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 computing, machine learning, and artificial intelligence 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 aims to crystalize “data-driven innovation” as a formal innovation process paradigm, dissect its value creation, and distinguish it from datadriven optimization (DDO), data-based innovation (DBI), and the traditional innovation processes that purely rely on human intelligence. With real-world examples and theoretical framing, we elucidate what DDI entails and how it addresses uncertainty and enhance creativity in the innovation process. We also discuss the strategies and actions that innovators, companies, R&D organizations, and governments can take to embrace the future of data-driven innovation.
Figures and Tables
Figure 1. The Double Hump Model explains value creation of the Data-Driven Innovation process
Figure 2. Creative Artificial Intelligence (CAI) requires both machine learning and machine creation
Figure 3. Roles of human innovators in the data-driven innovation process
Table 1. Data-Driven vs. Human-Social Approaches to Innovation Process Actions
Table 2. Differentiating Data-Driven Innovation from Data-Based Innovation & Data-Driven Optimization
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