TY - JOUR
T1 - How Big Data Has Changed Technology Roadmapping
T2 - A Review on Data-Driven Roadmapping
AU - Kim, Jinhong
AU - Park, Gamunnarbi
AU - Woo, Myoungkyun
AU - Geum, Youngjung
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2025
Y1 - 2025
N2 - 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).
AB - 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).
KW - Technology roadmap
KW - data analysis
KW - literature review
KW - technology planning
KW - trend analysis
UR - https://www.scopus.com/pages/publications/85214500051
U2 - 10.1109/ACCESS.2025.3526173
DO - 10.1109/ACCESS.2025.3526173
M3 - Article
AN - SCOPUS:85214500051
SN - 2169-3536
VL - 13
SP - 8297
EP - 8309
JO - IEEE Access
JF - IEEE Access
ER -