Articles

From Accounting to Analytics: My Career Transition

When I first started studying Certificate IV in Accounting at Chisholm Institute, I never imagined I'd end up passionate about data analytics. My path from accounting to analytics wasn't a sudden leap — it was a gradual discovery that I loved the "why" behind the numbers even more than the numbers themselves.

The Spark

Working through my Diploma and Advanced Diploma of Accounting, I noticed that the most interesting parts of my work weren't the journal entries or reconciliations — they were the moments when I could use data to explain what was happening in the business. Why were expenses up this quarter? Which division was driving revenue growth? These questions pulled me towards analytics.

Making the Leap

Enrolling in a Bachelor of Business Analytics at Deakin University was the best decision I made. The degree gave me structured exposure to tools like Power BI, Python, and SQL — but more importantly, it taught me to think analytically. I learned to frame business problems, gather the right data, and present findings in ways that non-technical stakeholders could act on.

What I'd Tell Others

If you're in accounting or finance and feel drawn to data, you already have a massive advantage. You understand business context, financial statements, and the importance of data accuracy. Those foundations are incredibly valuable in analytics. My advice: start with Excel (you probably already know it), then learn Power BI, and gradually pick up Python. The transition doesn't have to happen overnight.

Building Effective Power BI Dashboards: Lessons from Real Projects

After building Power BI dashboards across multiple industries — from construction project finance to energy consumer surveys — I've learned that the best dashboards aren't the flashiest ones. They're the ones people actually use. Here are the key lessons I've picked up along the way.

Start with the Question, Not the Data

The biggest mistake I see is jumping straight into building visuals before understanding what decisions the dashboard needs to support. Before opening Power BI, I always ask: "What question does this dashboard need to answer?" and "Who will use it and how often?" A dashboard for a CEO reviewing monthly performance looks very different from one for a project manager checking daily costs.

Less is More

Early on, I made dashboards with 15+ visuals on a single page. The feedback was always the same: "It's too busy." Now I aim for 4–6 visuals per page maximum, with clear titles and consistent formatting. If you need more detail, create drill-through pages rather than cramming everything onto one screen.

DAX is Your Superpower

Learning DAX properly was a game-changer. Simple measures like year-over-year comparisons, running totals, and percentage-of-total calculations are what make dashboards genuinely useful. I recommend starting with CALCULATE, FILTER, and time intelligence functions — they cover 80% of what you'll need.

Always Get Feedback Early

I never build a dashboard in isolation anymore. After the first draft, I share it with stakeholders and ask: "Does this answer your question?" and "Is anything confusing?" This saves hours of rework and builds trust with the people who'll rely on your work.

Getting Started with Python for Business Analysts

If you're a business analyst who's comfortable with Excel and Power BI but curious about Python, this article is for you. I was in the same position not long ago, and I want to share what I wish I'd known when I started.

Why Python?

Excel is powerful, but it has limits. When your dataset grows beyond 100,000 rows, when you need to automate repetitive cleaning tasks, or when you want to build predictive models — that's where Python shines. It's not about replacing Excel; it's about having another tool in your belt for when you need it.

Start with Pandas

Pandas is the Python library that will feel most familiar to Excel users. Think of a Pandas DataFrame as a spreadsheet you can manipulate with code. Start with these operations: reading CSV files (pd.read_csv()), filtering rows, grouping data (groupby()), and creating new calculated columns. If you can do VLOOKUP and pivot tables in Excel, you can learn Pandas.

Visualisation with Matplotlib

Once you can manipulate data, the next step is visualisation. Matplotlib (and its friendlier cousin Seaborn) let you create charts directly from your data. The big advantage over Excel charts? They're reproducible. Run the same script next month with new data and your charts update automatically.

My Recommended Learning Path

Install Anaconda (it comes with everything you need), start with Jupyter Notebooks (interactive and forgiving), and work through a real dataset you care about. I used the Online Retail II dataset for my first serious project, and having a real goal kept me motivated far better than textbook exercises.

Why Data Visualisation Matters More Than You Think

I used to believe that good analysis speaks for itself. Run the numbers, find the insight, and people will understand. I was wrong. The truth is, the way you present data matters just as much as the analysis itself.

The Presentation Gap

During my capstone project with Energy Consumers Australia, we had fantastic insights buried in complex charts that nobody outside our team could interpret. It wasn't until we simplified our visuals — using clear titles, consistent colours, and plain-language annotations — that our recommendations gained traction with stakeholders.

Three Rules I Follow

First, every chart needs a clear title that tells the viewer what they should take away — not just what the chart shows. "Revenue grew 15% in Q3" is better than "Quarterly Revenue." Second, use colour intentionally. Highlight the one thing that matters rather than making everything colourful. Third, remove clutter. Gridlines, borders, and 3D effects almost always make charts harder to read, not easier.

The Business Impact

At work, I've seen the difference firsthand. A well-designed dashboard doesn't just inform — it drives action. When senior management can glance at a visual and immediately understand the story, decisions happen faster. That's the real value of good data visualisation: it bridges the gap between analysis and action.