The long way
back to data.

I didn’t have a master plan.

I grew up in Milford Haven, Pembrokeshire, in a working-class family. I went to university, studied Maths & Statistics, didn’t put enough work in and got the 2:2 I deserved. When I graduated, I thought a career in analytics was probably off the table.

So I looked elsewhere. The honest reason I found recruitment? I wanted to make money quickly.

Then things went in a circle.

My first recruitment job involved hiring analysts and data people. Over time that became increasingly focused on analytics, STEM and buy-side finance. I spent years recruiting people into the sort of world I’d once assumed I couldn’t enter myself.

Meanwhile, I kept running into the same frustrations inside recruitment: important information was difficult to capture, systems were fragmented, reporting was imperfect and people were being asked to do administrative work that technology ought to make easier.

The problem became more interesting than the job title.

That gradually pulled me towards data, systems and sales technology. Today I lead sales technology in an international, PE-invested business: owning commercial technology, evaluating tools, working across CRM and data, improving reporting, supporting adoption and increasingly embedding AI into how the business operates.

I’m more technical than the average person, but I’m not a software developer or an AI engineer. I don’t pretend to be. I understand the systems well enough to work across APIs, authentication, databases, security and AI tooling. My value is in the space between the business and the technology: understanding the problem, the context and the data, then working out what should actually be built or changed.

I like proving things.

I’m almost obsessive about solving problems. I’ll form a hypothesis from experience, then want the data that proves or disproves it. Sometimes I move too quickly. Sometimes the assumption is wrong. That’s fine. I’d rather test it than turn a gut feeling into company folklore.

I also like building. AI has shortened the distance between having an idea and being able to test it, which means people with deep business context can now create things that previously needed a much bigger technical team just to explore.

Where I’m heading.

I’m interested in making businesses more efficient, not adding technology for the sake of it. That usually starts with data quality, how information is captured and how people actually work. The tools come later.

I think people will remain at the heart of businesses for a long time. AI should make them better informed, more consistent and dramatically more effective — not simply give them another system to log into.