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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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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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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