Salesforce Data Cloud uses two foundational object types to manage data: Data Lake Objects (DLOs) and Data Model Objects (DMOs). Understanding the difference between them is essential for building a unified, actionable customer profile.
In this article, you’ll learn the purpose, structure, and real-world usage of both object types—and when to use each—within Salesforce Data Cloud.
What is a Data Lake Object (DLO)?
A Data Lake Object is a raw storage object that holds the ingested data exactly as it was received from the source. Think of it as the staging layer in the architecture. Data Lake Objects help preserve original fields, formats, and structure for future reference, validation, or transformations.
- Stores unharmonized/raw data
- Used for auditing and re-mapping
- Not directly used for segmentation or identity resolution
What is a Data Model Object (DMO)?
A Data Model Object is a standardized object structure used by Salesforce to harmonize, unify, and activate customer data. DMOs map similar data (like email addresses, mobile numbers, purchase events) from different systems into a common format. This enables powerful identity resolution and segmentation.
- Stores harmonized and processed data
- Mapped via Data Streams
- Used in Unified Profile, Segments, and Calculated Insights
Key Differences: DLO vs DMO
| Feature | Data Lake Object (DLO) | Data Model Object (DMO) |
|---|---|---|
| Purpose | Raw data storage | Harmonized, unified data for activation |
| Editable | No (read-only) | Yes (via mappings) |
| Used in Segmentation | No | Yes |
| Supports Identity Resolution | No | Yes |
| Examples | Raw files from CRM or web click logs | Individual, Contact Point Email, Purchase Event |
How DLO and DMO Work Together
Data typically flows from source systems into DLOs via a Data Stream. Then, using mapping and transformation, it flows into the appropriate DMO. This architecture allows data to be preserved in its raw form while also being prepared for use in segmentation, personalization, and analytics.
Real-Life Use Case
Example: A retail company uploads purchase data from their ecommerce platform to a DLO. This raw data is mapped to a DMO called “Engagement Event” where it is structured and linked to individual customers. Marketing can then create segments like “High-Value Repeat Buyers.”
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Best Practices
- Always validate your DLO before mapping to DMO
- Use standard DMOs unless absolutely necessary to create a custom one
- Keep naming conventions consistent for easier debugging
- Document every mapping for auditing and governance
Frequently Asked Questions (FAQ)
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