Retail Sales Report

Project Overview

This project is an end-to-end data analytics solution for a retail business, aimed at uncovering insights into sales performance, customer behavior, and product trends. Using Power BI as the primary visualization tool, the project integrates data from multiple sources, including MySQL databases, CSV files, and shared folders, to deliver a comprehensive sales analysis. Key metrics and visualizations include sales trends, regional performance, product profitability, and customer segmentation, empowering stakeholders with data-driven insights for strategic decision-making.

Project steps:

Requirement Gathering:

• Gathering requirements involves understanding the objectives, scope, and constraints of the project. This typically involves creating
  • BRD (Business Requirement Document)
  • FRD (Functional Requirement Document)
• Here, for this project both BRD and FRD are given in a single pdf file.

Data Collection:

• The data for this project is collected from various sourses like Excel Spread sheet, CSV file, MySQL Database, and from a folder containing multiple files each containing similar data.

Data Cleaning and Preprocessing:

• Data that comes from different sources is not in the proper format. So, we need to clean the data, as well as if needed we can add new columns to the existing table or we can create new table.
• The Data Cleaning is done with the help of DAX (Data Analysis Expression) in Power BI.

Data Modeling:

• The Data is comming from different sources. So, it is necessary to structure and organize the data to create relationships between different tables, enabling more efficient analysis and visualization.

UI Report:

• Once the data cleaning and data modeling is over, then we can start creating our report in the Power BI desktop. The report is created based on the requirements given by the client.
• We can use various charts and graphs that are available in the Power BI desktop.
• Even we can use some custom charts in the Power BI Desktop for building our report.
• In this project I used scroller chart.

Additional Information (DAX Calculation) DAX--->Data Analysis Expression:

• While building the report you may need some measures(additional informations need to be calculated). These measures can be calculated by DAX in Power BI.
• Here I created Quater on Quater (QoQ) and Month on Month (MoM) total revenue change by using DAX (Data Analysis Expression).

RLS (Role Level Security):

• After building the report, you need to implement RLS (Role Level Security) within the Power BI Desktop. This RLS will give specific access to specific users.
• That is, if you need to restrict particular user from seeing the particular countries sales, you can restrict them by using RLS.

Create New Workspace:

• After completing the report, you need to share the report with your team mates. So, that we need to create a new workspace in Microsoft Fabric previously Power BI Service.

Publish:

• After creating the new workspace we now need to publish our end report to the workspace to share with the team mates.

Workspace Access:

• After Publishing the report to the workspace, we need to provide workspace access within the team.
• You can also implement Role Level Security(RLS) within the team.

Dashboard:

• You can create a dashboard by pinning important visuals from various reports and organize it in a single dashboard.

Create App and Share:

• After implementing all the steps above, now we can create the End App in Microsoft Fabric and we can share within the group. Also we can manage access of the Final App.

Technologies & Skills

• Data Sources: MySQL, CSV, flat files
• Data Cleaning & Transformation: Power Query, DAX
• Visualization & Reporting: Power BI
• Deployment: Microsoft Fabric (Power BI Service)
• Data Automation: Power BI Data Gateway for scheduled updates

Impact

This analysis provided actionable insights that helped the business optimize inventory, improve customer targeting, and drive overall sales growth. The automation of data updates saved time and enhanced decision-making by providing near real-time insights.

Key Takeaways

This project highlights my abilities in data wrangling, data visualization, and dashboard automation. I demonstrated proficiency in integrating multiple data sources, performing robust data transformations, and creating user-friendly dashboards to enable strategic insights, making this a valuable tool for any retail-oriented data analyst role.