Suggestion of a Big Data-Driven Design Evaluation Matrix: Hyundai Motors and Mercedes Benz

Abstract

Background Big data is one of the most important components of the next industrial revolution, which is propelled by private companies and government in an effort to create innovative business models. However, such movement is oriented from only the data discipline and industry-led professionals, having limited participation of experts from various fields. In this sense, this study proposes a designer-led evaluation methodology for design, based on big data so that practitioners can easily and quickly apply gathered data in the field. The aim is to enable data analysis using open platforms and collection. Methods For the research method, evaluation data is collected from users and media choosing web crawling over academic surveys. The web crawling technique collected two years of data from websites under the theme of Hyundai Motors and Mercedes-Benz, and classified data by processing sensitivity using Amazon Lex from Amazon Web Services(AWS), an open platform for big data. In addition, the study implemented a visualized evaluation map using Python in the coordination of each motor vehicle element. Results Consumer comments and media evaluations show that the number of positive comments regarding Hyundai Motors was recorded 2149, contrary to 1056 negative comments. Meanwhile, Mercedes-Benz had 1,581 positive and 1056 negative comments respectively. The most frequent comments centered around the entire side and the rear, while either positive or negative remarks about design reference focused on the entire side. Conclusions The proposed 2X2 matrix in the study provided a glimpse of the different frequency of comments in design contingent on brands. Also, the heat map enabled the delivery of comment frequency of each design and component area at multiple angles. The importance of industrial design will be newly recognized once the evaluation map based on big data can be carried out by designers.