I’m a Norwegian based in London. I’ve spent fifteen years building the data infrastructure that sits underneath commercial outcomes, mostly inside large organisations that didn’t have it yet.
The way I tend to work is hands-on. I don’t consult at arm’s length. I get into the schema, write the technical requirements, sit in the rooms where engineering and commercial stakeholders disagree, and make sure the data infrastructure that gets built is the kind that actually works. My hunch is that this combination, the commercial instinct and the technical fluency together, is fairly rare. It’s what I keep being hired for.
At wagamama, I took the business from zero first-party data to a running loyalty platform over five years. That meant manually migrating six brand databases, building attribution models from scratch when no tools existed, and scaling ROAS from 2:1 to 5:1 by identifying the conversion signals that others had missed. At the WTA, I owned the Customer Data Platform as the intelligence layer for a global sports property with over a billion fans, designed the shared data model and consent framework that enabled a multi-million dollar partnership, and secured six-figure infrastructure investment by connecting technical capability to business outcomes (Bloomreach case study, Vecton write-up).
I’m now working independently on what I think is the most interesting version of this problem: how organisations need to rethink data infrastructure for an AI-first world. AI systems don’t read your website the way humans do. They read your schema, your structured data, your context signals. If those aren’t right, you don’t exist in AI’s answers. I call the practice of getting them right Context Architecture.
I write about this on the Thinking page. If you want to work together, the Work with me page is the right place to start.
