This project presents an interactive Power BI dashboard developed to analyze global sales operations for a fictional company. Leveraging structured relational data, the dashboard highlights key business metrics, uncovers performance patterns, and enables data-driven decision-making across regions, products, and time.
- Power BI Desktop (.pbix)
- Microsoft Excel (data source format)
- Power Query Editor
- DAX (Data Analysis Expressions)
- Data Modeling & Relationships
The analysis is powered by 8 interconnected datasets, each representing a critical business entity:
| Dataset Name | Description |
|---|---|
customers.xlsx |
Customer records including contact info and location |
employees.xlsx |
Employee details and reporting hierarchy |
offices.xlsx |
Company office locations and regions |
orders.xlsx |
Customer orders with status, dates, and references |
order_details.xlsx |
Line-level product data linked to each order |
payments.xlsx |
Customer payment records with method and date |
product.xlsx |
Product catalog including pricing and product codes |
productlines.xlsx |
Product category metadata and descriptions |
- KPI Highlights: Total Sales, Profit, Margin %, and Payment stats
- Top Products: Best and worst performers ranked by revenue
- Product Line Analysis: Sales breakdown by category (e.g., Classic Cars, Motorcycles)
- Year-over-Year Comparison: Sales trends from 2003 to 2005
- Regional Performance: Customer distribution and office-wise revenue
- Employee Metrics: Sales by employee and reporting structure
- Interactive Slicers: Filter data dynamically by year, country, product line, and more
- Star Schema with
ordersas the central fact table - Relationships defined through unique keys (
customerNumber,employeeNumber,productCode, etc.) - Custom DAX measures for KPIs such as Total Profit, Average Order Value, and Sales Growth
- Clone or download the repository.
- Open
classic_models_dataset_dashboard.pbixin Power BI Desktop. - Explore the dashboard using built-in filters and slicers.
- Modify or extend the model based on your analytical needs.
- Add calculated KPIs like Customer Lifetime Value (CLTV)
- Integrate a data refresh pipeline using Power BI Service
- Introduce RFM analysis and clustering for customer segmentation
I’m Satyam Gupta, an aspiring Data Analyst with a strong foundation in Python, SQL, Power BI, and machine learning. I’m passionate about turning raw data into meaningful insights and actionable visualizations that drive smarter decision-making.