nyanibaquartey/OrderManagementDatabase

Analyzing sales data to derive insights into sales trends, customer behaviors, and product performance using a PostgreSQL database on Azure Data Studio.

Order Management Database and Analysis

Analyzing sales data to derive insights into sales trends, customer behaviors, and product performance using a PostgreSQL database on Azure Data Studio.

Table of Contents

• Project Overview
• Dataset
• Tools & Technologies Used
• Queries and Analysis
• How to Run the Analysis
• Future Enhancements

Project Overview

1. Initial Data Handling with Excel:

   • Enhanced naming conventions.
   • Data exploration for trends and outliers.

2. Entity Relationship Diagram (ERD):

   • ERD for database schema design.

3. Database Setup:

   • PostgreSQL in Azure Data Studio.
   • Data ingestion from CSV.

4. Data Analysis:

   • SQL queries for analytical insights.

Dataset

• Source: Sample Sales Data on Kaggle.
• Features: Order & shipment, customer details, product pricing.

Tools & Technologies Used

• Excel: Preliminary data handling.
• Azure Data Studio: Database setup and hosting.
• PostgreSQL: Data storage and querying.
• Python: Data processing and DB insertion.

Queries and Analysis

• Top 10 most-ordered products.
• Average order size.
• Top customers by amount ordered.

How to Run the Analysis

1. Initialize PostgreSQL on Azure Data Studio.
2. Establish tables with provided schemas.
3. Employ Python for CSV data insertion.
4. Execute SQL queries for analysis.

Future Enhancements

• Integration with visualization tools (e.g., Tableau, Power BI).
• Predictive modeling for sales forecasting.