Test Data Automation - 2025 Pricing, Features, Reviews, & Alternatives

1. Avoid the testing and development time spent finding, making, and waiting for data. Remove the bottlenecks and constraints associated with test data, making data for every test available on-the-fly and as-a-service.

2. Find bugs in-sprint and fix them while its quick and affordable. Automatically find all the data combinations needed for parallelised and automated testing, with integrated data generation to ensure that data does not undermine testing rigour. Find bugs earlier, while they are quicker and cheaper to resolve, reducing defect remediation costs and delivering quality software at speed.

3. Achieve stable and rigorous automated testing. Ensure that every parallelised test comes equipped with up-to-date, accurate data that links consistently across integrated systems. Avoid the time-consuming test failures caused by inaccurate or constrained data, delivering data in parallel to data-hungry automation frameworks.

4. Support compliance with evolving data privacy legislation. Minimise the spread of personally and commercially sensitive information across less-secure non-production environments, combining data masking and generation to create anonymous data for testing and development.

5. Reduce infrastructure and operations costs, testing and developing with subsetted and virtualised data sets. The lightweight virtual data requires a fraction of the storage when compared to physical data, while automated database orchestration replaces the time ops teams often spend maintaining parallel environments.