Philipp Frenzel
Team Lead IT-Dataplatform (Data As A Product Guy)
Winterthur, Switzerland
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With over 20 years of experience in data analytics and corporate performance management, I have consistently driven value through data-driven insights. My journey began in the dynamic field of data analytics, where I honed my skills in extracting meaningful patterns from complex datasets. Throughout my career, I’ve contributed to various industries, but my recent focus has been on the health insurance sector in Switzerland.
Key Highlights:
Data Analytics Expertise: Over the past two decades, I’ve navigated the ever-evolving landscape of data analytics. From statistical modeling to machine learning, I’ve leveraged cutting-edge techniques to uncover actionable insights.
Corporate Performance Management: As a seasoned professional, I’ve played a pivotal role in optimizing organizational performance. Whether it’s streamlining processes, enhancing efficiency, or aligning strategic goals, I’ve been at the forefront of driving positive change.
Health Insurance Industry: For the last four years, I’ve been part of a leading health insurance company in Switzerland. In this role, I’ve contributed to critical areas such as claims processing, risk evaluation, and customer experience. My expertise has helped improve accuracy, streamline operations, and enhance decision-making within the insurance domain.
Team Leadership: As the team leader of the data platform, I’ve fostered collaboration, innovation, and excellence. Guiding a talented group of professionals, I’ve overseen the development of robust data infrastructure, ensuring seamless data flow and accessibility.
Vision for the Future:
Looking ahead, I’m committed to staying at the forefront of data and analytics advancements. Whether it’s harnessing the power of AI, exploring predictive modeling, or driving digital transformation, I remain passionate about shaping the future of data-driven decision-making.
Area of Expertise
Topics
Enhancing a modern datamesh plattform on Microsoft Fabric with MCP-Servers
What if your AI could onboard data product consumers, validate contracts, enforce policies, and trigger workflows—without becoming a black box? In this episode, I share a clean MCP server blueprint: a Coordinator for control, a Planner for structured decisions, Data Product connectors via REST, and Microsoft’s Fabric MCP—wired together with Service Bus signals and computational governance tools.
400 Data Products Later: What Really Works (and What Breaks) in a Regulated Swiss Healthcare Company
In the last two years, we’ve shifted from “data as a byproduct” to truly data as a product inside a regulated Swiss healthcare environment. Today we operate around 400 data products with roughly 150 product owners—and scaling this is less about fancy architecture and more about the human system around it.
In this session, I’ll share what happens when data products move from a nice concept to day-to-day reality: how you define a usable interface, what “ownership” actually means when dozens of teams depend on each other, and why communication is often the real bottleneck. We’ll look at the operational side too—monitoring across many products, making quality visible, and setting up incentives so ownership doesn’t become a burden but a source of pride and impact.
Expect practical patterns, honest lessons learned, and the kind of challenges you only find once you’re well past the pilot phase.
Find more about me under: https://www.data-as-a-product-guy.com
Evolving Data Mesh Architecture: From Theory to Practical Innovation
As data-driven decision-making becomes central to modern businesses, implementing the principles of a DataMesh seems like the ideal path. But what happens when architectural ideals meet the complex realities of scaling an enterprise data platform?
Join us as we unpack the journey of buildn a data platform inspired by DataMesh. We’ll explore how we started with a theoretical foundation and made deliberate, practical deviations to address real-world challenges.
We will walk you through the tech and org trade-offs we faced - and lessons learned.
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