With the rise of agile and DevOps practices, software testing is more important than ever for delivering high quality applications at speed. However, providing testers with the right test data remains a major bottleneck.
Many organizations have turned to test data virtualization and synthetic data generation tools to help alleviate these test data challenges. But, while helpful, these solutions alone are not enough for implementing a truly effective test data strategy.
A complete test data management strategy instead requires a 3-stage approach:
Stage 1: Understand Your Data
Before generating or virtualizing test data, you need a deep understanding of the data that your application relies on. This includes profiling the data to reveal patterns, sensitive information, and relationships between entities, while performing comparisons to understand differences between environments.
AI and ML can further help automatically detect insights from large data sets. The result is knowing what types of test data you need and the appropriate data combinations to generate when creating complete and realistic data.
Stage 2: Find and Make the Right Test Data
With data understanding as the foundation, organizations can start provisioning test data. This is where test data virtualization comes in — tools that allow access to data from production without copying sensitive information. Other techniques like subsetting, masking, and synthetic generation are also used to make test data. The goal is creating the right test data sets for different testing needs. For instance, performance testing requires large volumes, while functional testing needs specific application data.
Stage 3: Deliver Test Data Effectively
Finally, test data needs to be delivered to testing environments efficiently. Test data management incorporates virtualization to provision data on demand without persisting copies. Self-service access, automation, and integration with testing tools streamlines test data delivery. Containers and sandboxes provide pre-configured test data for fast provisioning. With the right delivery mechanisms, testers get the data they need, when and where they need it.
The 3 Stages of an Effective Test Data Management (TDM) Strategy.
What should you do next?
Advanced test data techniques are just one part of a complete strategy. Focusing on virtualization or synthetic data alone limits effectiveness. Companies need to implement test data management holistically, tackling understanding, making, and delivering data to achieve testing success. A comprehensive approach to managing test data is required for keeping pace with today’s faster software release cycles.
It’s important to note that the wrong selection of test data tools can undermine test data efforts, similar to issues with test automation. Organizations must carefully evaluate their needs and environment before adopting tools. Otherwise, they may end up with ill-fitted technologies that fail to solve core test data challenges. A staged approach ensures the right tools are supporting each step of effective test data management:
An effective Test Data Management strategy picks the right tools — and takes a staged approach.
Simply investing in test data virtualization or synthetic generation alone is like buying only a pair of trainers and expecting to run a marathon. You need the full gear — understanding the course, nutrition, training, and mental preparation. Test data management requires a complete strategy, not just piecemeal tools, for successful testing.
Curiosity Software are test data veterans, offering over 30 years’ experience and a comprehensive test data toolset. Talk to us if you want to take your test data strategy to the next level!
About the author: Rich Jordan is an Enterprise Solutions Architect at Curiosity Software and has spent the past 20 years within the Testing Industry — mostly in Financial Services, leading teams creating test capabilities that have won multiple awards in Testing and DevOps categories.