Hey data analysts! How many of you use Google OpenRefine for data reconciliation? This gem of a tool has been in my toolbox for a number of years and it's always a delight when a project lands on my plate that necessitates using it.
Most recently, I normalized hundreds of thousands of records representing businesses in my state broken down by county. Using built-in features, I enriched the data set with NAICS code mappings, Geo data which I extrapolated lat/long coordinates from, and cross-referenced additional data from several other relevant data sets. The exercise also surfaced compelling stats via its powerful faceting functionality that I can use to describe the data set when it is put to use.
If you use OpenRefine, where does it fit into your process and what do you use it for?
If you're new to it, here's a link so you can check it out! https://openrefine.org
One business problem. Three analytical routes. One data-driven story. ☕📊
I turned raw business data into actionable decisions—not just another Power BI dashboard.
For the Onyx Data October 2026 Challenge, I worked on the Sunrise Coffee Chain analytics project, analyzing store performance, profitability, customer loyalty, revenue mix, and trading patterns across 60 UK stores (2023–2024).
🔹 My Analytical Approach
1️⃣ Python — Data Preparation
Cleaned and prepared the source datasets to establish a reliable foundation for analysis.
2️⃣ SQL — Business Analysis
Investigated store-level profitability, cost pressure, customer loyalty, revenue contribution, and trading patterns.
3️⃣ Power BI — Interactive Dashboards
Built a 3-page interactive report featuring advanced DAX measures, KPI cards, YoY comparisons, profitability analysis, and business-focused visualizations.
📊 Key Findings
• £89.7M Revenue | £16.9M Net Profit
The business recorded an aggregate net profit margin of 18.8% across the analysis period.
• Store Location Matters
Transport-Hub stores achieved a 25.1% aggregate net profit margin, compared with 18.6% for High-Street stores.
• An Area Requiring Attention
South London Corner recorded a £50.5K net loss over 24 months.
• Product Revenue Contribution
Espresso contributed 45.7% of daily revenue, while food contributed 26.1%.
• Trading Patterns
Average daily revenue was approximately 24% lower on weekends and 33% lower on UK bank holidays than on regular trading days.
💡 My Biggest Takeaway
Revenue alone does not tell the whole story. Understanding profitability, cost pressure, product contribution, and trading patterns helps businesses identify where to improve performance and make better decisions.
I would investigate South London Corner first. Break the 24-month loss into store-month revenue, direct costs and allocated costs, then compare it with similar stores over matched trading periods. Reconcile at store-month grain before joining product or loyalty tables so a...
I developed an Excel-based inventory management solution to organize product records, monitor stock levels, and simplify inventory tracking. The system helps users maintain structured data, review stock information, and improve day-to-day inventory management through spreadsheet automation and clear reporting.