  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.
