Session Topic
AI Generated Test Data: Know Your Need
If all you have is a hammer, everything looks like a nail: AI continues to transform testing landscapes, promising to drastically reduce complexity and make automation a breeze. Yet, with development speeding up, the availability of realistic, de-identified test data is becoming a bottleneck. On the enterprise scale, AI can neither replace the nuanced reality of production data, nor guarantee compliance. Plus, the organizational challenge of different data consumers (developers, testers, automation engineers, ML engineers) with diverging requirements is often overlooked. Let’s examine the complexities of supplying test data enterprise-wide across departments and diverse environments - tailored to the individual consumers’ needs. I’ll share how to separate technical from organizational complexity and where artificial intelligence can help when employed in the right context.
Biography
Bastian is a data management and process automation specialist with a multifaceted background in technical sales and consulting in finance, insurance, retail and pharma. With previous leadership roles in data science and software QA, Bastian has honed a pragmatic, holistic approach to solving complex data challenges. Bastian's passion lies in bridging technological innovation with practical, user-centric solutions, ensuring that test environments are as robust and agile as the challenges they face. In his current role as Lead Sales Engineer US at UBS HAINER, he drives the adoption of the XDM Data Orchestration Platform — enabling enterprise software engineers, testers, and machine learning teams to access production-grade, real-world data on demand.