This project focuses on analyzing Revenu data from a toy shop to generate actionable insights. It involves cleaning messy data, normalizing it into a star schema, creating an effective data model, and building an interactive dashboard in Power BI.
- Power BI
- Power Query
- DAX (Data Analysis Expressions)
- Raw data contained blank rows, blank columns, and was in an unstructured format.
- Utilized Power Query Editor to:
- Remove nulls and irrelevant columns.
- Rename columns and ensure consistent formatting.
- Set correct data types.
- Transformed the flat-file structure into a star schema for optimized data modeling.
- Created the following tables:
Fact_Sales: Includes sales metrics like Revenue, Quantity, Profit, along with keys for Date, Store, and Product.
Dim_Store: Store attributes such as Name, Type, Region.Dim_Region: Region names and hierarchy.Dim_Date: A calendar table for time-based analysis.
-
Established relationships:
Fact_Sales➝Dim_StoreFact_Sales➝Dim_RegionFact_Sales➝Dim_Date
-
Used one-to-many relationships and ensured referential integrity.!
-
- Total Revenue vs Previous Month
- Total Quantity vs Previous Month
- Total Profit vs Previous Month
Implemented using DAX measures such as PREVIOUSMONTH(), CALCULATE(), and DATEADD() for time intelligence.
- Revenue Trend by Weekdays
- Store Type Revenue Share
- Top Performing Product vs Others
- Top Performing Store
- Creating a dynamic calculation to display:
- Top N Product and group others as "Others"
- Top N Store.
- Delivered an interactive and business-focused Power BI dashboard.
- Enabled month-over-month comparison of sales KPIs.
- Identified key revenue drivers and top-performing entities.
- Facilitated data-driven decisions for store and product strategies.