Synthetically Augment Data as you Mask It

Delivering Quality and Compliance at Speed

Mask sensitive information before it reaches test environments and augment the data with combinations needed for rigorous testing. Test Data Automation delivers quality and compliant data at the speed required by automated testing frameworks and rapid release cycles. 

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Production Data is not “Gold Copy” Test Data

Today, legislation like GDPR and CCPA make using raw production data riskier than ever, chancing massive fines, customer churn, and brand damage. Data masking is therefore the minimum for most organizations, removing the most obvious sensitive information from data moved to less secure test environments. However, consistently masking data from numerous sources is slow and complex. Often, a central Ops team must work round the clock to fulfil constant requests, slowly delivering a limited number of out-of-date copies. QA in turn waste time waiting for data, while tests fail due to out-of-date and misaligned data. 

Copying production data furthermore does nothing to improve the quality of the historical data, which lacks outliers and negative results, as well as data needed for testing new functionalityWith a logistical “mask and copy” approach to test data provisioning, QA therefore lacks the data needed to test early, rigorously, and regularly. The released systems are in turn exposed to damaging bugs, creating user frustration and massive rework for development. 

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Unlock Compliance and Quality

Test Data Automation provides a quick and simple approach to masking complex data, while weaving in rich synthetic test data to combine compliant testing with quality testing. Data scanning reliably identifies sensitive information across data sources, with support for all major database and file types. Masking data consistently across multiple data sources is as simple as specifying Excel-like functions in an easy-to-configure seed spreadsheet, using a dictionary of re-usable masking rules to transform data consistently across data sources. The data functions resolve dynamically as the re-usable masking functions are run from a central catalogue, producing rich and varied test data for reliable and rigorous test execution.  

Mask and augment

Parallel test teams can trigger high-speed masking jobs on demand, eliminating the delays associated with waiting for slow and erroneous data refreshes. Test Data Automation further creates any missing data combinations needed for rigorous testing, detecting defects earlier and at less cost to fix. Data creation can leverage a complete set of synthetic test data functions, specified alongside the masking functions to improve data coverage. Alternatively, intuitive data modelling applies coverage algorithms to generate every positive and negative data scenario contained in quick-to-build flowchart models. The accurate synthetic data augments the reliably masked data, rapidly running high speed “mask and augment” routines that enable both quality and compliance at speed.


Quality Data at Speed

Watch this short demo of masking and augmenting HR Data for rigorous ETL Testing, and discover how: 

  1. All previous mask and generate jobs are rapidly re-usable from a central catalogue, specifying the seed data sheet with which to create rich masked data.

  2. Masking data is as quick and simple as specifying combinable, Excel-like function in the easy-to-use seed data configuration spreadsheet.

  3. The comprehensive set of masking functions resolve dynamically each time a masking is re-run, producing varied sets of accurately masked data.

  4. Intuitive data modelling in Test Modeller creates a visual flowchart of the valid and invalid combinations needed to detect bugs earlier, and at less cost to fix.

  5. Automated coverage algorithms identify every combination of data reflected in the model, applying easy-to-define business rules to reflect necessary constraints in the generated data.

  6. The model-based data generation adds any missing rows to the seed data spreadsheet, providing rich masked data complete with outliers, negative results and unexpected scenarios.

  7. Automated, easily-re-usable “mask and augment” routines are easily embeddable in CI/CD processes and as a standard step in automated test execution, producing rich data “just in time”.


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