Global Retail Sales & Budget Intelligence Dashboard- Power BI

📂 GitHub Repository: View Full SQL Script & Reproducible Files on GitHub

Project Overview: Executive Sales & Budget Analytics

This project is a practical exercise in sales performance analysis and variance reporting using Power BI. Using a cleaned version of the public AdventureWorks retail dataset alongside created budget targets, I modeled the data to analyze sales performance against mock business goals.

1. Mismatched Timeframes & Granularity

The AdventureWorks sales data ended earlier than the simulated budget timeline, which stretched through 2026. Connecting mismatched date ranges caused comparison visuals to display misleading grand totals, flatline trends on daily charts, and skewed variance metrics on summary cards.

• Resolution: I rebuilt the data model by clearing broken relationships and creating a clear bridge between the fact tables' integer date keys (OrderDateKey) and a master date table. In Power Query, I appended year-shifted copies of historical sales data forward into 2025 and 2026 to align the date ranges between actual sales and budget targets.

  
  

2. Visualizing Skewed Product Category Values

Because the dataset features high-ticket items (Bikes) alongside low-cost items (Accessories and Clothing), raw dollar charts were completely dominated by bike sales. Accessories were squeezed into flat lines near the bottom axis, making them hard to read.

• Resolution: Rather than modifying the underlying data, I adjusted the visual metrics to display a Percent of Total breakdown alongside absolute revenue. This made category trends visible and easier to compare without losing the raw dollar context.

  
  

3. Map Settings & Report Layout Tweaks

Setting up the report required tweaking a few Power BI defaults. Default security settings initially blocked the map visual from rendering location data, and automatic timeline padding created empty spaces on the ends of line charts.

• Resolution: I enabled map visual permissions in Power BI settings and verified location data mapping for accuracy. To clean up line chart visuals, I applied a visual-level filter to show active date ranges where sales were greater than $0, snapping the chart lines cleanly to active periods.

  
  

Key Takeaways & Skills Applied

• Data Modeling: Built a star schema linking multiple fact tables (Sales and Budget) to shared dimension tables (Date, Product, Territory) using integer keys and DAX measures.

• Consistent Formatting: Applied a simplified, uniform color scheme and report layout across charts, slicers, and card metrics for clean readability.

• Filter Interactivity: Configured cross-filtering and slicer controls to let users analyze sales vs. budget variance across different regions and categories.