The Problem
The Kaggle Sample Superstore dataset models a US retail business — orders, customers, products, and shipping across four regions. It is a common starting point for BI work precisely because it is messy enough to be interesting: some sub-categories are top-line revenue drivers that quietly lose money, and regional performance varies in ways that are invisible in aggregate totals.
The goal of this project was mainly to learn Tableau by building a real dashboard from a familiar business dataset. I wanted to practice turning raw business data into something readable, interactive, and useful without overcomplicating the analysis.
What I Practiced
This project helped me get more comfortable with the core Tableau workflow: connecting a dataset, understanding the fields, creating calculated measures, arranging visuals into a dashboard, and thinking about how someone would actually read the result.
I also used it to practice basic BI judgment. The challenge was not just making charts, but deciding what information belonged together, what should be easy to compare, and how to keep the dashboard understandable for a non-technical viewer.
Dashboard
The dashboard is published on Tableau Public and is the main artifact for this project. It focuses on giving a clear overview of the business dataset and showing how Tableau can be used to explore performance across different parts of a retail business.
The most useful part of the project was learning how dashboard structure affects interpretation. Even with a standard dataset, the layout and filtering choices make a big difference in whether the final result feels like a usable BI dashboard or just a set of separate visuals.
Code & Artifacts
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Tableau Public: Sample Superstore Dashboard
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Dataset: Kaggle — Sample Superstore