Data Governance: Integrating Salesforce Deduplication and Data Cloud

In the realm of data governance and data management, there has often been curiosity surrounding the distinctions between Salesforce deduplication and the Data Cloud’s profile unification capabilities. It’s crucial to clarify that Data Cloud doesn’t merge records; instead, it assembles them into what can be described as a ‘golden record.’ This golden record is a consolidated data representation, preserving the original records within the source system.

However, an intriguing opportunity exists to harness Data Cloud’s capabilities to enhance Salesforce deduplication in a symbiotic relationship. 

Before delving into how these two aspects can complement each other, let’s revisit their fundamental differences.

Salesforce Deduplication

This process relies on Salesforce duplicate and matching rules, which are rule-based and operate at the object level. For instance, you can establish rules for contacts, opportunities, and other objects. Salesforce deduplication primarily focuses on preventing and rectifying obvious duplicates before they enter the Data Cloud ecosystem.

Data Cloud Profile Unification

In contrast, Data Cloud employs match and reconciliation rules, utilizing deterministic and probabilistic matching techniques. This approach accommodates the subtle variations in how individuals represent their data. The goal is to construct a unified profile, often called the ‘golden record,’ which provides a comprehensive view of a set of records. Importantly, Data Cloud does not merge the original records; it consolidates them into a harmonized representation.

Also Read –  Salesforce Data Cloud Consultant Credential

A Data Governance Use Case

Let’s explore a practical data governance use case demonstrating how Data Cloud can seamlessly integrate with your Salesforce database, creating a valuable feedback loop that benefits both systems.

1. Salesforce Deduplication Rules: Initiate deduplication and matching rules within Salesforce to proactively identify and prevent the creation of apparent duplicate records. These rules are configured at the object level to catch duplicates specific to each entity.
2. Data Ingestion into Data Cloud: Your Salesforce data is ingested into Data Cloud through a connector, establishing a data stream that regularly updates the information. It’s worth noting that a complete data refresh takes place every 14 days to ensure data accuracy.
3. Data Flow Within Data Cloud: As the data enters the Data Cloud, it traverses through various stages, including the data source object, data lake object, and data model objects.
4. Match and Reconciliation Rules: Data Cloud’s match rules and reconciliation rules come into play, collaborating to construct the unified profile or ‘golden record’ of each individual within your dataset. These rules apply deterministic and probabilistic matching techniques to achieve a comprehensive representation.
5. Data Action Target: Implement a Data Action target that can transmit the unified profiles to Salesforce through a Platform Event. A Change Data Capture Event can trigger this transmission. Subsequently, a Flow within Salesforce is initiated to merge any potential duplicate records identified in the unified profiles.

Conclusion

As organizations navigate the data-driven landscape, maintaining data integrity, preventing duplicates, and creating golden records become a competitive advantage. By harnessing the capabilities of both Salesforce and Data Cloud, organizations can take a significant step toward achieving Salesforce data at scale. 

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