I started at ground level. Working directly with customers, understanding what breaks when processes fail, seeing what actually drives revenue and retention. You learn fast what matters and what's just noise.

Then I moved into work that required me to see the bigger picture. Coordinating across teams, functions, and boundaries. Managing competing priorities and conflicting stakeholder needs. That taught me to understand how different pieces fit together and what cascades from one decision to another.

Then I learned to use data to answer the questions I'd been asking manually for years. Each step built on the last. It wasn't a departure, it was all the same underlying work: understanding complexity, spotting what matters, connecting the dots so things actually change. Most organisations have a gap between the people who understand operations and the people who understand data. Operators make intuitive calls. Analysts produce reports that sit unread in inboxes.

I live in both worlds. I understand what operational teams actually care about: the real frictions, the constraints that shape decisions, the things that drive behaviour. And I'm fluent enough in data to know when something is genuinely important versus when it's just statistical noise. That means I can ask the right questions, find the patterns that actually matter, and explain them in a way that makes people act. I don't do analysis for its own sake. Every question connects to something real. Every insight has a next step.

I design metrics that change how teams operate, not vanity numbers that look good in a dashboard. I spot inefficiencies that others walk past every day. I can look at a problem from multiple angles and use data to figure out which lever actually moves things.

But fundamentally, I'm solving a pattern recognition problem. The patterns are usually there. You just need to know where to look, how to read them, and how to tell the story so people understand why they matter.