I believe clarity is the ultimate competitive advantage in data and AI.
Most data initiatives fail not because the technology was wrong, but because they started with tools and architecture instead of business reality. data.KISS is my approach to fixing that: simplicity over hype, fundamentals over buzzwords, value over volume. Just pragmatic systems that deliver value in complex, regulated environments.
This is my corner of the internet to share opinionated, battle-tested thinking on data architecture, data management, and the realistic application of AI.
How I Work
Use-Case-First Thinking
Every architecture decision starts with a business question. If there’s no use case, there’s no pipeline.
Complexity Reduction
Unnecessary complexity prevents good decisions. I ask: What can we standardize, automate, or remove?
Critical Thinking
I question assumptions – especially in AI/data hype cycles – to find what actually delivers value.
JOMO (Joy of Missing Out)
I guard focus by saying “no” to trends that don’t deliver measurable value.
Bridge Between IT and Business
Technical solutions only matter when they create business impact. I work in both worlds and translate between them.
What I Do
I’m a Senior Data Architect at Adam Riese (W&W-Gruppe), where I design and build production-grade data platforms in a highly regulated insurance environment. In a small, high-impact team I own data products end-to-end – from lakehouse architecture and semantic modeling to automated, metadata-driven pipelines and actionable governance.
In practice, this currently means:
- Data Architecture & Modeling: I design and migrate data architectures – currently from historically grown, report-centric mart tables to star schema. The goal: move complexity out of the PowerBI frontend into the backend, so that business users get a data model that is both performant and understandable.
- AI-assisted and Metadata-Driven Automation & Standardization: I build products that use more and more automation instead of manual coding – including standardized semantics, clear KPI definitions, and consistent naming. The result: faster delivery, fewer errors, and a shared language between IT and business.
- Action-Oriented Data Governance: I ensure compliance requirements are known and met – not through paperwork, but through implementation: standardized definitions, resource and cost optimization (FinOps), and tracking product KPIs – from daily active users to business adoption – to measure whether data products actually deliver value.
- Teaching & Knowledge Sharing: I teach the next generation of data professionals at DHBW Baden-Württemberg how to think before they build – because fundamentals don’t expire. I also regularly speak at conferences (TDWI, KI Navigator, DOAG, etc.).
This blog is where I share what I learn in the process – opinions included.
Start Here
New to the blog? These posts capture my core philosophy:
- The Data House – How Use‑Cases‑First architecture cuts through confusing buzzwords.
- Skills 2026+ – What data professionals actually need.
- One Database, Six Workloads: Analyzing F1 Telemetry With Oracle Converged Database – How to simplify complex stacks without sacrificing performance.
- Agentic AI with DuckDB – When (and when not) to use LLMs for analytics.
If you believe our industry needs more clarity and less hype – Connect with me on LinkedIn
If you want to discuss pragmatic data architecture, data.KISS or AI – you can also subscribe via RSS.
If you value clarity over complexity and fundamentals over hype – you’re in the right place.