This Power BI project follows a complete data analysis workflow; importing, cleaning, modelling, and visualizing business data to uncover actionable insights. It showcases strong skills in data transformation, DAX calculations, and interactive reporting to support informed decision making.
This project follows a complete ETL (Extract, Transform, Load) pipeline to ensure clean, structured data for analysis in Power BI.
- Imported CSV files & folder-based data into query editor
- Combined multiple files into structured tables
- Removed erorrs and empty rows to ensure data accuracy
- Eliminated duplicates and handled missing values to maintain data integrity
- Standardized column names & data types for consistency across tables
- Applied transformation steps, including splitting/merging columns, formatting values, and extracting needed information
- Created many new calculated columns to enhance analysis
- Built a Calendar Table with calculated columns (Week, Month, Year, Start of Year, Start of Quarter) for time-based analysis
- Loaded cleaned and transformed data from Power Query into Power BI
- Ensured data structure compatibility for smooth report building
After loading the cleaned dataset to Power BI fronend, a well built data model was ceated using snowflake schema. Steps included are:
- Established relationships between tables using Primary & Foreign Keys
- Defined one-to-many relationships for correct data aggregations
- Kept cross filter direction to single to avoid circular dependencies and ambiguous joins
- Used snowflake schema to ensure efficient query performance
- Hid specific columns to prevent incorrect filtering in dashboards
DAX (Data Analysis Expressions) was used to create custom calculations for dynamic reporting and accurate business analysis. To keep all measures organized, a separate table 'Measure Table' was created to store them in one place, making them to find and manage easily.
- Total Orders - Calculates the total number of orders placed
- Weekend Orders - Calculates the number of orders made on Saturday & Sunday
- % of All Orders - Measures how a specific subset of orders contributes to total order
- Revenue Target - Compares current revenue with the predefined target
- Return Rate - Calculates the percentage of returned orders compared to total orders
- Previous Month Profit - Gets the total profit from the last month
- Average Revenue Per Customer - Calculates the average revenue generated per individual customer
- 90 Days Rolling Profit - Calculates total profit over the last 90 days, updating dynamically as new data comes in
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90 Days Rolling Profit = CALCULATE( [Total Profit], DATESINPERIOD( 'Calendar Lookup'[Date], MAX('Calendar Lookup'[Date]), -90, DAY ) )
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The final data and DAX measures were used to display key metrics, trends, comparisons, dsitributions, top products/customers using different types of charts and visual elements.
- Card - To show summary metrics (Total Revenue, Total Orders)
- KPI, Gauge - To track performance against set goals (Monthly Orders vs Target)
- Line Chart, Clustered Bar Chart, Donut Chart - For trend analysis and comparisons (Orders Trending, Orders by Income Level)
- Map - To visualize geographic data distribution
- Table, Matrix - To display detailed records and summary metrics
- Slicers for filtering by year, continent
- Drill-through to product-level details
- Custom tooltip on hover
- Page navigation using buttons
- Custom slicer pane using buttons and bookmarks
Finally come the dashboards. Each dashboard provides insights into different aspects of the business and contains a page navigator, allowing users to seamlessly switch between different report pages for a smooth and interactive experience.
Provides a high-level overview of total revenue, total profit, total orders, return rate, weekely revenue trend, key metrics KPI, top 10 products etc. Also features a slicer pane and a button to reset all filters.
Shows the total orders distribution across continents. It also features a slicer based on continents.
It is a drill-through page that provides a deep dive into individual product performance (monthly orders, revenue against set target, profit & revenue trending), accessible from the main executive dashboard.
Displays key metrics like the count of unique customers, revenue per customer, and top customer within a selected time window. It also provides insights into order distribution by occupation and income level, helping analyze customer behavior effectively
Enhances the Order by Category clustered bar chart, providing additional details when users hover over a specific category, making data exploration more intuitive.
- Power BI Desktop
- Power Query
- DAX (Data Analysis Expressions)
- Microsoft Excel (to see raw data)
This project is licensed under MIT License.
Contributions, issues, and feature requests are welcome!
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