Why Design-Driven Synthetic Data is the Future of Quality Engineering

Many enterprise QA and development teams today face a familiar challenge: lack of reliable, compliant, and scalable test data. The World Quality Report 2024 reveals that test data availability remains one of the top three obstacles limiting the speed and quality of software delivery—even as 72% of teams now leverage automation.

While synthetic data solutions are gaining traction, not all approaches deliver the control and precision needed for modern quality engineering. Statistical replica models and AI-generated synthetic data fall short, introducing data gaps and inconsistencies, compliance risks, and lack of enterprise scalability. These methods require access to sensitive production data and struggle to deliver the volume, variety and format of data needed for comprehensive testing.

GenRocket’s Design-Driven Synthetic Data Generation solves these problems. Unlike other solutions, it puts testers and developers in full control—allowing them to design and generate exactly the data they need, when they need it. No production data is required. Every test case can be supported with valid, secure, relational, and repeatable synthetic data, delivered at scale and integrated directly into CI/CD pipelines.

As enterprises modernize their quality engineering practices, a design-driven approach is proving essential. It eliminates the risks and limitations of other synthetic data technologies—empowering teams to maximize test coverage, data security, and operational efficiency.