This project is a Power BI dashboard built on the Sample Superstore dataset.
The goal is to identify which products, regions, categories, and customer segments are most profitable and which should be reconsidered.
- Source: Sample Superstore dataset
- Period: 2014 – 2017
- Fields: Row ID, Order ID, Order Date, Ship Date, Ship Mode, Customer ID, Customer Name, Segment, Country, City, State, Postal Code, Region, Product ID, Category, Sub-Category, Product Name, Sales, Quantity, Discount, Profit
- Link: Superstore Dataset on Kaggle
- Profit Margin:
[Profit] / [Sales] - Order/Ship Date (Year, Month, Date): Split [Order Date] and [Ship Date] into [Year], [Month], [Date]
- Total Sales
- Total Profit
- Profit Margin (%)
- YoY Sales Growth
- Data cleaning: Power Query
- Visualizations: Line chart, Bar chart, Donut chart, Table, Map, KPI Cards
- Slicers: allow users to filter by Time, Region, and Category
- Drill-down by Year/Quarter/Month
- Total Sales: $2.30M across 2014–2017, with +20.34% YoY growth in 2017 vs 2016
- Sales Seasonality: Sales consistently peak in Q4, highlighting strong seasonal demand
- West Region: leading region with the highest sales contribution (~32%) and strong profit performance
- Technology: top-performing category (36% of sales, 17.4% profit margin)
- Furniture: lowest margin (2.49%) → opportunity for supplier negotiation or product mix optimization
- Power BI Desktop
- DAX (Data Analysis Expressions)
- Power Query (data transformation & cleaning)
⭐ This project is part of my learning journey in data analytics with Power BI and demonstrates my ability to transform raw data into actionable business insights.