Years of teaching taught me that understanding something and explaining it are two very different skills. I'm discovering that the same lesson applies to data: good analysis only becomes useful when the person receiving it can understand what it means.

I tracked 298 job applications over seven months. When I finally treated my job search like a dataset, the results challenged several of my assumptions - and changed the question I was asking.

For me, SQL is not just about extracting data — it’s a way of turning vague business questions into clear, testable insights.

A good Power BI dashboard isn’t about showing everything - it’s about highlighting what matters and supporting clear, confident decisions.

Before dashboards or visuals, I use Excel as a thinking space - to explore data, test assumptions and understand the problem I’m trying to solve.

Before opening any tools, I focus on understanding the problem, the decision behind it and what “good enough” really means for a data analysis project.

Being effective in data analysis isn’t about knowing everything - it’s about asking the right questions and focusing on decisions that matter.

At the beginning, I thought mistakes meant I wasn’t good enough. Over time, I realised they were the moments that taught me the most about real data analysis.