How Big Data Has Changed Technology Roadmapping: A Review on Data-Driven Roadmapping - Jinhong Kim; Gamunnarbi Park; Myoungkyun Woo; Youngjung Geum (IEEE Access)
With the rise of data mining and predictive technologies, systematic data analytics has become popular in business practices. As data analysis can contribute to technology planning in various ways, previous studies have attempted to integrate data analysis and technology planning tools. This is also the case for the technology roadmap, which is a prominent and promising technology-planning tool. Many studies have discussed data-driven technology roadmaps using various approaches. However, studies on the dynamic trends in data-driven approaches are lacking. In response, this study collected data-driven roadmap literature and conducted various analyses to identify publication patterns and changes in methodological characteristics. Keywords, networks, topics, and methodology analyses were conducted to provide in-depth implications for data-driven roadmapping. Results indicated that patent analysis still occupies a big seat in data-driven roadmapping. In addition, data-driven roadmapping has changed from business and market analyses to intelligent frameworks for future trend prediction, together with recent deep learning techniques. Apart from simple trend analysis to support decision-making, it has evolved to generate the technology roadmap using generative AI techniques such as generative adversarial network (GAN).