That’s a Valid Question – Ensuring Data Validity with GenRocket Synthetic Test Data

Developers and testers need guaranteed data validity, or else they face the age-old dilemma of ‘garbage in, garbage out.’ Because synthetic test data is a new and different approach than using masked production data for testing, questions arise as to whether synthetic test data offers the same integrity, accuracy, and quality. Added to this is the unfortunate choice by some vendors who label synthetic data ‘fake data’ and it’s no wonder there’s a misperception about using synthetic test data to either augment or replace production data .

GenRocket Offers Guaranteed Data Validity

With GenRocket, there is no tradeoff between the accuracy of production data and the superior volume, variety, and formatting capabilities of synthetic data generation. With GenRocket, dev and test teams can have superior data validity along with greater control over the volume, variety and output format.

The GenRocket Architecture Ensures Data Integrity, Accuracy, and Quality

The GenRocket platform is architected to ensure data validity in terms of the referential integrity, accuracy, and quality of synthetic data designed and generated by every Test Data Case. GenRocket holds the only US patent for generating synthetic data with referential integrity. And its data modeling and design capabilities allow real production data values to be blended with controlled synthetic data values to simulate realistic and accurate transaction flows for testing any complex use case.

But What Do We Mean By “Data Validity”?

Let’s unpack the GenRocket definition of data validity for a minute by exploring an important concept: reference data.

Reference data is a data value that’s related to other data values to form relationships between tables in a relational database for accurate and efficient data retrieval and manipulation. Reference data is commonly used by applications that need to store and retrieve large amounts of transaction data, such as e-commerce systems, financial systems, and insurance claims processing systems. Examples of reference data include ICD-10 and CPT codes, both of which are used in the healthcare industry and must correspond exactly to a medical diagnosis (ICD-10) or treatment (CP). Such codes are reference data.

In workflow testing, reference data such as these codes are used to test conditional logic and control flows in the software under test. The relationship among medical codes, policy provisions, and patient eligibility all rely on the validity of the reference data.

This is an area in which the GenRocket platform excels. To ensure data validity, GenRocket imports the data model defining these relationships, queries the production data values that represent Reference Data, and combines them with synthetically generated transaction data to maximize coverage. This allows for testing positive and negative use cases, edge case scenarios, and all combinations and permutations of transaction data values.

Why Not Use Masked Production Data for Reference Data?

Masked production data doesn’t offer the full range of data values needed conduct extensive transaction flow testing with full coverage. GenRocket synthetic test data eliminates this limitation with ease.

Additionally, reference data can also be generated dynamically by an application during a transaction processing sequence. A good example of dynamic reference data is the transaction flow that must be tested when processing an insurance policy application. Developers and testers using GenRocket can create a Test Data Case containing Scenarios for generating each reference data value and storing it in a mapping table referenced during the transaction workflow. Reference data is retrieved by the Scenarios and combined with controlled synthetic data variations of policy data.

GenRocket synthetic test data provides testers and developers with the assurance of data validity in the volume, variety, and format required for full and thorough test coverage.

Understanding Data Validity and GenRocket Synthetic Test Data

To read the complete article on data validity, including full examples from the healthcare and insurance industries, check out our latest blog article.