Welcome to the Bike Store Analytics Dashboard – a data-driven project that transforms raw bike rental data into powerful business insights using Microsoft SQL Server and Power BI.
📌 Project Overview This project analyzes two years' worth of bike rental data to answer key business questions such as:
When are we making the most money? How do seasons and times of day affect revenue? What impact does pricing have on rider demand? Who are our riders – casual or registered? Using SQL for data extraction and transformation and Power BI for interactive data visualization, this dashboard uncovers meaningful patterns to support strategic decision-making.
💾 Tech Stack Database: Microsoft SQL Server Data Modeling & Queries: T-SQL (CTEs, joins, calculations) Visualization: Microsoft Power BI Data Analysis: DAX (Data Analysis Expressions)
📊 Key Insights 💸 Revenue Peaks: Highest earnings occur at 5 PM and 6 PM, aligning with post-work hours. 📈 Pricing Strategy: A 25% price increase led to a 64% surge in rider demand — showing elasticity in rider behavior. 📅 Seasonal Trends: Season 3 (likely summer) generated the highest revenue. 👥 Rider Demographic: Over 81% of users are registered riders, but casual users account for significant daily fluctuations. ⏰ Profitability by Hour: Midday to early evening (10 AM – 3 PM) are the most profitable hours.
🧠 Skills Demonstrated ✅ SQL querying (data transformation using CTEs, UNION, calculated columns) ✅ DAX (custom KPIs and measures) ✅ Power BI dashboard design (clean UI with slicers, cards, and visuals) ✅ Business storytelling with data
Go to Get Data > SQL Server, enter your Server Name and Database Name, then expand the Advanced options, and paste the following SQL query:
WITH cte AS ( SELECT * FROM bike_share_yr_0 UNION ALL SELECT * FROM bike_share_yr_1 )
SELECT dteday, season, a.yr, weekday, hr, rider_type, riders, price, COGS, riders * price AS revenue, riders * price - COGS AS profit FROM cte a LEFT JOIN cost_table b ON a.yr = b.yr