Data-Driven Innovation: What Is It?

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 article defines and crystalizes “DDI” as a formal innovation process paradigm, dissects its value creation, and distinguishes it from data-driven optimization, data-based innovation, 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 DDI approaches. On this basis, I recommend the strategies and actions for innovators, companies, R&D organizations, and governments to enact DDI.