n/aI’d start by sitting down with the business stakeholders to properly understand what questions they’re trying to answer and what success looks like. That context is crucial before touching any data.
From there, here’s how I’d approach building an end-to-end reporting solution in Power BI:
1. Data Extraction
I’d use SQL to extract the raw data from the source database or Fabric environment. I prefer writing efficient, well-structured queries — selecting only the columns I need, applying filters early, and using CTEs or window functions where necessary to keep the initial pull as clean and lightweight as possible.
2. Data Transformation
I’m a big believer in doing most of the heavy lifting here. Depending on the complexity, I’d use either Power Query (for simpler cases) or Python with Pandas in a Jupyter notebook or script. Python gives me more flexibility for complex cleaning, feature engineering, or joining messy datasets. For example, in my dissertation project I worked with over 9.5 million sensor readings and unified multiple sources — so I’m comfortable handling large, imperfect data. I always aim to create a clean, modelling-ready dataset with consistent formats, proper data types, and clear naming conventions.
3. Data Modelling
Once in Power BI, I’d build a proper star schema — fact tables for the core metrics and dimension tables for context (dates, customers, products, etc.). I pay close attention to relationships, cardinality, and removing unnecessary columns to keep the model lean. I also create a proper Date table and use it for all time intelligence.
4. DAX Calculations
I’d build reusable DAX measures for key metrics rather than calculated columns where possible, to keep performance strong. Things like Year-over-Year growth, rolling averages, or percentage of total — written clearly with good comments so others can understand them. I always test measures against raw SQL queries to validate accuracy.
5. Dashboard Design & Delivery
I’d design the dashboard with the user in mind — keeping it clean, intuitive, and focused on the most important insights first. I use bookmarks, drill-throughs, and tooltips to make it interactive without overwhelming the page. Before releasing it, I do thorough testing for data accuracy, run performance checks (using Performance Analyzer), and apply best practices like incremental refresh for larger datasets.
Finally, I’d set up proper documentation, data refresh schedules, and ideally some basic row-level security if needed. Throughout the process, I keep communication open with stakeholders to make sure the final product actually drives decisions.
