This project focuses on analyzing business sales data using Power BI to understand performance trends, identify top-performing products, and uncover areas for improvement. The goal is to support data-driven decision-making for business growth.
- Power BI (Data Visualization)
- Excel / CSV Dataset
- Data Cleaning & Transformation
- DAX (Data Analysis Expressions)
The dataset used is the Superstore dataset, which includes:
- Order Date
- Product Name
- Category
- Sales
- Profit
- Region / State
- Quantity
The dashboard includes the following key components:
-
KPI Cards:
- Total Sales (2.30M)
- Total Profit (2863K approx.)
- Total Orders (5K)
- Average Profit Margin (12.03%)
-
Visualizations:
- Sales Trend over Time (Line Chart)
- Sales by Category (Bar Chart)
- Top 10 Products by Sales (Bar Chart)
- Sales vs Profit by Product (Scatter Plot)
- Sales by State (Distribution View)
- Category Filter (Slicer)
- Technology category generates the highest sales among all categories.
- A few products show high sales but low or negative profit, indicating heavy discounting or cost issues.
- Sales trend shows fluctuations over time, suggesting seasonal or demand variation.
- Some states contribute significantly more to overall sales compared to others.
- Focus on high-performing categories like Technology to maximize revenue.
- Reduce discounts or optimize pricing for low-profit products.
- Promote high-margin products to improve profitability.
- Expand business operations in top-performing states.
This project demonstrates how data analytics can help businesses understand performance, identify problems, and make informed decisions using Power BI.