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| # Adventure Works Sales Performance Analysis | |
| ## 📌 About the Project | |
| This Power BI project provides a comprehensive analysis of the **Adventure Works** retail data. It features a modern, high-contrast interface designed for executive-level decision-making, focusing on sales trends, product performance, and regional metrics from 2011 to 2014. | |
| ## 🛠️ Technical Stack | |
| * **Power BI Desktop**: Dashboard design and visualization. | |
| * **Power Query (M)**: Data extraction, cleaning, and transformation (ETL). | |
| * **DAX (Data Analysis Expressions)**: Created measures for dynamic KPIs (Max Due, Sub Total, Total Orders). | |
| * **Data Modeling**: Implementation of a Star Schema for optimized reporting performance. | |
| ## 📊 Project Architecture & Design | |
| ### 1. The Data Model (Star Schema) | |
| <img src="./assets/data_model.png" width="100%" height="450" alt="Home Dashboard" style="border-radius: 20px; object-fit: cover;"> | |
| To ensure high performance and accurate filtering, the project utilizes a **Star Schema**. A central **FactTable** is linked to specific Dimension tables—`Shipping_DIM`, `Product_Dim`, `Status_DIM`, and `Territory_DIM`—enabling seamless cross-filtering and simplified DAX calculations. | |
| ### 2. Executive Sales Dashboard | |
| <img src="./assets/overview.png" width="100%" height="450" alt="Home Dashboard" style="border-radius: 20px; object-fit: cover;"> | |
| The primary dashboard provides a 360-degree view of business health: | |
| * **Top-Level KPIs**: Real-time tracking of Max Due ($26.73K), Sub Total ($30.09M), and Total Orders (1,465). | |
| * **Trend Analysis**: A "Yearly Orders" area chart visualizing the growth trajectory peaking in 2013. | |
| * **Regional Distribution**: Bar charts identifying **Canada** and the **Northwest** as the highest-performing territories. | |
| * **Status Monitoring**: Categorization of orders by status (Approved, In Process, Shipped) to monitor the supply chain. | |
| ## 🚀 Key Insights | |
| * **Volume Drivers**: "Bikes" represent the highest quantity category, followed by Clothing. | |
| * **Seasonal Trends**: Quarter 3 consistently shows the highest order distribution at **36%**. | |
| * **Global Reach**: Analysis across 4 continents and 10+ specific territories. | |
| ## ⚙️ Data Processing (ETL) | |
| 1. **Cleaning**: Standardized data types and handled missing values in the Fact Table. | |
| 2. **Transformation**: Optimized column headers and removed redundant metadata. | |
| 3. **Modeling**: Established 1:Many relationships to ensure filter integrity across all visuals. | |