Map and Transform Data step

Map complex nested JSON objects to a target schema with dynamic field creation and data transformation.

Roles and availability

Available as a Workflow Studio core action. Users with the flow_designer or admin role can add an action to a flow and define configuration details. For AI-assisted field mapping with the Data Mapper component, users require the sn_dm_connected.automap_user role to access auto-mapping capabilities in a flow.

For the auto-mapping ML engine to function, the system user ih_import (used by Integration Hub for API calls) must also be granted the sn_dm_connected.automap_user role. This is typically configured automatically during feature activation. If auto-mapping is not functioning, verify that the ih_import user has this role assigned in your system.

Inputs

Provide a value for each input that your flow needs. To add dynamic values, you can drag pills from the Data panel or select them from the pill picker.

Field	Description
Source Data	Data type: JSON Object. The nested JSON object to map and transform. Drag-and-drop a JSON data pill or use the data pill picker to select the source object. The source can include multiple levels of nested objects and arrays.
Target	Data type: Object. The output structure for the transformed data. Select Add field to display options to create a target field and configure its mapping. The root label of the target defines the name of the output object. For example, to map a nested person object to user fields, set the root label to "user" and add fields for each element. You can map multiple source fields to a single target field to combine or transform data.
Script Field	Data type: Script Objects. Optional custom transformations using JavaScript. Select Add scripted field to create a script object, define its name, select which source fields it uses, and write JavaScript code to perform transformations on those fields.

Using auto-mapping

When using the Data Mapper component within a flow, you can leverage AI-assisted auto-mapping to automatically suggest field mappings based on source and target schemas. Auto-mapping analyzes field names, types, and data patterns to recommend intelligent mappings, reducing manual configuration time. The auto-mapping feature runs under an elevated execution context to verify secure access to required resources.

Running auto-mapping

Click the Automap button (marked with a sparkle icon) to generate field mapping suggestions. The button is enabled only when both source and target fields are present.

You can choose to auto-map:

• All matching fields in the target table: Auto-map every field based on source field analysis
• All unmapped fields in the target selection: Auto-map only target fields that don't currently have mappings

Clearing mappings

You can remove mappings in bulk using options in the target section overflow menu:

• Clear all fields: Removes every mapping from the target table. Use this to start fresh with a clean slate.
• Clear unmapped fields: Removes only the fields that don't currently have mappings. Use this for cleanup after accepting some suggestions and rejecting others.

A confirmation dialog appears before executing any bulk clear operation to prevent accidental data loss. Review the dialog carefully before confirming.

Outputs

These outputs appear in the Data panel. You can use them as inputs elsewhere in your flow.

Field	Description
Target Object	Data type: Object. The transformed JSON object containing all mapped target fields with values from the source data and scripted field transformations. The object is named based on the target root label you configured.
Step Status	Data type: Object. Status information about the step execution, including success or failure indicators and error messages if any errors occurred during the transformation.

Example: Transform and flatten user data with field combinations

In this example, a flow receives nested user data with a mix of simple and complex fields. The Map and Transform Data step transforms and combines source fields to create a simplified target structure.

The source data is a user object with the following structure:

• user (object)
• name (string)
• age (string)
• email (object)
  • address (string)
  • type (string)

The step configuration sets the target root label to "Root" and creates target fields that include field combinations:

• name_age (combines name and age fields into a single target field)
• test (also combines name and age fields for a different purpose)
• name_name (maps the name field)
• age (maps the age field)
• email (nested object containing address and type fields)

The mapped source fields column shows the data pills for each mapping. For example, the name_age field displays data pills showing both "name" and "age" selections. The transformation shows actual mapped values in real-time.

After the action runs, the Root output object contains all transformed fields with values combined and mapped. The Step Status output provides execution details. The transformed user data is ready for use in subsequent actions such as creating or updating user records.