A Case for JCars Logistics Business
Introduction Building a dashboard is only one part of a successful Business Intelligence (BI) project. Before a visual can provide meaningful insights or support a business decision, the underlying data must first be understood, cleaned, standardized, modelled, and validated. This was the central lesson from my JCars Logistics Power BI project, where I developed an end-to-end BI and data…
The article presents a comprehensive case study of a Business Intelligence (BI) project undertaken for JCars Logistics, a vehicle sales and logistics business operating in Kenya. The project aimed to transform a raw transactional dataset into an interactive, executive-ready Power BI performance and diagnostic platform. The author details the challenges encountered while dealing with real-world business data, such as duplicate identifiers, inconsistent date formats, and inconsistent categorical values.
To address these issues, the author employed systematic data cleaning techniques, including text cleaning, categorical feature consolidation, and missing data imputation. The article also highlights the data modeling process, where the analytical model was structured as a Star Schema centered on Fact_Sales, linking dimensions with integer surrogate keys.
The model supports time-series reporting across multiple milestones such as order and delivery dates, and it includes key performance indicators (KPIs) such as the total number of cars sold, total sales revenue generated, total gross profit, gross profit margin, best-performing car makes, and strong-performing car models. The final outcome is an interactive management dashboard capable of supporting evidence-based business decisions.
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