Learning Journey

Clear data doesn't always change people's minds. A look at how psychology, existing beliefs and business context influence the way we interpret evidence and make decisions.

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.

Changing careers doesn't have to mean starting from zero. A reflection on why I don't want my previous career to disappear, and how banking, teaching and running a business continue to shape the analyst I'm becoming.

Changing careers into data taught me that learning SQL was not the hardest part. The real challenge was learning how to connect years of valuable experience with a profession that often recognises experience only when it comes with the "right" job title.

A reflection on how working in target-driven environments changed the way I look at KPIs, reporting and performance data.

A practical reflection on why data analysis doesn’t start with data — but with unclear business problems that need to be understood first.

A reflection on why reporting alone is not enough - and why the real value of analysis starts when decisions are made.

A personal reflection on entering data analytics - and why experience doesn’t always “count” until it has the right job title.

A practical reflection on how stakeholder requests often differ from actual business needs — and why understanding the problem matters more than delivering the output.

When data doesn’t give a clear answer, the goal is not to force one - but to make the decision clearer by showing trade-offs, risks and possible scenarios.

A reflection on why “junior” doesn’t mean “inexperienced” and how diverse backgrounds strengthen analytical teams.

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.

Good data analysis isn’t about analysing everything. It’s about understanding what truly matters and knowing what to leave out.

For me, being a data analyst is about asking the right questions, understanding context, and turning data into meaningful insights.

As part of my data and business analysis learning path, I completed two very different certifications: BCS Business Analysis Foundation and CompTIA Data+.

When I started learning data analysis, I thought the hardest part would be Python or statistics. I was wrong. The hardest part was knowing where to start , what actually matters , and how not to get lost in the data. Over time, through courses and hands-on projects, I developed a simple approach that helps me stay focused and make sense of what I’m analysing. This post sums up how I usually work. 1. Defining the Problem First (Before Touching the Data) Before opening Excel,