{"id":11058,"date":"2023-02-15T16:57:28","date_gmt":"2023-02-15T16:57:28","guid":{"rendered":"https:\/\/www.genrocket.com\/blog\/?p=11058"},"modified":"2026-05-05T15:19:09","modified_gmt":"2026-05-05T15:19:09","slug":"how-to-ensure-data-validity-when-testing-with-synthetic-data","status":"publish","type":"post","link":"https:\/\/www.genrocket.com\/blog\/how-to-ensure-data-validity-when-testing-with-synthetic-data\/","title":{"rendered":"How to Ensure Data Validity When Testing with Synthetic Data"},"content":{"rendered":"

Data validity is essential when testing the accuracy of business logic designed into information systems that process complex transactions. Without guaranteed data validity, software testing<\/a> succumbs to that age-old dilemma – garbage in, garbage out \u2013 and puts software quality at risk.<\/p>\n

Increasingly, dev and test teams are turning to synthetic data to accelerate test cycles and maximize coverage with expanded data variety. However, synthetic data generation is still a new and different approach when compared to masked production data, and so questions about the validity of synthetic data<\/a> have emerged.<\/p>\n

Does generating synthetic data compromise the validity of test data when compared with real data copied from a production database?<\/em><\/strong><\/p>\n

The simple answer is no. Since GenRocket allows the generation of pattern data, permutation data, data by percentage, data with specific dates in the past, present, and future, data by rules, conditions, calculations, and many more options, GenRocket\u2019s Test Data Automation<\/a> platform provides better data validity than production data and with infinitely greater data variety, volume, and velocity. In essence, GenRocket provides the data needed to fully test the business logic of a complex transaction flow.<\/p>\n

Here’s how it works. Data validity is achieved by blending generated synthetic data with important production data values we\u2019ll call Reference Data. In most transaction flows, there are key fields that define the relationships between multiple data domains and serve as reference data for connecting the dots between various data elements.<\/strong><\/em> For example, an ecommerce application brings together product, vendor, customer, and payment domains when a customer makes shopping cart purchase from one or more vendors with a desired method of payment.<\/p>\n

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For each transaction, an order number serves as reference data<\/em> that links to key fields in the customer, vendor, product, and payment domains that are included in the order. Similarly, the customer ID is reference data<\/em> that links to their purchase history and stored methods of payment. And the vendor ID is reference data<\/em> that links to product SKUs offered by that vendor in the online store.<\/p>\n

GenRocket has the ability to query reference data<\/em> values from a production database and blend them with controlled and conditioned synthetic data to provide the best of both worlds \u2013 data validity and unlimited data variety.<\/strong> Reference data<\/em> retrieved from production ensures the validity of data relationships in a multi-domain a transaction flow. At the same time, controlled and conditioned synthetic data can be generated to replace sensitive (PII\/PHI) data and extend the volume and variety of test data<\/a> far beyond what is available in a production database.<\/p>\n

Here\u2019s an example of Reference Data<\/em> in the healthcare industry.<\/strong> When processing insurance claims, an ICD-10 code is used to represent a medical diagnosis while a CPT code is used to represent a corresponding medical treatment or procedure. The standardized library of ICD-10 and CPT codes used for claims processing are examples of reference data<\/em> because they\u2019re used as the basis for determining the insurance coverage provided to the patient and the payment amount that is made to healthcare providers.<\/p>\n

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During program execution, reference data<\/em> is used to process information, make decisions, and calculate results.<\/strong> Medical codes are used to make billing and payment decisions. Health Plan Identifier (HPID) codes are used to identify the level of coverage for various treatments. And the member ID determines specific benefits for a given patient. When testing transaction flows, reference data<\/em> is essential for testing conditional logic and control flows in the software under test.<\/strong><\/p>\n

When processing insurance claims transactions, it\u2019s important to ensure the proper ICD-10 codes have been used for a given illness or injury and the proper CPT codes have been used for medical services provided. That\u2019s because improper and inaccurate coding is a common source of invalid and rejected claims and\/or overpayments that represent lost revenue for the carrier.<\/strong><\/p>\n

Valid ICD-10 and CPT codes are cross-checked with coverage limits based on the patient\u2019s eligibility and healthcare plan. The reference data<\/em> used for testing this business logic would also include the plan identifier and the member ID.<\/p>\n

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The relationships between medical codes, policy provisions and patient eligibility all rely on the validity of reference data<\/em>. To ensure data validity, GenRocket imports the data model defining these relationships, queries the real production data values, and combines them with synthetic transaction data to maximize test coverage.<\/strong> This allows for testing positive and negative use cases, edge case scenarios, and all combinations and permutations of transaction data values.<\/p>\n

By itself, production data doesn\u2019t provide the data variety that is needed for testing transaction flows with full coverage.<\/strong> Additionally, sensitive data (PII\/PHI) values found in production data must be profiled and masked prior to use in a lower test environment. This can take days and weeks to provision. And for new policy provisions, production data may not even exist, forcing the team to manually create the data varieties they need.<\/p>\n

Synthetic data removes these limitations.<\/strong> GenRocket can retrieve reference data<\/em> from an existing production database and blend it with synthetic test data that is 100% private and secure. Then synthetic data can be generated in the volume, variety, and format required for any category of test.<\/p>\n

Examples of Enumerated Reference Data<\/h3>\n

Reference Data<\/em> that already exists in the production database and used to define relationships between data tables is also known as enumerated data<\/em>. The medical codes in the claims processing example above are good examples. Here are some other examples of reference data<\/em> that is also enumerated data<\/em>.<\/p>\n