Data Cloud / Admins / Flow

Data Cloud: Data Governance Use Case

By Lucy Mazalon

People will have been wondering about the differences between Salesforce deduplication and Data Cloud profile unification. It’s important to note that Data Cloud doesn’t merge records – the records will still exist in the source system as they were when they were ingested. What Data Cloud is doing is compiling records together to render a ‘golden record’, which can be leveraged in the activation stages.

However, you can use Data Cloud’s powers to influence Salesforce deduplication in a nice feedback loop. First, let’s recap the differences before we outline how they could be brought together.

Recap: Salesforce Duplicate Rules vs. Data Cloud Rules 

  • Salesforce deduplication: Salesforce duplicate and matching rules work together. They are strictly rules-based, and work by object (e.g. you have rules for the contact object, others for the opportunity object, etc.). 
  • Data Cloud profile unification: Data Cloud match rules and reconciliation rules perform both deterministic and probabilistic matching (which caters to the nuances in how people represent themselves in their data). 

Salesforce duplicate rules are intended to clean up obvious duplicates before those duplicates enter Data Cloud. Following Data Cloud’s identity resolution work, you may find that potential duplicate records can be cleaned up on the Salesforce side. Remember, Data Cloud doesn’t merge records, only rendering a unified profile of a set of records.

Bringing Together Duplicate and Reconciliation Rules  

Here’s a data governance use case to consider for Data Cloud. This is not an in-depth tutorial, but instead, is an outline of what you could explore to set up a nice feedback loop between Data Cloud and your Salesforce database, and vice versa. 

  1. You have active deduplication and matching rules working on your Salesforce data to alert and/or block duplicate record creation. Again, these will be set up by object to catch obvious duplicate records. 
  2. The Salesforce data is ingested into Data Cloud (via a connector), and a data stream created to ingest data updated on a set frequency. (Note: A full refresh occurs every 14 days.)
  3. The data is transferred through the data source object, data lake object, and data model objects.
  4. Match rules and reconciliation rules work together to build the ‘unified profile’ of that individual (i.e. the ‘golden record’). 
  5. A Data Action target can send this back to Salesforce (via a Platform Event) based on a Change Data Capture Event, to trigger a Flow that will merge together duplicate records.
READ MORE: 14 Key Salesforce Data Cloud Terms to Know

Summary

While Data Cloud does not merge records, its powers can be used to influence Salesforce deduplication in a feedback loop. This post has hopefully given you an idea of how you can achieve data governance with Salesforce Data Cloud and duplicate rules.

The Author

Lucy Mazalon

Lucy is the Operations Director at Salesforce Ben. She is a 10x certified Marketing Champion and founder of The DRIP.

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