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

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,