Data Cleansing and Preparation

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
  Using data transforms impacts the consumption of credits used for billing for orgs operating Data 360 under a Data 360 license.
• 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).