Simplifying Data Analysis: A Visualization Framework and Practical Application for Complex BEV Data

Effective data visualization for business applications is crucial in extracting meaningful insights from vast amounts of data generated from various sources. However, there is a need for more visualization tools that combine multiple features, such as complexity reduction methods through the principal component analysis with the data analysis method clustering and interactive visualization. The paper discusses the necessity for novel methods to handle complex and high-dimensional data. It proposes an innovative framework for a visualization tool that integrates complexity reduction, analysis methods, figural features, and various output possibilities. The proposed framework is further practically tested with a dataset of battery electric vehicles (BEV) from the German market. Lastly, the implications for research and practice are discussed, and further research avenues are proposed.