Anonymising Customer Records

Before you begin

Record-level anonymisation is available when all of the following conditions are met:

Requirement	Expected configuration
Update strategy	The Datasource uses Upsert with a primary key.
Ingestion mode	The Datasource uses Worker or Ingester mode.
Identifier	Each customer record has a known primary key value.
Permission	The person or integration initiating the operation has permission to update the Datasource.

This operation is designed for targeted privacy requests. It removes selected records without replacing the complete Datasource.

How record-level anonymisation works

Your privacy process sends one or more primary key values to the Datasource anonymisation operation. Reelevant writes a deletion marker for each identifier into the live Upsert stream. After the markers are processed, the matching records stop appearing in Datasource queries. Other records remain available and unchanged.

Behaviour	Detail
Maximum identifiers	Up to 10,000 primary key values per operation.
Processing	Asynchronous. A short delay may occur before queries stop returning the records.
Query result	Anonymised records are excluded from all Datasource queries.
Datasource structure	The Datasource, its Field Mapping, and unaffected records remain unchanged.

Record-level anonymisation is not available for Append or Override strategies, or for PubSub and Proxy ingestion modes.

Submit an anonymisation request

Set DATASOURCE_ID to the Datasource identifier and ACCESS_TOKEN to an OAuth 2.0 access token. See Authentication for token creation. The ids array contains the primary key values to anonymise.

DATASOURCE_ID="replace-with-datasource-id"
ACCESS_TOKEN="replace-with-access-token"

curl --request POST "https://api.reelevant.com/v2/datasources/${DATASOURCE_ID}/anonymize" \
  --header "Authorization: Bearer ${ACCESS_TOKEN}" \
  --header "Content-Type: application/json" \
  --data '{ "ids": ["C-1042", "C-2088"] }'

A 200 status confirms that Reelevant accepted the request for asynchronous processing. The anonymized field reports the number of submitted identifiers. Requests with an empty ids array or more than 10,000 values return 400.

Worked example

A CRM Datasource uses customer_id as its primary key and Upsert as its update strategy.

Before anonymisation

customer_id	segment	country
C-1042	loyal_customer	FR
C-2088	new_customer	BE

A customer privacy request identifies C-1042. Your privacy process submits this primary key for anonymisation.

After anonymisation

customer_id	segment	country
C-2088	new_customer	BE

Queries no longer return C-1042. The record for C-2088 remains available without modification.

Coordinate deletion across your data systems

Anonymising a record in Reelevant does not remove it from the original source system. It also does not remove copies stored in separately configured analytics destinations. For a complete right-to-erasure process:

Remove or anonymise the record in the source system.
Submit the same primary key to the Reelevant Datasource anonymisation operation.
Apply the required deletion policy to each analytics destination.
Verify that the identifier no longer appears in Datasource queries.
Retain the audit evidence required by your organisation.

A Worker Datasource can import the record again if a later source refresh still contains the same primary key. Update the source system before the next refresh to prevent re-ingestion.

Operational checklist

Check	Why it matters
Confirm the requester’s identity	Prevents unauthorised deletion requests.
Resolve the correct primary key	Ensures the intended customer record is selected.
Split requests above 10,000 identifiers	Keeps each operation within the supported limit.
Check the source system	Prevents the next Worker refresh from restoring the record.
Check analytics destinations	Ensures deletion policies cover separately stored copies.
Query after processing	Confirms that the record is no longer available to Workflows.

For query validation, see Querying Datasources. For an architectural overview, see Data Ingestion.

Getting Started

DataHub Deep Dive

Advanced Workflows

Email Rendering

Transformations

Advanced Analytics

Before you begin

How record-level anonymisation works

Submit an anonymisation request

Worked example

Before anonymisation

After anonymisation

Coordinate deletion across your data systems

Operational checklist

​Before you begin

​How record-level anonymisation works

​Submit an anonymisation request

​Worked example

​Before anonymisation

​After anonymisation

​Coordinate deletion across your data systems

​Operational checklist

Before you begin

How record-level anonymisation works

Submit an anonymisation request

Worked example

Before anonymisation

After anonymisation

Coordinate deletion across your data systems

Operational checklist