Data doesn't transform
organizations.

The question that has followed me through my entire career sounds almost too simple to say out loud: why are we doing this?

Not why we capture data. Not why we run clinical trials. But why — really why. There is a patient at the end of every dataset we produce. A person waiting for a therapy that may or may not exist because of decisions made on the basis of data we shaped.

Most of our industry is extraordinarily good at the how. The processes, the standards, the governance — we've built remarkable machinery. And now AI is making that machinery faster than we ever imagined. But speed is not the point. Efficiency was never the point.

Effectiveness is. Doing the right things — for the right reasons — with enough clarity to know the difference. That question changes how I lead. And it's the reason I care so deeply about what statistical programming functions are capable of becoming.

It started with a boxplot I couldn't explain.

My first encounter with clinical data was as a student assistant at the Institute for Medical Statistics in Düsseldorf — tasked with visualizing measurements from a dental implant study using SAS. I produced the plot. It was technically correct. And it made no sense whatsoever.

My supervisor told me as much. So I sat down, studied the variables, understood how they were collected and what they actually measured. Only then could I show something meaningful. That moment planted a seed I didn't recognize at the time: data without understanding is just noise dressed up as output.

Years later, the seed became a conviction. I was interviewing for my first move from CRO work into pharma. The Head of R&D asked what the clinical rationale was for the studies I'd worked on. I had nothing. I had processed the data. I had never read the protocol.

I left that interview deeply dissatisfied — not with them, but with myself. So I went home and read every protocol I could find. I spent the next year closing the gap — deliberately, methodically. When I finally made the move to Schwarz Pharma, it was because I felt ready. On my own terms.

That question followed me from Düsseldorf over Monheim to Wuppertal, from CRO to Schwarz Pharma to UCB to Bayer. It's the reason I believe statistical programming is not a support function, but the intelligence layer of drug development.

But no transformation happens in isolation — and mine didn't either.

In 2013, I chaired the PHUSE conference in Brussels. The first ever sold-out PHUSE conference. I was leading a program committee of over thirty people — almost all of them more experienced than I was at the time. Heads of statistical programming functions, subject matter experts from across the industry.

The theme I chose was Patient Centricity. Not as a slogan — as a lens. Leading that committee taught me something I've carried ever since: you don't lead experienced people through authority. You lead them through purpose. Give people a WHY worth working toward — and they will bring more than you asked for.

PHUSE is where private convictions become collective momentum. Where I've learned that the shift I'm trying to make inside Bayer is happening — in different forms, at different speeds — across the entire industry. That realization changed how I lead. And how I think about what's possible.

A few things I've come to believe — earned through experience, not arrived at through theory:

I live near Krefeld — close enough to Düsseldorf, the city that shaped me, that I still visit regularly. Close enough to feel connected. Far enough to think clearly.

At home, it's Telse and me, our dog, and a house that feels a little quieter since our two daughters found their own way in the world. I ride gravel, follow the Krefeld Pinguine with the devotion of a season ticket holder, and read — a lot. Not as a hobby. As a practice.

I'm also a certified barista — which means the kitchen metaphor at the heart of the Data Caterer™ framework isn't borrowed. It's lived. Food and coffee are serious passions: the craft of selecting quality ingredients, understanding what someone actually craves, and transforming raw material into an experience worth having. That's not a metaphor for data work. It's the same instinct, applied in a different medium.