What Really Works Today – And What Doesn’t

Artificial intelligence can do a lot in the test data space: less manual work, more automation, faster results. In practice, however, AI has to prove itself against a range of requirements: reproducible test bases, complex business scenarios, compliance rules, and teams with completely different needs.

This is exactly where our test data management platform comes in: AI is added where it can actually play to its strengths. In our talk, we show how proven test data processes can be combined with modern AI capabilities:

  • Limits of AI in test data generation – and why production data is indispensable
    We explain why rare defects and complex process chains still require the level of detail only production data can provide – and why AI-generated data can complement, but not replace, them. Topics include reproducible regression tests, stable environments, and the secure handling of personal data.
  • AI-powered synthetic data instead of copy & paste
    Using concrete examples, we show how to generate realistic synthetic test data from a business domain model (e.g. customers, contracts, transactions). How can AI help respect defined value ranges, distributions, relationships, and statistics? How do we create data that looks like real profiles, but without privacy or compliance risk?
  • Test data orchestration for different consumers – technically decoupled
    Developers, testers, automation teams, and ML engineers all need different data, but not extra hurdles. We demonstrate how test data workflows can be modeled, automated, and consumed via ordering interfaces (“DataShop”) – from targeted defect tests to regular regressions. Technical complexity stays within the platform; for users, the only thing that matters is: “I get exactly the data I need for my use case.”
  • Tangible day-to-day relief: search, order, reserve
    Without completely reworking the slide deck, we highlight concrete areas where AI and automation already provide real, hands-on value:

    • faster discovery of suitable constellations using searchable, business-centric indices
    • automated generation of additional variants based on defined scenarios
    • reservation and protection of test data so that teams sharing environments don’t overwrite and break each other’s data foundations
  • Hybrid model: production data + AI + automation
    We outline what a combined approach looks like: production data as a reference, safeguarded by masking and modification, enriched with AI-generated synthetic data for new or rare constellations. All of this is embedded into automated workflows, reporting, and self-service ordering – with a focus on reproducibility, compliance, and developer experience.

Takeaways

  • A realistic understanding of when AI clearly adds value in test data management today – and when it’s better to stick with proven approaches.
  • Concrete insights into a platform that uses AI for test data in a model-based, controllable, and reproducible way – rather than just generating “some” data.
  • Practical guidance on how to evolve from reactive data provisioning to a proactive, hybrid test data strategy – without disrupting your established processes.

Christoph Stock, Manager Product Development, UBS Hainer GmbH will host the session “Test Data and AI in Everyday Practice“ that will take place on Thursday March 5.

Meet world’s leading Test Automation experts! Register now and ensure your place at this unique conference. Get a combi ticket for a fee of € 990,- or register for Day 1 for € 545,- or for Day 2 for € 495,-.