\nThe organization deployed GenRocket’s Quality Engineering Platform (QEP) to replace manual provisioning with Design-Driven Synthetic Data. The result:<\/strong> roughly three weeks of manual effort (~120 person-hours) recovered per team, per project \u2014 across both the Oracle HCM and Payload API workstreams, regression and performance coverage expanded, full HIPAA alignment, and a centralized TDM program now scaling across the enterprise.<\/p>\n
![\"GenRocket](\"https:\/\/www.genrocket.com\/wp-content\/uploads\/2026\/05\/GenRocket-TDM.svg\")
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Context<\/h2>\n
The client is one of the world’s largest diagnostic testing services providers, touching tens of millions of patients and working with half the physicians and hospitals in the United States. Its testing ecosystem spans HR systems, transactional APIs, patient registration, billing, and analytics \u2014 all of which must be validated continuously without compromising PHI. <\/p>\n
The Quality Engineering team set three clear goals for modernizing its test data program:<\/p>\n
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- Accelerate testing<\/strong> through automation instead of manual spreadsheet-based data creation.<\/li>\n
- Maximize coverage<\/strong> with greater data variety across functional, regression, and performance tests.<\/li>\n
- Reduce cost and boost team efficiency<\/strong> by removing the production-data dependency and its lengthy approval cycles.<\/li>\n<\/ul>\n
Quality Engineering Challenges<\/h2>\n
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- Manual Data Creation:<\/strong> Testers built data in Excel, one row at a time \u2014 capping volume, introducing errors, and slowing every cycle.<\/li>\n
- Restricted Production Data:<\/strong> Access controls were in place, but using production data still carried HIPAA exposure and multi-week approval cycles.<\/li>\n
- Limited Lower-Environment Data:<\/strong> Sparse, stale data meant performance and integration scenarios couldn’t be executed realistically.<\/li>\n
- Inconsistent TDM Practices:<\/strong> No standard way to model, design, or reuse test data across teams. The result \u2014 thin coverage, delayed releases, and defects leaking to production.<\/li>\n<\/ul>\n
The GenRocket Solution<\/h2>\n
The client adopted GenRocket’s Quality Engineering Platform (QEP) \u2014 built on Design-Driven Synthetic Data, which generates data from metadata and design logic rather than copying and masking production records. QEP was deployed across two foundational use cases, both following GenRocket’s Model \u2192 Design \u2192 Deploy \u2192 Manage methodology.<\/p>\n
Use Case 1 \u2014 Oracle HCM Performance Testing<\/strong><\/p>\n
The HR team needed realistic employee data spanning departments, reporting hierarchies, and employment types (FT, PT, Temp, Contractor) to execute performance testing on Oracle HCM. Data had to be delivered in .DAT format for HDL (HCM Data Loader) ingestion.<\/p>\n
QEP imported the HCM schema and hierarchy metadata from the secure lower environment, designed test data cases for employment-type distributions with positive and negative data \u2014 and generated referentially intact .DAT files for HDL load. Executable test data cases were stored in the test data case project library for reuse across HR testers.<\/p>\n
Use Case 2 \u2014 Payload API Transactional Testing<\/strong><\/p>\n
The API team needed high-volume transactional data \u2014 logs, activities, and payloads \u2014 to validate XAPI, PAPI, and SAPI request\/response flows in staging. Data had to land in PostgreSQL with PK\/FK integrity across log, activities, and payload tables.<\/p>\n
The PostgreSQL schema and inter-table relationships were imported into GenRocket. Then test data cases were designed with encoded statistical profiles mirroring real transaction patterns. Also test data cases with data permutations across transaction IDs and response payloads for happy-path and failure-mode logic were designed as well. Test data could be generated \u201con demand\u201d and loaded into PostgreSQL via scripted integration \u2014 preserving PK\/FK relationships across all three tables. The same test data case design allows outputs of JSON or XML for API-level testing, and the test data cases are stored in the shared test data case project library for self-service access and re-use.<\/p>\n
