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27cbe7a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 | # 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.
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