In many companies, analysts spend hours manually preparing weekly or monthly performance reports. In this project, I built a simple automated data pipeline that processes sales data, calculates important business KPIs, and generates a report automatically using Python.
The goal of this project is to simulate how businesses automate data extraction, data processing, KPI generation, and reporting workflows.
- Automated data processing using Python and Pandas
- Calculation of important business KPIs
- Automated report generation
- Structured project workflow similar to real-world data pipelines
- Reproducible analytics pipeline
- Python
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Jupyter Notebook
Raw Data
↓
Data Extraction
↓
Data Validation
↓
Data Transformation
↓
KPI Calculation
↓
Report Generation
The pipeline calculates key business metrics such as:
- Total Revenue
- Total Orders
- Average Order Value
After running the pipeline, a report is automatically generated here:
reports/business_report.csv
Clone the repository:
git clone https://github.com/prabandkumar/automated-business-reporting-data-pipeline.git
Install the required libraries:
pip install -r requirements.txt
Run the pipeline:
python scripts/run_pipeline.py
The script processes the data and automatically generates the business report.
| Metric | Value |
|---|---|
| Total Revenue | Calculated |
| Total Orders | Calculated |
| Average Order Value | Calculated |
- Generate Excel reports with charts
- Integrate Power BI dashboards
- Automate email report delivery
- Use workflow orchestration tools like Airflow