Cleaning and preparing your data is crucial for success when using the Data 360 segmentation and activation capabilities.
Required Editions
| Available in: Lightning Experience |
After ingesting your data into Data 360, use the library functions, operators, and raw data fields to prepare for data mapping.
- Streaming Data Transforms
A streaming data transform reads one record in a source object, reshapes the record data, and writes one or more records to a target object. The source and target objects must be different objects. A streaming data transform runs continuously as a streaming process, picking up new or changed data. - Batch Data Transforms
To transform your data for further usage, such as identity resolution, segmentation, or calculated insights, or to derive insights in Salesforce reports, use a batch data transform. A batch transform is a repeatable series of operations that you can run when data updates. The first time you run a batch data transform, it pulls in data and defines it according to your steps. You can then run a transform manually or set it up to run at scheduled intervals. - Billing Considerations for Data Transforms
Data Transforms use Data 360 to cleanse, enrich, and reshape data from DLOs and DMOs to prepare it for insights, segmentation, and AI features. Use of Data Transforms impacts the consumption of credits in these usage types. - Create a Data Transform Platform Event Flow
Create a platform-triggered flow for data transform event actions. - Create a Data Transform Record Event Flow
Create a record-triggered flow for data transform event actions. With a record-triggered flow, you can update another record, send a notification, or initiate a process. - Normalized and Denormalized Data
Data originates from multiple sources and can be normalized or denormalized. The Data 360 standard data model is normalized, so incoming data must be normalized before it can be mapped to the data model. Because not all source systems provide normalized export options or normalize data, assess your field-level data to establish how to transform and map the source data to the standard data model. - Normalize Denormalized Data Use Case
In this use case, the data comes from a single Marketing Cloud Engagement data extension but must be normalized to map to the Data 360 data model objects (DMO).