Data-Driven Innovation: What Is It
Accepted for Publication in IEEE Transactions on Engineering Management
19 Pages Posted: 2 Nov 2021 Last revised: 20 Jan 2022
Date Written: December 15, 2021
Abstract
The future of innovation processes is anticipated to be more data-driven and empowered by the ubiquitous digitalization, increasing data accessibility and rapid advances in machine learning, artificial intelligence, and computing technologies. While the data-driven innovation (DDI) paradigm is emerging, it has yet been formally defined and theorized and often confused with several other data-related phenomena. This paper defines and crystalizes “data-driven innovation” as a formal innovation process paradigm, dissects its value creation, and distinguishes it from data-driven optimization (DDO), data-based innovation (DBI), and the traditional innovation processes that purely rely on human intelligence. With real-world examples and theoretical framing, I elucidate what DDI entails and how it addresses uncertainty and enhance creativity in the innovation process and present a process-based taxonomy of different data-driven innovation approaches. On this basis, I recommend the strategies and actions for innovators, companies, R&D organizations, and governments to enact data-driven innovation.
Keywords: Innovation Process, Uncertainty, Creativity, Data Science, Machine Learning, Artificial Intelligence
JEL Classification: O31, O32, O33
Suggested Citation: Suggested Citation