Innovation patterns of big data technology in large companies and start-ups: an empirical analysis

ABSTRACT

With the unprecedented growth in information technology, the importance of big data analytical skills has grown exponentially. Therefore, it is important to understand innovations in big data and related technologies. Since large companies and start-ups are considered the principal sources of innovation, we investigate their innovation patterns pertaining to big data. We analyse all the patents in the G06F-17/30 class in the United States Patent and Trademark Office (USPTO) for 2017 and use hierarchical clustering, word stems analysis and minimum spanning tree to classify the patents as part of an exploratory research. Our results show that large companies concentrate on B2C businesses, such as entertaining and interacting skills, whereas start-ups focus on niche markets, such as materials, components and social media. Our paper contributes to a more a comprehensive understanding of future technologies and the decisions for investments by venture capitalists and governments.

KEYWORDS:

• Big data
• innovation pattern
• large companies
• start-ups

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Ohsung Kwon

Ohsung Kwon is associate research fellow at Korea Institute for Industrial Economics & Trade (KIET). He received his MS and PhD degrees in Business and Technology Management and BS degree in Mathematical Sciences from Korea Advanced Institute of Science and Technology (KAIST). He has studied start-ups and their technological innovation, especially in ICT, energy, and finance sectors, using various statistics and network methodologies.

Sangmin Lim

Sangmin Lim is strategy manager at Samsung SDS. He received his MS and PhD degrees in Business and Technology Management from KAIST and BA degree in Accounting and Taxation from Kyung Hee University. His research interests include entrepreneurship and technological innovation, trade network and social phenomenon.

Duk Hee Lee

Duk Hee Lee is professor at School of Business and Technology Management and also Head of Graduate School of Innovation and Technology Management, Korea Advanced Institute of Science and Technology (KAIST). His research areas are network and complex economics, innovation ecosystems and ICT economics and policy. He has investigated the fitness of network science combined with econometric methods like social network analysis, system dynamics, agent-based model with the tasks in the economy. And he analysed the mechanism behind market dynamics such as the direct interdependence among economic agents, irrational behaviours and the non-reductive phenomena between micro and macro economies. He received the PhD degree in Economics at the State University of New York (Buffalo).