📊 Blinkit Sales Analysis – SQL & Power BI Project

This project is an end-to-end data analytics case study on Blinkit sales data.
It demonstrates how raw data can be transformed into meaningful business insights using SQL and Power BI.

The workflow followed in this project is:

Raw Data → SQL Cleaning & Analysis → Power BI Dashboard → Business Insights

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🎯 Project Objective

To analyze Blinkit’s sales data and uncover insights related to:

• Overall business performance
• Product-level trends
• Customer preferences
• Impact of outlet size, type, and location on sales
• Growth patterns over time

The goal is to help stakeholders make data-driven decisions about inventory, outlet expansion, and product strategy.

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🛠 Tools & Technologies

SQL Server (SSMS)

• Data cleaning
• KPI calculations
• Business queries
• Conditional aggregation & subqueries

Power BI

• Interactive dashboards
• Visual storytelling
• KPI cards & charts

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📂 Repository Structure

Blinkit-Sales-Analysis/ │ ├── data/ │ └── blinkit_data.csv # Raw dataset │ ├── blinkit_analysis.sql # All SQL queries used in analysis │ ├── Blinkit_Dashboard.pbix # Power BI dashboard file │ └── README.md

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🗄 SQL Analysis

Before building the Power BI dashboard, I performed full data exploration and analysis using SQL.

Key steps:

• Cleaned and standardized inconsistent values in Item_Fat_Content
• Calculated overall KPIs:
  • Total Sales
  • Average Sales
  • Number of Items
  • Average Rating
• Analyzed sales by:
  • Item Fat Content
  • Item Type (Top 5 products)
  • Outlet Size (with percentage contribution)
  • Outlet Type
  • Outlet Location
  • Outlet Establishment Year
• Concepts used:
  • GROUP BY, CASE, SUM, AVG, COUNT, ROUND
  • Subqueries for percentage calculations
  • Conditional aggregation for category-wise metrics

All SQL queries are available in:
📄 blinkit_analysis.sql

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📊 Power BI Dashboard

The Power BI dashboard visualizes the insights generated using SQL:

KPI Cards:

• Total Sales
• Average Sales
• Number of Items
• Average Rating

Visuals include:

• Sales by Item Type
• Sales by Outlet Size & Type
• Sales by Location Tier
• Trend by Establishment Year
• Fat Content comparison (Low Fat vs Regular)

Dashboard Preview:

The dashboard allows users to:

• Quickly understand performance
• Compare categories
• Identify high-performing outlets and products

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🔍 Key Business Insights

• Low Fat items contribute a significant share of total sales.
• Medium-sized outlets generate the highest revenue contribution.
• Certain outlet types outperform others in both revenue and ratings.
• Newer outlets show competitive sales performance compared to older ones.
• A small set of item types drives a large portion of total revenue.

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🚀 What This Project Demonstrates

This project showcases my ability to:

• Clean and standardize real-world data
• Write structured and optimized SQL queries
• Perform business-focused data analysis
• Translate raw numbers into insights
• Build professional dashboards in Power BI
• Follow an end-to-end analytics workflow

📌 This project is designed to reflect how real-world data analyst tasks are performed, making it suitable for fresher-level Data Analyst and Business Analyst roles.