XDM Getting Started

Start with XDM by connecting your systems, modelling applications, configuring target environments and masking rules. Guided workflows and self-service access help teams request and receive fit-for-purpose test data, then expand into CI/CD automation.

Step 1: Connect your database systems

Set up access to the required database systems, identify the systems containing the relevant test data, and configure a dedicated user with the appropriate permissions to access the data.

Step 1: Connect your database systems

Step 2: Model your applications

The application’s data model is made up of application-specific objects distributed across various database tables. Define for XDM the functional relationships of the associated tables. Typically, this modeling process is incremental, starting with a specific scope and gradually expanding to encompass other parts of the application.

Step 2: Model your applications

Step 3: Configure the application environments

The environment configuration defines the specific environments where the application data is available. A typical application environment includes production, test, and development instances, each with one or several instances. These environments must be registered in XDM and can serve as source or target.

Step 3: Configure the application environments

Step 4: Setup data masking

Identifying objects containing sensitive data and defining how they should be modified is crucial. In XDM, these modifications can be centrally mapped using modification methods, which are reusable modules that describe how to change data. They effectively mask sensitive data of the same type across systems and applications.

Step 4: Setup data masking

Step 5: Set-up the on-demand data shop

To provide users with a test data provisioning self-service via the Data Shop, first create a copy workflow outlining the processing of data and the utilization of the Data Shop.

The Data Shop serves as an initial hub for testers to find and order test cases that meet their requirements. Testers can fill out a web form with parameters defined in previous steps, such as the classification term of the business object to copy, the options to select which parts of the application should be copied, the source and target environments, and other test case-specific settings.

Step 5: Set-up the on-demand data shop

Final Step: Automate the Test Data Provisioning Process

Once set up, XDM enables each tester to independently search for and provide test data without requiring manual intervention from other specialists. Test data provisioning within intricate system landscapes is entirely automated and can be initiated through a single ordering process by an individual.

Final Step: Automate the Test Data Provisioning Process

XDM Getting Started FAQs

Most organizations start with a defined, high-value use case. That might be masking data for a lower environment, generating synthetic data for a new initiative, or automating test data delivery for a pipeline-driven application.

The best first use case is one with visible pain and measurable value. Good candidates include replacing unsafe production copies, reducing refresh delays, or supplying data to unblock development and testing.

That depends on the primary requirement. If realism and production-like behavior matter most, production-derived data is usually the better starting point. If privacy, flexibility, or greenfield development is more important, synthetic data may be the better first step.

The decision usually comes down to realism, sensitivity, and control. If the application depends on existing production behavior, masking may be the better option. If the priority is privacy, flexibility, or generating new scenarios, synthetic data may be the better fit.

A successful rollout typically involves development, QA, DevOps, security, compliance, and data stakeholders. Early alignment helps ensure the solution meets both delivery and governance needs.

Yes. XDM supports a blended strategy, allowing organizations to use protected production-derived data in some workflows and synthetic data in others.