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\n Challenge<\/strong><\/th>\n GenRocket Solution<\/strong><\/th>\n<\/tr>\n<\/thead>\n\n \n Manual data creation in Excel<\/td>\n Automated, design-driven generation at enterprise volume<\/td>\n<\/tr>\n \n Production data exposure risk (HIPAA)<\/td>\n 100% synthetic data \u2014 zero access to production or PHI<\/td>\n<\/tr>\n \n Limited data variety in lower environments<\/td>\n Test data cases for positive and negative data<\/td>\n<\/tr>\n \n Referential integrity gaps across tables<\/td>\n PK\/FK relationships preserved natively across systems<\/td>\n<\/tr>\n \n Inconsistent TDM practices across teams<\/td>\n Test Data Case Project Library + Center of Excellence model<\/td>\n<\/tr>\n \n Multiple output formats (.DAT, SQL, JSON, XML)<\/td>\n Single design, multi-format deployment on demand<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n Results & Benefits<\/h2>\n
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- Time Savings:<\/strong> ~3 weeks of manual effort (~120 hours) recovered per team per project across both use cases.<\/li>\n
- Higher Test Coverage:<\/strong> Regression and performance coverage expanded to include scenarios that were previously impossible in lower environments.<\/li>\n
- Absolute Data Privacy:<\/strong> Production data fully removed from testing. With 100% synthetic data, PHI exposure risk in non-production is eliminated \u2014 not just mitigated.<\/li>\n
- Referential Integrity at Scale:<\/strong> PK\/FK relationships across log, activities, and payload tables are maintained automatically.<\/li>\n
- Multi-Format Flexibility:<\/strong> .DAT, SQL, JSON, XML from a single executable design.<\/li>\n
- Environment Refresh On Demand:<\/strong> Minutes instead of weeks \u2014 no more waiting on production extracts.<\/li>\n
- Team Productivity:<\/strong> Engineers moved from building data to validating outcomes.<\/li>\n<\/ul>\n
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\n Metric<\/strong><\/th>\n Before GenRocket<\/strong><\/th>\n After GenRocket<\/strong><\/th>\n<\/tr>\n<\/thead>\n\n \n Data provisioning<\/td>\n Manual Excel spreadsheets<\/td>\n Automated, design-driven, on demand<\/td>\n<\/tr>\n \n Effort per project<\/td>\n ~3 weeks manual work<\/td>\n Minutes to hours<\/td>\n<\/tr>\n \n Production data dependency<\/td>\n Required (HIPAA risk)<\/td>\n Eliminated<\/td>\n<\/tr>\n \n Performance data volume<\/td>\n Limited<\/td>\n Millions to billions of rows<\/td>\n<\/tr>\n \n Reusability across teams<\/td>\n None<\/td>\n Project Library shared across TDM<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n Compliance, Scalability & Enterprise Expansion<\/h2>\n
Because GenRocket is metadata-driven and never accesses or stores customer data, the client’s HIPAA exposure in non-production dropped to zero. Standardized TDM processes and executable designs in the Test Data Case Project Library create a clean audit trail, and referential integrity, bias mitigation, and controlled variety are enforced at design time \u2014 not patched in afterwards.<\/p>\n
The QEP platform scaled cleanly from thousands to millions of records via the \u201cPartition Engine\u201d and multi-threaded architecture. G-Questionnaire lets non-expert testers modify and repurpose Test Data Cases via a self-service model. <\/p>\n
GenRocket\u2019s \u201cTDM Bridge\u201d provides a phased path from customers who are familiar with using masked production data to move to a synthetic data-first model without disrupting existing workflows.<\/p>\n
The early success across Oracle HCM and Payload API has seeded a broader TDM program within this organization. Multiple new projects are now being onboarded, with a Center of Excellence model supporting distributed teams \u2014 each contributing reusable designs that compound benefit as adoption grows.<\/p>\n
What’s Next<\/h2>\n
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- CI\/CD Integration:<\/strong> Embed synthetic data generation into release pipelines for on-demand provisioning on every build.<\/li>\n
- Unstructured Data:<\/strong> Use GenRocket’s Unstructured Data Accelerator<\/a> (UDA) to generate synthetic PDFs, images, and clinical documents for end-to-end workflow testing.<\/li>\n
- AI \/ ML Model Testing:<\/strong> Generate bias-controlled, edge-case-rich training and validation datasets without touching PHI.<\/li>\n
- Ephemeral Environments:<\/strong> Replace static test data storage with real-time generation tied to short-lived environments.<\/li>\n<\/ul>\n
Conclusion<\/h2>\n
- Unstructured Data:<\/strong> Use GenRocket’s Unstructured Data Accelerator<\/a> (UDA) to generate synthetic PDFs, images, and clinical documents for end-to-end workflow testing.<\/li>\n
- Higher Test Coverage:<\/strong> Regression and performance coverage expanded to include scenarios that were previously impossible in lower environments.<\/li>\n
- Restricted Production Data:<\/strong> Access controls were in place, but using production data still carried HIPAA exposure and multi-week approval cycles.<\/li>\n
- Maximize coverage<\/strong> with greater data variety across functional, regression, and performance tests.<\/li>\n