Data-Driven Innovation: What Is It

TL;DR

This paper formally defines data-driven innovation (DDI), distinguishes it from related concepts, and provides a taxonomy and strategic recommendations for leveraging DDI in various organizations.

Contribution

It introduces a formal definition of DDI, dissects its value creation, and offers a taxonomy and strategic guidance for implementing DDI effectively.

Findings

Defines and clarifies the concept of DDI.

Provides a process-based taxonomy of DDI approaches.

Recommends strategies for organizations to adopt DDI.

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.