To normalize purchased product names (e.g., converting casing to proper case and replacing null values with a default value) within Salesforce Data Cloud, the best approach is to create a batch data transform that generates a new DLO. Here’s the detailed explanation:

Understanding the Problem :The company wants to ensure that product names in Marketing Cloud Engagement are consistent and properly formatted. The inconsistencies in casing (e.g., "Jacket," "jacket," "shoes," "SHOES") and the presence of null values need to be addressed before activation.

Why Batch Data Transform?

A batch data transform allows you to process large volumes of data in bulk, making it ideal for cleaning and normalizing datasets.

By creating a new DLO, you ensure that the original data remains intact while providing a clean, transformed dataset for downstream use cases like email personalization.

Steps to Implement This Solution :

Step 1: Navigate to the Data Streams section in Salesforce Data Cloud and identify the data stream containing the purchased product names.

Step 2: Create a new batch data transform by selecting the relevant data stream as the source.

Step 3: Use transformation functions to normalize the product names:

Apply the PROPER() function to convert all product names to proper case.

Use the COALESCE() function to replace null values with a default value (e.g., "Unknown Product").

Step 4: Configure the batch data transform to output the results into a new DLO . This ensures that the transformed data is stored separately from the original dataset.

Step 5: Activate the new DLO for use in Marketing Cloud Engagement. Ensure that the email templates pull product names from the transformed DLO instead of the original dataset.

Why Not Other Options?

A. Create a streaming insight with a data action: Streaming insights are designed for real-time processing and are not suitable for bulk transformations like normalizing casing or replacing null values.

B. Use formula fields when ingesting at the data stream level: Formula fields are useful for simple calculations but are limited in scope and cannot handle complex transformations like null value replacement. Additionally, modifying the ingestion process may not be feasible if the data stream is already in use.

C. Create one batch data transform per data stream: This approach is inefficient and redundant. Instead of creating multiple transforms, a single batch transform can handle all the required changes and output a unified, clean dataset.

By creating a batch data transform that generates a new DLO, the company ensures that the product names are consistently formatted and ready for use in personalized emails, improving the overall customer experience.