13 maj 2026
52 min
Data Engineering, AI Experimentation, Health Tech, and Data Platforms are reshaping enterprise innovation. In this episode of Builders, Jonas Dieckmann, Global Manager of Data Intelligence & Team Lead of Data Engineering at Philips, explains how one of the world’s largest health tech companies is scaling AI through cross-functional collaboration, domain-driven data platforms, and rapid experimentation. Why do so many enterprise AI initiatives fail — and what is Philips doing differently?
Jonas shares:
If you’re building data platforms, scaling AI teams, or navigating enterprise transformation, this episode delivers practical insights from the frontlines of global health tech.
🎧 Subscribe to Builders for more conversations with leaders shaping the future of AI, engineering, and innovation.
#DataEngineering #AI #HealthTech #DataPlatform #DataMesh #Philips
Chapters
(00:00) How Philips Is Driving Data Innovation in Health Tech
(01:24) Jonas Dieckmann’s Journey Into Data & AI Leadership
(02:44) The Biggest Challenges of Data Platforms in Healthcare
(05:27) Why Health Tech Data Is More Complex Than Most Industries
(08:07) Inside Philips’ AI Squad Strategy for Innovation
(13:15) How Philips Chooses AI Use Cases That Actually Matter
(16:36) Why Fast AI Experiments Lead to Better Results
(22:46) The Shift From Centralized Data Platforms to Data Mesh
(28:35) Data Governance and Ownership in a Data Mesh World
(30:37) What Future Data Platforms Must Support for AI
(33:01) Why Metadata and Data Lineage Are Becoming Essential
(35:26) What Separates Great Data Engineers From the Rest
(39:58) How Philips Evaluates Talent for Data & AI Teams
(45:30) The Most Exciting Trends in Data and AI Right Now
(47:39) The Biggest Mistakes Companies Make When Scaling AI
(50:03) Jonas Dieckmann’s Vision for the Future of Data at Philips
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