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Complex data generation in 2 minutes
Automatically analyse large data sets, generating the smallest set of data with every combination and relationship. Synthetic data generation from Test Data Automation shortens delivery cycles, sidesteps compliance risks, and generates rich data for rigorous testing.
Test data bottlenecks, compliance risks, and costly bugs
Testing today requires data that is more complex than ever, reflecting the variations and relationships created by complex systems. Data must link consistently across tables and system architectures, but the relationships are often poorly documented and understood. Unable to make accurate data, organisations often copy production data that is full of sensitive information and risks costly non-compliance. The data sets are additionally large, yet do not contain the positive and negative scenarios needed for rigorous testing. This leads to bloated, costly test runs, which nonetheless allow bugs into production. Copying the complex data manually is furthermore slow, delaying releases as teams wait for data. It further fails to fulfil relationships across data, adding even more delays as tests fail due to inconsistent data.
Synthetic test data shortens release cycles and builds quality earlier
Complex data generation from Test Data Automation automatically creates data that covers every combination needed in testing, generated on demand for rapid and resource-efficient development. The rich data retains every relationship found in large data sets, which are automatically analysed and defined. The analysis further identifies the likelihood of different combinations occurring together, generating data for optimal code and requirements coverage. Data optimisation techniques like all pairs and triples create the smallest data set needed to cover every combination and relationship, creating concise and executable test suites. For data-driven systems with billions of permutations, complex data generation produces concise, complete and compliant data on demand.
With on demand data generation, testers, developers, and CI/CD tooling enjoy the data they need, when and where they need it. Test Data Automation regenerates up-to-date data as systems and requirements change, while dynamic test strategies skew data to focus on critical combinations. Test and development teams no longer wait for slow and manual data provisioning, and nor do they compete for shared data sets. Instead, they enjoy rich data that’s auto-generated on demand. The data fulfils every relationship needed for referential integrity, avoiding test failures caused by data. The realistic, fictitious synthetic data furthermore sidesteps data privacy requirements, developing rapidly without the constraints of slow data provisioning, compliance risks and inaccurate data.
Auto-analyse and generate complex data sets
Watch the two-minute overview of Complex Data Generation from Test Data Automation, to see how:
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Test Data Automation automatically analyses large data sets, identifying relationships in data and assessing the likelihood of combinations occurring together.
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Synthetic data generation applies optimisation techniques like all pairs and triples to create the smallest data set needed for full coverage, saving testing time and resources.
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Test data optimisation shrinks billions of possible data combinations without losing variety, creating rigorous data-driven test suites that can be executed on demand.
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Generating consistent synthetic data removes the need to use production copies in testing and development, supporting compliance with data privacy legislation.
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Test Data Automation regenerates up-to-date test data as systems and requirements change, avoiding test failures caused by inconsistent and out-of-date data.
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Dynamic test strategies focus data generation on high frequency data combinations, enabling optimal code and requirements coverage.
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The complex data generation is quick and easy to set up, providing sample data and specifying criteria to limit generation to particular scenarios.
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