One business problem. Three analytical routes. One data-driven story. โ๐
I turned raw business data into actionable decisionsโnot 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 (2023โ2024).
๐น My Analytical Approach
1๏ธโฃ Python โ Data Preparation
Cleaned and prepared the source datasets to establish a reliable foundation for analysis.
2๏ธโฃ SQL โ Business Analysis
Investigated store-level profitability, cost pressure, customer loyalty, revenue contribution, and trading patterns.
3๏ธโฃ Power BI โ Interactive Dashboards
Built a 3-page interactive report featuring advanced DAX measures, KPI cards, YoY comparisons, profitability analysis, and business-focused visualizations.
๐ Key Findings
โข ยฃ89.7M Revenue | ยฃ16.9M Net Profit
The business recorded an aggregate net profit margin of 18.8% across the analysis period.
โข Store Location Matters
Transport-Hub stores achieved a 25.1% aggregate net profit margin, compared with 18.6% for High-Street stores.
โข An Area Requiring Attention
South London Corner recorded a ยฃ50.5K net loss over 24 months.
โข Product Revenue Contribution
Espresso contributed 45.7% of daily revenue, while food contributed 26.1%.
โข Trading Patterns
Average daily revenue was approximately 24% lower on weekends and 33% lower on UK bank holidays than on regular trading days.
๐ก 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.
One business problem. Three analytical routes. One data-driven story. โ๐
I turned raw business data into actionable decisionsโnot just another Power BI dash...