Join Ashok Kumar as he shares how efficient test data management can boost test coverage and team velocity in distributed agile environments.
Discover strategies for automating test data creation, managing exclusive access, and enabling seamless collaboration with internal teams and external partners through APIs.
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How is access to test data controlled when exposing it to external partners and vendors?
What was the single biggest unexpected challenge or learning you encountered while building this custom TDM solution?
What’s the best way to make sure test data is realistic enough to catch real-world issues?
Building a custom TDM platform is a significant investment. How did your team make the ‘build vs. buy’ decision? What key feature was missing in commercial tools that ultimately led you to create your own solution?
What were the most significant learnings or unexpected challenges encountered during the implementation of the test data management framework, and how were these addressed?
What factors tipped the scale for your team to develop a custom TDM platform rather than adopting an off-the-shelf solution?
How did you weigh the trade-offs between buying a ready-made TDM tool and investing in a custom build, and which unmet need pushed you toward building?
What are the future plans or next steps for the evolution of the test data management system, with the the increasing adoption of AI and machine learning in test data creation and analysis in mind?
Aside from reserving data, what other features or mechanisms were incorporated into the solution to prevent data contamination and ensure consistent test results across various teams and test runs?
How does poor or insufficient test data impact the reliability of test results?
What are best practices for generating or sourcing realistic test data?
How did the new framework contribute to “happier teams” and “less noise” in test results? Were there qualitative or quantitative indicators of this positive impact?
As AI takes over repetitive testing, what’s the new superpower for human testers?
How can AI help generate smarter, context-aware test data that not only increases coverage but also uncovers edge cases humans might overlook?
What strategies work best for keeping test data relevant as applications evolve, so today’s effective coverage doesn’t become tomorrow’s blind spot?
Is poor test data the biggest hidden blocker to achieving true test coverage?
What is underlying tech stack for the framework?
Which types of test data is AI currently effective at generating?
What tools or frameworks are recommended for managing large volumes of test data effectively?
