allow for updating the exclusion criteria in one place.

References:

Create a Container Block

Create a Segment in Data Cloud

Create and Publish a Data Kit

Create a Nested Segment

To grant a user permissions to create, manage, and activate segments without allowing them to create reports or manage data sources, the consultant should assign theData Cloud Marketing Specialistpermission set. Here’s why:

Understanding the Role Requirements:

The user needs access to segment creation, management, and activation.

The user should not have permissions to create reports or manage data sources, which are higher-level administrative tasks.

Why Data Cloud Marketing Specialist?

TheData Cloud Marketing Specialistpermission set provides access to segment-related functionalities, including creating, managing, and activating segments.

It excludes permissions for creating reports or managing data sources, aligning perfectly with the stated requirements.

Steps to Assign the Permission Set:

Step 1:Navigate toSetup > Users > Permission Setsin Salesforce.

Step 2:Locate and assign theData Cloud Marketing Specialistpermission set to the user.

Step 3:Verify that the user has the required permissions by testing their access in Data Cloud.

Why Not Other Options?

B. Data Cloud Marketing Manager:This permission set includes broader permissions, such as managing campaigns and audiences, which are not required for this role.

C. Data Cloud Data Aware Specialist:This role focuses on data ingestion and transformation, not segment management.

D. Data Cloud User:This is a basic permission set that provides limited access and does not include segment management capabilities.

By assigning theData Cloud Marketing Specialistpermission set, the consultant ensures that the user has the necessary permissions without overextending their access.