This is an end-to-end Data Analytics project that analyzes business performance across different regions, product categories, and customer segments. The project transforms raw business data into meaningful insights using Excel, SQL, PostgreSQL, and Power BI.
- Analyze sales and profit performance
- Identify high-risk business areas
- Monitor monthly sales trends
- Evaluate customer segment performance
- Discover profitable product categories
- Generate actionable business insights
- Microsoft Excel (Data Cleaning)
- SQL
- PostgreSQL
- Power BI
- DAX
- Window Functions
- CTEs
cleaned.sales.data.csv– Cleaned datasetraw.data.csv– Original datasetsql_queries/– SQL queries for business analysisdashboard.image.png– Dashboard previewLICENSE– MIT License
- Removed duplicate records.
- Handled missing values.
- Fixed inconsistent date formats.
- Prepared clean data for analysis.
Performed business analysis using PostgreSQL with:
- CTEs
- Window Functions (
RANK,LAG,SUM OVER) - Customer Analysis
- Product Performance Analysis
- Month-over-Month Sales Analysis
- Pareto Analysis
- Risk Analysis
Created an interactive Power BI dashboard with:
- KPI Cards
- Dynamic Filters
- Monthly Sales Trends
- Regional Performance
- Customer Segment Analysis
- Product Category Analysis
- Risk Distribution
- South recorded the highest number of high-risk orders.
- West and North generated the highest overall sales.
- Furniture and Office Supplies contributed the highest revenue.
- Sales declined during the final months.
- High sales do not always result in high profit.
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- Data Cleaning
- SQL
- PostgreSQL
- Power BI
- DAX
- Window Functions
- KPI Reporting
- Data Visualization
- Business Intelligence
This project demonstrates how raw business data can be transformed into actionable business insights through data cleaning, SQL analysis, and interactive Power BI dashboards.
Last Updated: September 2026 This project demonstrates how business data can be transformed into meaningful insights using Excel, SQL, PostgreSQL, and Power BI. It highlights practical skills in data cleaning, analysis, visualization, and dashboard development to support data-driven business decisions.