📊 Large-Scale Transaction & Sales Data Cleaning I recently completed a data cleaning project inv...📊 Large-Scale Transaction & Sales Data Cleaning I recently completed a data cleaning project inv...
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I recently completed a data cleaning project involving 533,643 transaction & sales records.
The dataset required standardization and quality checks across multiple fields.
Work performed:
• Transaction number cleaning and standardization
• Customer number standardization
• Product name cleanup and spelling consistency
• Customer name formatting
• Country field cleanup
• Quantity validation and outlier review
• Missing-value identification
• Correction of invalid negative quantity values
• Transaction date validation
The final objective was to produce a more consistent, structured, and analysis-ready dataset.
This project strengthened my practical experience in handling large datasets and performing systematic data quality checks using Excel.
Skills: Data Cleaning | Data Quality | Excel | Data Analysis | Data Standardization
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.