Data Model Concepts

Improve the interoperability of data across applications, and map data between your connected data sources and the Data 360 Customer 360 Data Model.

• Data Flow
  Use the power of Data 360 data in and out of Data 360. Data can be ingested and stored in Data 360 or can be connected via metadata and queried without storage.
• Data Federation, Ingestion, and Modeling
  Data federation and data ingestion bring data from external data sources into Data 360. Data modeling is the next important phase to process data in Data 360.
• Customer 360 Data Model
  The Customer 360 Data Model reduces the complexities of integrating data across cloud applications by providing standardized data guidelines. Extend the data model to create data lakes, generate analytics, train machine-learning models, build a single view of the customer, and more.
• Data Objects in Data 360
  When working with Data 360, it’s helpful to know about Data 360 data objects including data lake objects (DLO) and data model objects (DMO).
• Unstructured Data in Data 360
  By bringing unstructured data in Data 360, you can ground your Agentforce agents, generative AI, analytics, and automation use cases with business-specific data that delivers deeper insights for your users and customers.
• Data Types in Data 360
  Data 360 supports these data types.