Most data blogs chase the latest hype. This one challenges it.
data.KISS is my antidote to buzzword-driven architecture and AI. Here, I break down why proven, pragmatic methods often beat trendy frameworks — and when they don’t. This is my place to experiment, reflect, and share opinionated takes on data architecture, data management, and AI.
I’m Andreas Buckenhofer, data architect in insurance, lecturer at DHBW, and advocate for complexity reduction. If you’re tired of “yet another reference model,” you’re in the right place.
I have experience in automotive, insurance and finance/regulatory contexts where trust, traceability, and operational stability matter.
What Drives My Work
Complexity Reduction
I simplify by asking: What can we standardize, automate, or remove?
Critical Thinking
I question assumptions—especially in AI/data hype cycles—to find what actually delivers value.
Bridge Between IT and Business
Technical solutions only matter when they create business impact. I translate between both worlds.
JOMO (Joy of Missing Out)
I guard focus by saying “no” to trends that don’t deliver measurable value.
Knowledge Sharing
Teaching at DHBW and speaking at conferences keeps my thinking sharp and practical.
New to the blog? These posts capture my core philosophy and how I work:
- 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.
Want to talk? Connect with me on LinkedIn if you want to discuss pragmatic data architecture, data.KISS or AI. For updates, you can also subscribe via RSS.
If you value clarity over complexity and fundamentals over hype—you’re in the right place.