Philipp Frenzel
Team Lead IT-Dataplatform (Data As A Product Guy)
Winterthur, Switzerland
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AI Platform Lead | Data & AI Strategist | Agentic Systems Advocate
With more than 20 years of experience in data, analytics, and digital transformation, I have spent my career helping organizations turn information into measurable business value. My foundation lies in data analytics, data platforms, and enterprise-scale data governance, where I have led teams, platforms, and strategic initiatives in highly regulated environments.
Over the past years, my focus has evolved from building data-driven organizations to enabling AI-driven organizations. Today, I lead the intersection of Data Platforms and AI Platforms within the Swiss health insurance industry, helping shape the foundations for trusted, scalable, and governed AI adoption.
Key Areas of Expertise
- AI Platform Leadership
Leading the evolution from traditional data platforms toward enterprise AI platforms, enabling organizations to operationalize Generative AI, Agentic AI, and intelligent automation at scale.
- AI Governance & Responsible AI
Designing governance frameworks that balance innovation, security, compliance, and transparency in one of the most regulated industries. My work focuses on ensuring AI solutions remain trustworthy, auditable, and aligned with business and regulatory requirements.
- Agentic Development & AI Engineering
Specialized in designing and implementing enterprise-grade AI agents using the Microsoft Agent Framework, combining modern software engineering practices with AI-native architectures. My focus includes multi-agent systems, orchestration patterns, identity propagation, and secure agent-to-agent collaboration.
- Data & AI Platforms
Deep expertise in Azure, Microsoft Fabric, Data Mesh, and enterprise data platforms. I believe that future AI capabilities can only succeed when built upon strong foundations of data quality, governance, interoperability, and platform thinking.
- Leadership & Transformation
Leading multidisciplinary teams through technological change, fostering innovation cultures, and helping organizations navigate the transition from data-centric to AI-centric operating models.
Vision
I believe the next generation of enterprise systems will be built around intelligent agents that act as active participants within business processes. Data products will evolve into intelligent, interactive products capable of reasoning, collaborating, and creating value autonomously.
My mission is to help organizations build the platforms, governance models, and engineering capabilities required to make this transformation secure, scalable, and sustainable.
Area of Expertise
Topics
From Metadata to Notebooks: Metadata-Driven AI Data Engineering (MADE) in Microsoft Fabric
Your Fabric metadata already describes the pipelines you're hand-writing every sprint. See how an AI agent reads OneLake Catalog and the Fabric MCP, proposes data-product cuts from ForeignKey graphs and update frequencies, and writes production PySpark notebooks back into your workspace — with governance intact.
Fabric RTI in Action: Solving Data Mesh Dependencies at Scale
How do you orchestrate 600 data products without losing control? In this session, we reveal the practical implementation of a multi-domain data mesh. We’ll break down our transition to Fabric native RTI features for dependency management. See how we use Eventstream and Activator to respond to Fabric job events in real time. We’ll share our architectural "why" (ADRs) and the "how," providing a blueprint for managing complex upstream and downstream flows in a native ecosystem.
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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