Editorial: Perspectives on the value of Big Data sharing

Introduction The concept of Big Data has gone through several cycles of awareness, with no small amount of industry hype (Davenport et al., 2012) followed by a growing interest in academia (Abbasi et al., 2016; Batini et al., 2015; Buhl et al., 2013; Goes, 2014; G€ unther et al., 2017; Rai, 2016). The current common understanding of Big Data can be summarized by the following definition that appeared in 2013 in the first issue of Big Data, one of the first journals on the topic: Big Data is data that exceeds the processing capacity of conventional database systems. The data is too big, moves too fast or does not fit the structures of your database architectures. To gain value from this data, youmust choose an alternative way to process it (Dumbill, 2013). The hype (and phenomenon) followed and overlapped with public sector interest in open government data (OGD) (Bertot et al., 2014), symbolically reinforced at a global level by the memoranda and directives signed by Barack Obama in the early years of his first mandate (Chignard, 2013; Obama, 2009). The overlap of interests between the private and public sectors raises the question of the different types of value (economic, public and social value) that Big Data may have and the challenges related to having access to and sharing them, such as data quality and privacy (Batini et al., 2015; Jain et al., 2016; Menon and Sarkar, 2016). This special section aims to provide an outlook on research on these issues, considering specifically the connection, on the one hand, between Big Data, public safety, security and quality of life and, on the other hand, the different paths of business model innovation (cf. Massa and Tucci, 2021) enabled by Big Data, such as social innovation (Misuraca et al., 2018) and crowd-driven innovation (Afuah and Tucci, 2012; Tucci et al., 2018). Inspired by the rise of Big Data platforms and infrastructure that handle both structured and unstructured data from a multitude of domains and data sources (ranging from environmental and weather data to wearables, passenger vehicle sensors, financial and insurance institutions’ data streams and social web data), this special section explores the benefits and advantages—as well as the challenges, limitations and threats (at the data security and privacy level)—that emerge from the Big Data value chain (Curry, 2016; Miller and Mork, 2013), delivering “intelligence” to support operations that surround various aspects of human living. It is worth noting that the special section also considers the social value impact of Big Data-driven innovation in terms of capabilities and functionings enabled by emerging Big Data ecosystems. In particular, we refer to the construct of the “capability approach” (Nussbaum, 2011; Sen, 1992) where the focus is on “human functionings,” which are the various states of human beings and the doings or activities that a person can undertake, and capabilities, i.e. the opportunities to achieve functionings as outcomes. For example, traveling is a functioning, and the real opportunity to have a safe and secure trip is the corresponding capability. Taking these issues into account, Big Data and open linked data are a key resource for enabling capabilities, support decision-making on these issues and Editorial