I didn't stumble into data. I'd been working with it every week, I just didn't call it analytics yet.
I began my career as a content strategist and social media manager. On paper, the job was about ideas, captions and content calendars. In practice, every week ended the same way: pulling performance numbers, lining them up in a spreadsheet, and asking three questions. What worked? What didn't? And why?
One post would take off while the next went flat. An audience that loved one format ignored another. Clients didn't just want content, they wanted proof it was working. So I got into the habit of treating every campaign like a small experiment: track it, compare it, adjust it.
Somewhere between those weekly reports, I noticed that the part of the job I looked forward to most wasn't publishing. It was the review. Finding the pattern behind a spike. Spotting the one metric everyone was overlooking. Turning a messy export into a clear recommendation someone could act on.
Platform dashboards only went so far. I wanted to clean the data myself, query it, model it and build the dashboard, not just read someone else's.
So I enrolled in a data analytics bootcamp and started building the toolkit properly:
I learned fastest by doing. Since then I've worked through projects in very different worlds: microfinance customers and credit risk, healthcare records, e-commerce profitability, global COVID-19 trends and African export markets. Each one started with messy data and ended with something a decision-maker could use.
My background is my advantage. A Business Administration degree taught me how organisations think about performance, cost and risk. Working in content taught me to explain things simply and lead with the "so what". Data analytics gives me the evidence.
That mix means I don't stop at a chart. I ask what the business needs to decide, find the answer in the data, and present it in a way people will actually read.