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This project explores how clear, practical MI reporting can support day-to-day operational decisions by making workload, backlog and service performance easier to understand.

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.

A Business Analysis case study exploring how attendance patterns, capacity utilisation and staffing decisions influence day-to-day operations in a UK nursery.

A practical look at how I use Python in data analysis when Excel is not enough — from scenario testing to deeper analytical insights.

A small data analysis project comparing Data Analyst job postings in the UK and Poland using LinkedIn data. The Tableau dashboard explores competition levels, experience expectations and the most commonly requested analytical tools.

A data analysis project exploring how discount levels influence profitability using Python and a Power BI dashboard to identify an optimal discount strategy.

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

A clinical trial case study exploring how statistical analysis translates into clear, decision-ready conclusions.

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.

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+.

Why I worked on this project Sales data is everywhere. Revenue charts look great in presentations. But one question kept coming back while I was learning and working with commercial data: Does high sales actually mean good performance? In many businesses, revenue becomes the main success metric - often without fully understanding what sits underneath.. This project was my way of stepping back and asking a more uncomfortable, but far more useful question: Where does profit rea

The more I worked with customer data, the clearer it became that the “average customer” rarely exists. This project focuses on understanding real shopping behaviours - not just summary metrics.

E-commerce data can look impressive at first glance - until you step back and ask where revenue really comes from. In this project, I explored sales concentration, seasonality, and customer contribution to understand what truly drives online revenue.

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,