GenRocket provides synthetic test data for all major Commercial Off-the-Shelf (COTS) systems including but not limited to Salesforce, Guidewire, Duck Creek, SAP, Workday, Murex, Oracle ERP/HCM etc. GenRocket enables organizations to design, manage and secure test data for any COTS application effectively.
The platform offers many flexible data formats for working with COTS applications, ensuring organizations can create and maintain 100% secure test data across their entire application portfolio with referential integrity. These two methods are Web Services Integration and CSV Export with File Masking.
Web Services Integration
GenRocket integrates directly with COTS applications through their published API interfaces, supporting both modern REST and legacy SOAP web services. This approach enables real-time synthetic data generation and validation while maintaining referential integrity through a metadata-driven architecture.
In this method, GenRocket’s Design-Driven Data methodology is used. This involves importing a data model, configuring the use of intelligent data generators to produce the volume and variety of data required for any and all test cases, and executing a Test Data Case from within those test cases to feed synthetic data to the application under test via REST or SOAP API.
With the use of GenRocket’s Design-Driven Data approach an endless variety of automated test scenarios can be supported, including positive and negative testing, edge cases, and complex data patterns or permutations, all without accessing sensitive production data.
It’s important to note that you do not need to model an entire system’s database structure to begin generating test data. GenRocket allows you to selectively model only the specific domains and tables needed for their specific testing scenarios.
This targeted approach allows teams to:
- Start small by modeling only the domains required for specific test cases
- Gradually expand the model as additional testing needs arise
- Maintain relationships and referential integrity between selected domains
- Generate synthetic test data for specific subsystems without having to understand the entire COTS application structure
For example, when testing an order processing workflow, teams might only need to model customer, inventory, and invoicing domains — rather than the thousands of tables that might exist in the full COTS system. This focused approach significantly reduces implementation complexity while still maintaining data integrity across the modeled domains.
CSV Export with File Masking
The second method of COTS test data generation is to export data from the source data environment as a CSV file and mask the sensitive data using GenRocket’s file masking capabilities. With GenRocket, sensitive data is replaced with controlled and conditioned synthetic data as opposed to being anonymized algorithmically. This results in 100% data privacy combined with control over data variety. This method allows organizations to:
- Export data from their COTS environment
- Apply synthetic data masking to replace any PII and PHI
- Reload data at high speed into testing environments
GenRocket’s file masking capabilities provide superior security compared to traditional anonymization approaches, as sensitive data is replaced with synthetic values rather than using potentially reversible algorithms. The platform’s high-performance bulk load receiver can then process thousands of masked records simultaneously, significantly reducing data provisioning time.
Unmatched Security & Compliance
Working with COTS systems often involves handling sensitive information such as PII (Personally Identifiable Information) and PHI (Protected Health Information). GenRocket can eliminate security risks by generating synthetic test data from metadata models rather than production data, ensuring compliance with GDPR, HIPAA, CCPA, and other privacy regulations. It’s file masking capabilities also provide superior security for sensitive data in that PII and PHI are synthetically replaced as opposed to anonymized by a potentially reversible algorithm.
Enterprise-Level Scalability & Efficiency
The platform delivers enterprise scalability through powerful performance features and advanced management tools. A key component is a self-service interface that provides secure, role-based access to test data scenarios. Teams can quickly create or modify test data cases, reducing bottlenecks and accelerating testing cycles while enabling global collaboration.
A distributed library of test data cases with synchronized project control, built-in versioning, and categorization helps manage reusable test data projects across the enterprise. Comprehensive analytics offer full visibility into platform usage and ROI, helping organizations meet compliance requirements and optimize system utilization.
On-demand synthetic data generation eliminates the need for multiple masked production data copies, cutting storage costs while supporting multiple environments. Seamless integration with CI/CD pipelines ensures complete test coverage and removes data-related delays in testing.
This enterprise-grade approach allows organizations to scale testing operations efficiently while maintaining control, security, and cost-effectiveness.