Complete Data Subsetting

Rapidly build accurate data subsets.

Use simple-to-define parameters and out-of-the-box workflows to build referentially intact data sets quickly and easily.

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Take the complexity out of building complex data subsets


With Test Data Automation, data subsetting is:
  • Quick and Simple, defining parameters in Microsoft Excel, and running pre-defined Workflows to perform the subsetting.

  • High performance, using out-of-the-box actions and “cascade” joins that walk from one table to another for rapid extracts.

  • Iterative, using recursive data collection and readily repeatable rules to gather data until you have created the perfect set.


  • Resource efficient, setting easy-to-use criteria to gather only as much data as you need for the perfect subset.

  • Targetted and concise, using data de-duplication to remove repeated rows and create smaller, leaner data subsets.

  • Built for test and development, using additional TDM utilities to create functioning data sets for testing complex applications.

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Provision concise and coherent data sets in parallel


Subsetting from Test Data Automation “crawls” up and down parent and child tables, recursively gathering all the related data needed for a coherent data set.


Test Data Automation Enables:
Traditional TDM Processes lead to:

Parallel Testing and Development:

Testers and developers work in parallel from the same source data, using isolated subsets. This avoids the delays caused by cross-team constraints, the frustration of test data being edited, moved or deleted by another team.

Test Data Constraints and Delays:

Testers and developers compete for data among a limited number of data sets. Engineers must wait for data to become available, and bottlenecks mount when data is lost to a refresh or is cannibalized by another team.

Shorter Test Run Times:

Testing is less resource-intensive and faster, using smaller but representative sets of data that are faster to run and produce less cumbersome results.

Resource-Intensive Test Execution:

Large copies of production data are used in testing, but the data requires vast time and resources to run and produces vast, unwieldy results.

Faster, Affordable Masking:

Masking uses a representative Data Subset that contains less complex data. This reduces the need for slow and complex masking prior to testing.

Slow and Costly Masking:

Slow and complex masking must be performed on large copies of complicated production data, ramping up test data provisioning time and cost.

Contained Data Extracts:

A high-speed TDM utility extracts only as much data as is needed, avoiding slow and cumbersome extracts of large data sets.

Slow and Cumbersome Data Extracts:

Complex data extracts are slow and cumbersome. They require complex scripts and must be performed on large, complex data sets.

Quicker Testing and Development:

Test and development teams spend less time searching for data, working instead from data sets just big enough to fulfil their exact needs.

Testers Hunt for the Data they Need:

Testers need exact data combinations to fulfil their tests. They must hunt manually for these rare scenarios from among unwieldy production data sets.

Easily Understood Data:

Subsetting enables easier data exploration, running repeated and exploratory subsets instead of experimenting with complex data joins.


Slow and Complex Data Exploration:

Data stored across vast, interrelated tables is hard to understand. Experimenting with complex data joins provides little clarification.


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Parallel, less resource-intensive testing and development.

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