Synthetic Test Data Generation

"Just in time" data for every test.

Test Data Automation generates end-to-end data journeys for complete system testing, built on-the-fly during test automation and CI/CD pipelines.

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Data to deliver quality systems, available on demand

With Test Data Automation, test data generation is:
  • Rigorous, using 200+ out-of-the-box functions to generate all the data needed to find bugs earlier and at less cost to fix.

  • Delay-free, generating rich test data on-the-fly as test cases and automated tests are created or executed.

  • Built for cross-system testing, seamlessly passing values from one publish to another to create consistent data across systems.

  • Fully extensible, generating consistent data into databases, files, messages, mainframe systems, source control systems, and more.

  • Intuitive, auto-generating configuration spreadsheets and filling in the blanks to generate referentially intact data.

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Discover Test Data-as-a-Service

Read our solution brief to learn how you can transform the relationship that your teams and frameworks share with data, shifting from slow and manual data "provisioning" to streaming rich test data in real-time.

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Maximise coverage, minimise risk


Detect bugs when they cost far less time and effort to fix. Synthetically generate compliant data based on test definitions, maximising coverage to find more bugs first time round.

With Test Data Automation:
With traditional test data automation:

Rich test data available for every test:

Rich synthetic test data fills gaps in coverage, applying coverage analysis, 200+ generation functions and flow-based generation to find bugs earlier and at less cost to fix.


Test data leaves systems exposed:

Production data contains a fraction of the combinations needed for rigorous testing, lacking the outliers and unexpected results needed to prevent bugs hitting production

Realistic but fictitious test data minimises risk:

Synthetic data provides all the combinations needed for testing, but with none of the sensitive content. Combining generation seamlessly with masking provides a hybrid approach to compliance.

PII in test environments risks costly non-compliance:

Copies of production contain personally identifiable information and commercially sensitive data, risking costly non-compliance with data privacy legislation, significant brand damage and customer churn.

Complete test data on tap:

“Just in time” generation and allocation find and make data as tests are generated or run. Teams can trigger publishes from an online portal, delivering fully tested software in-sprint.

Test data bottlenecks delay releases:

Test and development teams face lengthy waits for data provisioning, as a central team struggles to fulfil complex data dependencies. They must then through large data sets for the combinations they need.

Rapid and repeatable test data generation:

Test Data Automation models data structures, using fill-in-the-blanks configuration and visual data flows to generate referentially intact data. Each job becomes re-usable in a central catalogue, breaking dependencies on a siloed team.


Data refreshes fall behind rapid releases:

An over-worked data provisioning team struggle to reflect highly complex data relationships and are quickly overwhelmed by repetitive requests. Data becomes increasingly out-dated relative to systems under test, creating test failures.

On demand data journeys test complex systems in-sprint:

Easy-to-use functions, side-by-side configuration and visual data flows publish data into multiple targets at once. This passes variables from one process to the next, generating consistent data journeys for testing.

Inconsistent test data creates frustrating test failures:

Manual test data processes struggle to fulfil relationships within and across data sources. This adds to provisioning delays while inconsistencies across components lead to frustrating test failures.

Parallelised teams and tests:

Test data generation seamlessly integrates with rapid data cloning, providing all the volumes of unique data combinations needed by parallel test teams and tests.

Data constraints prevent agility:

There are never enough data sets to test and develop in parallel, while clashes create time-consuming test failures as multiple tests consume the same test data combination.


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Eliminate test data bottlenecks, execute rigorous tests in-sprint.

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