📊 Sales Insights Dashboard – Power BI Case Study

1️⃣ Problem Statement

A computer hardware manufacturing company was facing declining and inconsistent sales performance.

The sales team lacked:

• Clear visibility into revenue trends
• Market-level performance insights
• Real-time performance tracking
• Automated reporting

Manual data gathering was time-consuming and inefficient. To solve this, the company hired a Data Analytics Team to uncover hidden sales insights and build a centralized dashboard in Power BI.

2️⃣ Project Objective

To design and implement an automated Power BI dashboard that:

• Tracks total revenue
• Tracks sales quantity
• Provides revenue breakdown by market and product
• Enables faster, data-driven decision-making
• Reduces manual reporting time

3️⃣ Stakeholders Involved

Sales Director, Marketing Team, Customer Service Team, Data & Analytics Team, IT Team

Team Roles:

• Software Engineers – Falcons
• Data Engineers – Data Miners
• Data Analysts – Data Masters

4️⃣ Success Criteria

• The dashboard uncovers sales insights using the latest available data
• The sales team improves decision-making
• The company proves at least 10% cost savings
• Analysts reduce manual data gathering by 20%

🔎 Data Discovery & Understanding

Database: DEV_sales_insights — Schema: sales

Key Tables:

• Customers — SELECT * FROM sales.customers;
• Transactions — SELECT * FROM sales.transactions;
• Products — SELECT * FROM sales.products;
• Markets — market_code, market_name, zone

Primary Analysis

Total Transactions: SELECT COUNT(*) FROM sales.transactions; — Result: 150,283

Total Customers: SELECT COUNT(*) FROM sales.customers; — Result: 38

📅 Year-Based Analysis (Examples)

SELECT sales.transactions.*, sales.date.* FROM sales.transactions INNER JOIN sales.date ON sales.transactions.order_date = sales.date.date WHERE sales.date.year = 2020;

Total Revenue in 2020 example:

SELECT SUM(sales.transactions.sales_amount) FROM sales.transactions INNER JOIN sales.date ON sales.transactions.order_date = sales.date.date WHERE sales.date.year = 2020;

🧹 Data Cleaning & ETL

Connecting Power BI to MySQL: Server: localhost — Database: sales. Load tables and create relationships (market_code, order_date, product_code).

Common cleaning issues shown with example queries (negative sales, currency inconsistencies).

📊 Dashboard Development (Power BI)

Measures (DAX) examples:

Revenue = SUM('sales transactions'[sales_amount]) Sales Qty = SUM('sales transactions'[sales_qty])

Visualizations include KPI cards, Revenue by Market, Top 5 Customers, Top 5 Products, Revenue Trend, Year & Month slicers, drill down by month.

🎯 Final Outcome

The final dashboard automates sales reporting, provides real-time insights, tracks revenue trends, identifies top-performing markets and products, and reduces manual reporting time.