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Artificially Generating Structured Test Data

Artificially Generating Structured Test Data

Introducing an innovative solution for businesses looking to upgrade their systems and ensure hassle-free data migration. Our whitepaper explores the use of Generative AI to artificially generate structured test data, allowing for maximum testing coverage and avoiding data privacy challenges that is usually experienced in a data migration. With Generative AI, we can maintain and retain intrinsic patterns from legacy systems in synthetic test data, covering all possible scenarios and boundary conditions.

The Generative Adversarial Network (GAN) model is a specialized class of AI that is designed to generate new data that is similar to existing data. This makes GAN the perfect tool for generating synthetic test data that resembles real-world data, ensuring accurate testing of the new advanced system. Say goodbye to tedious and time-consuming migration processes and hello to seamless, scalable, and cost-effective solutions.

Learn more in our whitepaper on how Generative AI is changing the game for data migration.