Data-driven innovation: switching the perspective on Big Data

Abstract

Purpose The pervasive spread of digital technologies brought an incredible boost in data availability. Companies are dealing with massive amount of data that wait to be exploited. At the same time, scholars are providing different strategies and methods to help companies capture the value embedded in their data to foster innovation and improve the efficiency of existing processes. In these research studies, data are the by-product of something else, and they are a silent asset that needs to be exploited. What if data might be considered the final goal? The paper aims to discuss these issues. Design/methodology/approach The research is based on an exploratory multiple case study analysis, on the basis of three cases used as an illustration for new ideas. In particular, the gathered data are analyzed according to models previously presented in the literature review, building on and expanding them. Findings The research proposes a data-driven approach to innovation, offering a peculiar view of the innovation process. The trigger point is the need of data that let begin the entire development process of a complex system. In this perspective, the application that data are a by-product of the entire innovation process and not the primary output is peculiar since the vast majority of the literature consider data as the by-product of the primary product. Research limitations/implications Future research is needed to assess the replicability of the model outside the mobile app industry and to measure its performances. Nevertheless, this paper provides insights both for scholars and managers, enlarging the discussion on digital innovation and digital business models. Practical implications The results provide a development process to foster innovation relying on the need of data as the trigger point, guiding entrepreneurs and managers in the building process of the entire digital system. Originality/value Previous research studies often considered Big Data (BD) in innovation as a way to enlarge the current product offer or to make the innovation process more effective or efficient; this paper changes the perspective by considering BD as the trigger and the enabler of the entire digital innovation process.

Fostering digital platform innovation: From two to multi‐sided platforms

“…First, they show how different kinds of two‐sided platforms may co‐exist, merge the transactional and non‐transactional dynamics with the same system, and even with the same sides (as shown, for example, with http://Booking.com). Second, they show how client‐as‐a‐source strategies highlighted in non‐transactional platforms (Trabucchi & Buganza, ; Trabucchi et al, ) may be relevant and implemented in transactional‐based structures. Both strategies are based on the chance to expand the basic transactional two‐sided structure by adding a non‐transactional perspective, relying on the power of big data.…”

“…Therefore, the economic relevance of smartphone apps is growing, and the revenues from mobile apps are expected to reach US$188.99 billion in 2020, up from US$69.7 billion in 2015 (Statista, ). Moreover, recent papers used the smartphone application industry as the empirical field for their analyses (Trabucchi et al, ; Trabucchi & Buganza, ) because mobile apps are considered the common channel through which matching between the sides is enabled (Täuscher & Laudien, ).…”

The big data-business strategy interconnection: a grand challenge for knowledge management. A review and future perspectives

“…The first, 'enhanced advertising', is a strategic option by which a company uses consumer data to propose highly contextualised advertising messages tailored on user preferences. The second, 'e-ethnography', is a strategy by which companies consider data relating to consumer habits, needs and relations as 'by-products' functional to the improvement of its core products and services and even as a final innovative output capable of automatically strengthening the corporate value proposition (Trabucchi and Buganza, 2019a). Lastly, in the third strategy, 'data trading', user data is directly sold to a third party, thus becoming a revenue-generating asset itself.…”

Social media and sensemaking patterns in new product development: demystifying the customer sentiment

“…The products are comprised of multiple traits and embedded components, such as quality, cost, technology, packaging and services, and any breakthrough in one dimension can be regarded as the production of new products (Takeuchi and Nonaka 1986). Seminal research work on NPD focuses on the innovative and strategic potential of the NPD cycle and the implications for the success or failure of the product launch (Schilling and Hill 1998;Veryzer 1998) and the key role of Big Data Analytics and AI have to play in this process (Trabucchi and Buganza 2019).…”