Alexander Arvidsson

One of the most difficult events to get into for me has always been the Power BI Gebruikersdagen - a.k.a the Dutch Power BI User Days. I’ve always held it in very high regard, and I never stopped submitting in the hopes of one getting to speak. Last year I was invited to deliver a half-day session on presentation skills and a normal one-hour long session on data modeling. I had a blast.

Using Claude Code, I built a full-stack application to replace Microsoft Access - without being a developer or understanding React, TypeScript, or Node.js. LLM-assisted coding enables rapid prototyping and bridges the gap between business requirements and technical implementation, but like flight simulators, it doesn’t make non-developers into developers. The parallel matters: functional prototypes aren’t production-ready systems, and knowing the difference requires actual expertise.

Prompt injection is architecturally unfixable in current LLMs, but defense-in-depth works. Training-time defenses like Instruction Hierarchy, inference-time techniques like Spotlighting, and architectural isolation create practical systems. Microsoft’s LLMail-Inject showed thatadaptive attacks succeed at 32% against single defenses, 0% against layered approaches. Real failures like GitHub Actions compromise prove that securing obvious surfaces isn’t enough. Like SQL injection, it’s manageable with layering.

I’m joining Advania as Lead Data Transformation Architect to build their data practice into something coherent. One of Sweden’s largest MSPs, Advania needs technical foundations that enable real analytics and AI - not just data theater. I’ll lead their direction across Microsoft Fabric, the Azure Data Platform, and hybrid solutions - from feasibility through implementation. The opportunity is building a fresh structure rather than retrofitting onto legacy decisions. This is walking toward work that matters.

The promise of #SNÆCKS trumps the cold and darkness of Oslo in February. Speaking at the third annual Fabric February promises to be a blast just like 2025!

LLMs fundamentally cannot distinguish between instructions and data. Whether you’re building RAG systems, connecting MCP servers to your data platform, or just using AI tools with sensitive information, every retrieved document is a potential instruction override. The Wall Street Journal just proved this by watching Claude lose over $1,000 running a vending machine after journalists convinced it to give everything away for free.

Knowing what’s broken is easy - fixing it requires understanding your domain, imagination to form hypotheses, and courage to act. Most data initiatives fail not because the analysis was wrong, but because nobody owned the outcome or knew what to do next. Reports aren’t neutral information: they’re persuasion. Before collecting data, ask: what decision does this inform? Use ‘data contracts’ to enforce discipline. The Portsmouth Sinfonia had instruments but couldn’t make music. You have data. Can you drive decisions?

The famous beer-and-diapers data mining story? Never happened. Most ‘data-driven’ companies are just data-decorated, exploring dashboards without hypotheses or action plans. Netflix, UPS, and Capital One succeeded because they started with clear hypotheses about what drives outcomes, then collected data to test them. You don’t explore a violin to see what noises it makes - you decide what piece to play. Are you playing instruments or making music?