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Top1. Introduction
The world economy has transformed into a full-fledged digital economy in recent years led by data-driven innovation. A perfect storm of information and technological infrastructure growth has led to an expanded digital economy with data emerging as one of the most important strategic assets (Gopalkrishnan et al., 2012; Morabito, 2015; Tallon, 2013). Being at the edge of the experience era, big tech giants and other myriad companies now consider customer experience as a key differentiator with data considered a key competitive advantage (Kamioka & Tapanainen, 2014; Rehan & Mohammad Saud, 2020). Designing and developing data-driven innovations (DDI) is the way that these companies live, breathe, strive, and sustain their competitive advantage in such a vast competitive data-driven environment. Big data teamed with advanced analytic portfolios has boosted cutting-edge data-driven innovation in an unprecedented way (Debora Di et al., 2015; Groves et al., 2016; Lee et al., 2014; Yun et al., 2020). Traditionally agile data-driven corporations, e.g., Amazon, Google, Apple, and Facebook as well as emerging start-ups, are deriving incremental benefits and more durable competitive advantage from DDI. DDI is defined as any process, method, model, product or service that that create and capture value (Davenport & Kudyba, 2016a). Using Netflix as an example, their personalized recommendation system captures data points on individual customer viewership and engagement on various TV shows and movies of more than 151 million subscribers. By implementing data analytics and algorithms, the company has developed its innovative recommendation platform that represents over 80% of the content streamed on the platform.
Such significant changes to business models are attributable to several factors which have functioned as capabilities for these global giants. These factors include advancement in information and communication technologies (ICT), growth of investment in big data and AI initiatives (Lu et al., 2018), strong data management and analytics capabilities (Kwon et al., 2014), strong data governance (Ladley, 2019), application of smart machines (Ransbotham & Kiron, 2017), building a data culture accompanied with organizational alignment and cultural compliance (New Vantage Partners, 2017). Therefore, acknowledging the significance of big data and analytics in developing data-centric innovations, further investigation is required to support this important research field.
There has been a focus on numerous waves of data-driven innovations in recent years such as data-driven R&D (Kayyali, 2013), data-intensive products (Zhan et al., 2016), data-driven processes, data-driven marketing (Erevelles et al., 2016), and data-driven organization. Data-driven researchers have also addressed dominant fields like, business intelligence (Chen et al., 2012), e-commerce (Akter & Wamba, 2016; Joines & Scherer, 2003), supply chain management (Sanders, 2014), smart city development (Ojo et al., 2015) and myriad of other domains. Acknowledging that DDI is in its infancy with limited theoretical and empirical research, there is nonetheless missing a comprehensive conceptualization based on a structured approach to the aforementioned data-driven initiatives. Such a gap has been identified by the extant research, which determines that the impending value of big data is yet to be revealed in the arena of new product innovation (LaValle et al., 2011; Davenport, 2013; Robert & Candi, 2014; Tan et al., 2015).
Early research typically centred on developing traditional information products (Browning et al., 2002; Kim et al., 2006; Littler et al., 1995; Meyer & Zack, 1996; Moenaert & Souder, 1990; Nambisan, 2003; Von Hippel, 1998) with the absence of analytics. Studies on data product innovation in a structured fashion are still in its infantry, and therefore, the question regarding how companies can take innovation initiatives in a data-impelled culture remains unanswered (Biemans & Langerak, 2015). Hence, the research question that has guided this study is: