--- title: "EuroSTAR 2026: AI, Quality Engineering and the Importance of Test Data" description: After EuroSTAR 2026 in Oslo, one message stood out without reliable test data, sustainable software quality remains out of reach for test automation. image: https://blog.datprof.com/hubfs/Eurostar%202026%20Oslo%20Conference.png --- ![](https://blog.datprof.com/hs-fs/hubfs/Eurostar%202026%20Oslo%20Conference.png?width=1655&height=950&name=Eurostar%202026%20Oslo%20Conference.png) AI Self-service test data Agile development # EuroSTAR 2026: Quality Engineering Starts with a Strong Foundation ![Maarten Urbach](https://blog.datprof.com/hs-fs/hubfs/Maarten%20Urbach.jpg?width=48&height=48&name=Maarten%20Urbach.jpg) Maarten Urbach 18 June 2026 EuroSTAR 2026: AI, Quality Engineering and the Importance of Test Data 2:41 Last week, the DATPROF team travelled to Oslo for EuroSTAR 2026. It was a few inspiring days filled with conversations, fresh perspectives, new ideas, and most importantly, meaningful connections with professionals from the software testing and quality engineering community. As always, a conference like [EuroSTAR ](https://conference.eurostarsoftwaretesting.com/)is much more than a collection of presentations and exhibition stands. It is an opportunity to reconnect with peers from across Europe and beyond, exchange experiences, and gain insight into the challenges and priorities organizations are facing today. One topic surfaced in almost every conversation: the growing impact of Artificial Intelligence on software quality. AI offers tremendous opportunities. From supporting test automation and improving analysis to enabling smarter development and testing practices, the potential seems almost limitless. At the same time, AI raises important questions. How do we implement AI responsibly? How do we maintain control over quality? How do we ensure new technologies create real value rather than additional complexity? Throughout many discussions, one observation stood out once again: the fundamentals still matter. ## Test Data: The Overlooked Foundation During conversations about test data, we noticed how many organizations continue to face the same challenges. Teams want to release faster, automate more, and take advantage of AI-driven capabilities, yet they struggle with the availability, quality, and usability of test data. And despite its importance, test data often receives less attention than it deserves. During an [Accenture ](https://www.accenture.com/)session, of [Anastasia Simou ](https://www.linkedin.com/in/anastasia-simou/")test data was compared to the “middle child syndrome”: it is always there, but often receives less attention than major initiatives such as AI, automation, and digital transformation. That comparison felt remarkably accurate. Everyone wants to accelerate development. Everyone wants more automation and greater use of AI. But without high-quality test data, those ambitions become much harder to achieve. You can build a beautiful house, but if the foundation is weak, problems will eventually appear. The same applies to software quality. ## AI, Automation and Quality Engineering Depend on Reliable Data AI can accelerate many aspects of software delivery, but it does not automatically improve the quality of the underlying data. When test data is incomplete, outdated, inaccessible, or non-representative, teams are forced to rely on workarounds. They test with unrealistic datasets, wait for manual refreshes, depend on full copies of production environments, or avoid critical scenarios because the right data simply is not available. This limits not only testing effectiveness but also the value AI can deliver. AI-driven testing relies on representative and trustworthy data. Test automation relies on predictable and repeatable test datasets. Quality engineering relies on a connected ecosystem where people, processes, environments, tooling, and data work together. That is why test data should not be viewed as an operational concern. It is a strategic enabler of modern software delivery. ## Turning Test Data from a Bottleneck into an Accelerator Many organizations experience test data as a bottleneck. Obtaining data takes too long. Production data cannot be used due to privacy regulations. Environments are large, expensive, and difficult to refresh. Teams depend on database administrators or centralized specialists to provision the data they need. A mature [test data management](https://www.datprof.com/test-data-management-platform/) approach changes that dynamic. With secure, realistic, and reusable test datasets, teams can work faster and more independently. [Data masking](https://www.datprof.com/data-masking/) helps protect sensitive information. [Synthetic test data generation](https://www.datprof.com/synthetic-test-data/) creates safe and scalable alternatives. Data subsetting reduces infrastructure complexity while maintaining data relevance. And self-service capabilities enable teams to access data when they need it. Instead of slowing down software delivery, test data becomes a driver of quality, speed, and innovation. ## Quality Engineering Requires a Strong Foundation Quality engineering is about much more than testing at the end of the development lifecycle. It is about building quality into every stage of software delivery. That requires close collaboration between development, QA, DevOps, security, and data teams. But collaboration becomes difficult when the foundation is not in place. If test data is not secure, compliance risks increase. If test data is not representative, test results become less reliable. If test data is not readily available, delivery pipelines slow down. If test data cannot be automated, CI/CD processes remain dependent on manual effort. Organizations can certainly make progress without addressing these fundamentals. New tools can be introduced. Processes can be improved. AI initiatives can be launched. However, sooner or later, the same limitations resurface if the foundation remains weak. In many ways, it is like building upward without strengthening the structure underneath. ## The Power of the Community Beyond the technology discussions, one of the most valuable aspects of EuroSTAR was reconnecting with people across the community. We met customers, partners, industry experts, and fellow practitioners from multiple countries. Each organization faces different challenges, but they share a common goal: delivering software that is better, faster, and more reliable. That is what continues to make EuroSTAR such a valuable event. Not only because of the sessions themselves, but because of the conversations in between. Those discussions often reveal what organizations are truly struggling with, where innovation is creating value, and where practical realities are still catching up with technological promises. ## Looking Ahead After a productive week in Oslo, we return with fresh ideas, valuable conversations, and renewed energy to help organizations advance their quality engineering journey. From AI and test automation to new tools, skills, and delivery practices, the industry continues to evolve rapidly. But one message remains constant: **Start with the foundation.** Because a strong foundation makes every next step possible. Why is test data important for AI-driven testing? AI-driven testing also depends on high-quality test data. Without reliable, representative and well-managed data, the outcomes may look advanced, but the results are still questionable. How does test data impact test automation? Reliable test data enables repeatable, consistent, and scalable automated testing processes. What is the role of test data in quality engineering? Test data provides the foundation for validating software quality throughout the development lifecycle while supporting automation, compliance, and faster delivery. How can organizations improve test data management? Organizations can improve test data management through data masking, synthetic data generation, subsetting, automation, and self-service access. ## Share this post [mailto:https%3A%2F%2Fblog.datprof.com%2Feurostar-2026-ai-quality-engineering-and-the-importance-of-test-data](mailto:https%3A%2F%2Fblog.datprof.com%2Feurostar-2026-ai-quality-engineering-and-the-importance-of-test-data) ## Keep reading [AI Test data management ### Build or buy test data management software? ](https://blog.datprof.com/build-or-buy-test-data-management-software) [AI Self-service test data Agile development ### DORA-Ready Test Environments | DATPROF ](https://blog.datprof.com/dora-ready-test-environments) ```json { "@context" : "https://schema.org", "@type" : "BlogPosting", "author" : { "@type" : "Person", "name" : "Maarten Urbach", "url" : "https://blog.datprof.com/author/maarten-urbach" }, "dateModified" : "2026-06-18T19:24:02.607Z", "datePublished" : "2026-06-18T19:24:02.000Z", "headline" : "EuroSTAR 2026: AI, Quality Engineering and the Importance of Test Data", "mainEntityOfPage" : { "@id" : "https://blog.datprof.com/eurostar-2026-ai-quality-engineering-and-the-importance-of-test-data", "@type" : "WebPage" }, "publisher" : { "@type" : "Organization", "logo" : { "@type" : "ImageObject", "url" : "https://blog.datprof.com/hubfs/logo_dark@8x.png" }, "name" : "DATPROF" } } ``` ```json { "@context" : "https://schema.org", "@id" : "https://blog.datprof.com/eurostar-2026-quality-engineering-test-data-foundation#blogposting", "@type" : "BlogPosting", "about" : [ { "@type" : "Thing", "name" : "Test Data Management" }, { "@type" : "Thing", "name" : "Quality Engineering" }, { "@type" : "Thing", "name" : "AI-driven testing" }, { "@type" : "Thing", "name" : "Test automation" }, { "@type" : "Event", "location" : { "@type" : "Place", "name" : "Oslo, Norway" }, "name" : "EuroSTAR 2026" } ], "author" : { "@type" : "Person", "name" : "Maarten Urbach" }, "dateModified" : "2026-06-19", "datePublished" : "2026-06-19", "description" : "After EuroSTAR 2026 in Oslo, DATPROF reflects on AI, test automation and quality engineering. 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