An interactive business intelligence dashboard built with Python and Dash, analyzing global retail performance across markets, segments, and product categories. Built as part of a BI Developer portfolio alongside Power BI projects.
Free tier — may take ~50s to wake up on first load.
This dashboard provides a full business overview of the Global Superstore dataset, with dynamic filters and KPI cards that update in real time based on user selections.
- Total Revenue, Total Profit, Profit Margin, Number of Orders
| Section | Description |
|---|---|
| Revenue & Profit Over Time | Monthly trend lines for revenue and profit |
| Sales by Market | Revenue breakdown by geographic market |
| Sales by Segment | Consumer, Corporate, and Home Office comparison |
| Top 10 Products | Best-performing products by revenue |
| Top 10 Countries | Leading countries by revenue |
| Profit Margin by Category | Margin comparison across product categories |
| Order Priority Distribution | Share of orders by priority level |
| Shipping Mode Analysis | Revenue and margin by delivery method |
- Dash / Plotly — interactive charts and reactive layout
- Pandas / NumPy — data cleaning and aggregation
- Gunicorn — production WSGI server
Global Superstore — fictional retail dataset widely used in BI exercises.
The dataset (Global_Superstore2.csv) covers orders from 2011 to 2014 across 147 countries.
The dataset is not included in this repository due to file size. Place
Global_Superstore2.csvin the project root before running.
# 1. Clone the repository
git clone https://github.com/thigmen/global-superstore-dashboard.git
cd global-superstore-dashboard
# 2. Install dependencies
pip install -r requirements.txt
# 3. Place Global_Superstore2.csv in the project root
# 4. Run the app
python dashboard.pyThen open http://127.0.0.1:8050 in your browser.
- Business Intelligence: KPI monitoring, revenue/profit analysis, market segmentation
- Reactive UI: Dash callbacks updating 8+ charts simultaneously from shared filters
- Data aggregation: Group-by operations, time series resampling, margin calculations
- Data visualization: Consistent dark-theme design with Plotly Express and Graph Objects
global-superstore-dashboard/
├── dashboard.py # Main Dash application
├── requirements.txt
└── Procfile # Gunicorn entry point for deployment