As time passes and technology becomes more impressive, more ways exist to process, store and visualise data, contributing to data analytics' power. The most common process is to collect data from different sources and store it in some file format. Some process then passes the acquired information to a data warehouse where the data can be stored and queried. Once the right data and tables have been selected, they are imported and exported to a visualisation system, where reports can be generated from the available information. All that remains is to create a custom website where the reports can be displayed. In this paper, we work with data related to petrol, diesel, and electric vehicles. The dataset was synthetically generated using a simulation model developed by the author to reflect realistic automotive scenarios. An ETL process will import the data into the data warehouse, which will be stored for subsequent analysis. The information is then exported from the data warehouse to be processed by the visualisation program and used to make statements. This process aims to demonstrate how the values derived from the generated data transition from their raw form to a visualised state. Given the current global transformation of the automotive industry, this topic was selected due to its relevance and the widespread impact of these changes. The study aims to generate various analytical statements, such as the average engine power across different car brands or the average fuel consumption per 100 kilometres for specific vehicle types.