Rapid and reliable Data Masking

Enhance data as you mask it.

Reliably identify sensitive information across databases and files, applying integrated masking and generation to produce the data needed for rigorous, continuous testing.

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Take the time and complexity out of masking - add the quality in

With Test Data Automation, data masking is:
  • Simple, defining Excel-like functions in easy-to-configure spreadsheets and executing out-of-the-box workflows.

  • Rapid and repeatable, reusing high-speed, parallelized masks from a central catalogue and refreshing data as soon as QA needs it.

  • Compliant, scanning files and databases reliably for sensitive data and maintaining accurate audits of previous masks.

  • Designed for quality, augment data as it is masked, weaving in test data generation functions and model-based design.

  • Built for complex systems, applying rules from central dictionaries to mask consistently across databases and files.

  • Agile, making data masks available on demand and resolving them dynamically during test execution and CI/CD processes.

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improve test data as you mask it


Turn compliance into an opportunity for more rigorous testing. Synthetically augment data as it’s masked, maximising coverage and detecting more defects first time round.


With Test Data Automation:
Slow and manual data masking lead to:

Control Over Compliance Risks:

Data scanning identifies problematic data from across databases and files. Personal and commercially sensitive information is audited and removed before it reaches QA.

Unknown PII in Test Environments:

Unmasked or residual sensitive information is moved to less secure QA environments. This risks legislative non-compliance, massive fines, and huge brand damage.

Agile Test Data:

High speed, parallelized masking routines are rapidly re-usable from a central catalogue. Ops teams focus on fulfilling new requests and testers enjoy on tap data refreshes.

Data Provisioning Bottlenecks:

Masking complex data consistently from multiple sources is slow and cumbersome. Provisioning wastes precious QA time as test teams wait for lengthy data refreshes.

Referentially Intact Test Data:

Re-usable masking rules in a shared dictionary are applied across files and databases, retaining the referential integrity needed for rapid and reliable test execution.

Time-Consuming Test Failures:

Inconsistent masking undermines end-to-end and integrated testing. Test data destabilises test automation frameworks and creates false positives/negatives.

Bugs Found First Time Round:

Synthetic test data generation fills gaps in data as it’s masked. Easy-to-use functions and model-based data design create data to test every scenario rapidly and rigorously.

Test Data Undermines Quality:

Masked production data contains just a fraction of scenarios needed in QA. Low coverage testing in turn exposes productions systems to costly and time-consuming bugs.

“Just in Time” Data for Every Test:

Automated “Find and Makes” hunt through data, generating new combinations needed in testing. Integrated masking secures data as it is allocated on-the-fly to test frameworks or CI/CD pipelines.

Testers Hunt for Data:

Validation checks data matches before tests run. Test Data Automation allocates and creates new to guarantee that data reflects test criteria exactly. Test execution is stable and accurate

Intuitive, Repeatable Data Masking:

Defining masking routines for complex data is as quick and simple as combining comprehensive Excel-like functions. Existing masks are easily repeatable from a central catalogue.

Masking is as Complex as the Data:

Manual masking, scripting, and in-house technology is slow and error prone. It has a high learning curve and requires deep understanding of complex data models.

Seamless Subset/Masks:

Seamless integration with high-speed data subsetting creates referentially intact data subsets. The complete data sets take less time to mask and enable shorter, more manageable test runs.

Unwieldy Data Undermines Speed:

Slow and manual masking must be performed on massive copies of complicated production data. Executing the large copies takes ages and produces cumbersome run results


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Discover quality and compliant data, at the speed demanded by QA.

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