📘 Finance SQL Mini Project — Analysis Summary

This project performs a structured financial analysis using SQL on the Finance_Data dataset.

It includes 22 SQL queries analyzing profitability, revenue trends, expenses, product performance, segmentation, and dataset validation.

🎯 Decision Context

This SQL analysis supports validation of financial metrics and identification of profitability risk drivers across products, countries, segments, and time.

It is designed to answer why revenue strength does not always translate into profit by examining cost structure, expense intensity, and margin behavior.

⭐ 1. Project Overview

• Analyzed key financial metrics (Revenue, Net Profit, Operating Expenses).
• Identified top-performing products, countries, and customer segments.
• Evaluated monthly and quarterly trends using CTEs.
• Applied joins, window functions, CASE logic, subqueries, and sanity checks.
• Generated actionable insights for financial decision-making.

🚫 Scope & Limitations

• Based on historical financial data only
• Focused on descriptive and diagnostic analysis
• Does not perform forecasting, scenario modeling, or prescriptive recommendations