Bingo Sales Dashboard

Retail Data Analytics Project Overview This project involves the analysis of monthly retail invoice data, spanning from February to May. The dataset comprises four sheets: Sales (transactions), Products (product information), Reps (indoor sales), and Stores (store information). The project aims to utilize Excel, SQL Server, SSIS, and Power BI to solve a set of challenges and deliver actionable insights.

Excel Challenges Lookup Sheet Creation:

Added a new sheet with lookups for each ID with related information. Calculations using Formula or Pivot Table:

a. Identified and ranked the Top 20 Products per Sold. b. Determined and ranked the Top 10 Reps per Count of Order number. c. Evaluated and ranked the Top and worst 10 Brands per Value. d. Ranked the Month in Each Store. e. Ranked Reps in Each Store. f. Ranked Products in Each Brand. SQL Server and SSIS Integration Star Schema Creation:

Designed and implemented a star schema using Microsoft SQL Server. Defined facts and dimensions, deciding on column placements within tables based on the understanding of the data. ETL Pipeline with SSIS:

Constructed ETL pipelines using SSIS to clean and load data into the Data Warehouse (DWH). Power BI Visualization Connection to DWH:

Connected Power BI to the Data Warehouse. Descriptive Analysis Dashboard:

Developed a descriptive analysis dashboard in Power BI, emphasizing attention to dashboard design principles. Presentation Provided a brief presentation explaining the thought process and work during the task. Included snapshots of the star schema, SSIS pipelines, and key decisions made during data modeling and ETL processes. Deliverables Cleaned Excel Workbook Data Warehouse Backup File SSIS Solution Folder Please ensure the correct attachment. Power BI Dashboard File Presentation File Includes snapshots of the star schema and SSIS pipelines created during the project. Conclusion This project showcases the versatility of utilizing Excel, SQL Server, SSIS, and Power BI in a cohesive manner to extract valuable insights from retail data. The seamless integration of these tools ensures a robust analytical approach and empowers decision-makers in the retail space.