Cache hits are simply *chef's kiss*

761M in a day, what were you building?

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.

🔗 Explore the Complete Project GitHub: https://github.com/SajeebTheAnalyst/sunrise-coffee-chain-analytics

If you were advising the management team, which finding would you investigate first—and why?

#DataAnalytics #SQL #PowerBI #Python #DAX #BusinessIntelligence #DataVisualization #OnyxData

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...