Most data portfolios never make one decision: what to stop building.

What shaped your perspective

A few years ago I was sitting near the pass at a Michelin-starred restaurant, watching thirteen dishes leave the kitchen in silence. No one was running. No raised voices. When a dietary restriction came in mid-service, the kitchen adjusted and kept moving.

I've spent fifteen years building and running data organizations. I had never once seen a team that looked like that.

The kitchens that worked weren't further along some maturity curve. They were calm because of a decision that happens before anyone touches a pan: someone decides, every single day, what doesn't go on the menu. Most data portfolios never make that decision. They just keep adding dishes. The team doesn't get worse. They get excellent at saying yes. And those aren't the same skill.

That's the pattern I've been working to name and address for the last decade.

What have you actually done

I've built and led data organizations in companies ranging from early-stage startups to large European marketplaces. I've worked as analyst, builder, product leader, and executive inside the systems I now help organizations think through. Seeing the same pattern from each of those positions is what shaped the way I work.

At idealo I ran a 35-person data product organization. We rebuilt how the team made portfolio decisions from the inside. Reducing annual infrastructure spend from seven figures to five while the team shifted from firefighting to deliberate product thinking.

At Zalando I led a data transformation in thirty weeks. The team wasn't the problem. They were capable. What was missing was a view of where effort was actually going versus where leadership assumed it was. Automation and process redesign eliminated over €200,000 in annual operational cost. More importantly, it gave the leadership team a number they could own.

Across fifteen years, what I kept encountering wasn't a talent problem or a technology problem. It was an economics problem: organizations were funding data without a shared language for what they were getting back. That realization changed how I work. Today I help leadership teams understand their data investments in economic rather than purely technical terms.

How do you think

I think in systems rather than in parts. A data portfolio is non-linear. Small decisions compound, capacity locks invisibly, and a team can enter a state where all their effort is sustaining the current load with nothing left to improve it. Most frameworks assume you can optimize your way out of that. In my experience, you can't. You need a different view first.

I don't see data teams as service organizations. I see them as investment portfolios, with assets that earn their keep, assets that are quietly draining everything around them, and a CEO who is almost always operating without the right instrument to tell the difference.

I don't start with dashboards, maturity models, or tooling. I start with three questions:

• Where is the team's capacity actually going. Not where the sprint plan says it's going?
• What is each product in the portfolio actually costing. Not the hosting bill, but the full carrying cost including incidents, support, and the organizational workarounds that exist because the product doesn't quite fit how people work?
• What is the business paying to bridge the gap between what the data team produces and what the business actually needs?

Those three questions, answered honestly, produce a picture most CEOs have never seen about their own data investment. Not because the information was hidden. Because nobody had built an instrument designed to surface it.

What is it like to work with me

I don't validate assumptions. I test them against the number your CFO can actually audit.

The first weeks of any engagement produce the same thing, regardless of the program: a precise figure for what's currently unaccounted for — hours, carrying cost, or the gap between what's built and what's needed. Not a slide deck. A number your team can defend.

From there we sit across from your leadership team, not behind a report: naming which products would get cut if the real cost were visible, without treating it as a verdict.

I work with a small number of organizations at a time. Not for scarcity — because this kind of work requires being in the room when the decisions actually happen, not reading about them afterward.

Who should contact you

CEOs and CFOs at Series B to D companies that have already invested in data and are beginning to ask a harder question: what business value are we actually getting from that investment?

Not companies that haven't started yet. Not teams looking for tooling advice or an implementation partner.

Companies where the data team is working hard, the budget keeps growing, and the conversation with the CFO is getting harder to have. Because nobody in the room has a financial view of what the portfolio is worth.

As investors increasingly ask not whether companies have data teams, but what return those teams generate, these conversations are becoming unavoidable. If you're having one of them, this is what I'm built for.