Transformations Reference

Transformations allow you to modify field values as data is transferred from source to target. This is essential for data anonymisation, GDPR compliance, and creating realistic test environments without exposing sensitive information.

Transformation Types

DMM Infinity supports six field-level transformation types:

Hide

Replaces the field value with an empty string. The field exists in the target but contains no data.

• Input: "John Smith"
• Output: ""
• Use case: Remove sensitive text fields entirely (e.g., notes, comments, free-text descriptions)

Static Value

Replaces the field value with a constant you specify. Every record receives the same value.

• Input: "john.smith@company.com"
• Output: "test@example.com" (or any value you configure)
• Use case: Set all email addresses to a test mailbox, or set all phone numbers to a placeholder

Anonymize

Generates a type-aware random value that matches the field's data type. Each record gets a unique random value.

• Input: "John Smith" (string field)
• Output: "Xkp2 Lm8qr" (random string of similar length)
• Input: "john@company.com" (email field)
• Output: "a7k2m@randomized.com" (valid email format)
• Use case: Generate realistic but fake data for testing environments

Scramble

Rearranges characters within the value while preserving the overall pattern and length. Maintains format markers like hyphens, spaces, and special characters.

• Input: "555-0123"
• Output: "312-5050"
• Input: "AB-1234-CD"
• Output: "CD-4312-AB"
• Use case: Preserve the format of reference numbers, phone numbers, or codes while making them unrecognisable

Regexp

Generates a value based on a regular expression pattern you provide. Useful for creating structured random data.

• Pattern: "[A-Z]{2}-[0-9]{4}"
• Output: "KM-7291"
• Use case: Generate realistic reference numbers, postal codes, or identifiers that follow a specific format

Ignore

Excludes the field entirely from the transfer. The field is not written to the target — it retains whatever value it had before (or the default value for new records).

• Use case: Skip computed fields, audit fields (CreatedDate, ModifiedBy), or fields managed by the target application

Summary Table

Choosing the Right Transformation

Use this decision guide to pick the best transformation for your use case:

Applying Transformations in the Pipeline Builder

To add transformations when creating or editing a pipeline:

1. In the pipeline builder, select the Transform action type (Step 1).
2. After selecting entities (Step 3), proceed to the Configure Transformations step.
3. Expand an entity to see its fields listed with their data types.
4. Click the transformation dropdown next to any field.
5. Select the desired transformation type.
6. If the type requires configuration (Static Value or Regexp), enter the value or pattern.
7. Repeat for each field you want to transform.
8. Fields without a selected transformation are copied as-is.