The increasing digitalization of businesses and society in general has triggered a veritable explosion in the amount of so-called Big Data made available and to be adopted and explored in the development of business. As stressed by Arthur (2011), digitalization is creating a second economy that is vast, automatic and invisible—thereby bringing about the greatest societal upheaval since the Industrial Revolution. Data has become massive and has moved from monthly, to weekly, to daily and hourly with regard to a large number of transactions made by millions of customers and entities across the ecosystems of organizations. Some studies estimate an increase in annually created, replicated and consumed data from around 1200 exabytes in 2010 to 40,000 in 2020 (Gantz and Reinsel 2012). Big Data is defined in terms of data volume (Manyika et al. 2011) and as high-volume, high-velocity and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision-making. There is growing awareness that this view is limited, however, as other factors are also important in discussing it, including the uncertainty of the data; its veracity, referring to the reliability of a certain data type (Schroeck et al. 2012). Such data can be applied by companies to target customers more effectively, to make better pricing decisions and demand predictions, and to optimize assortments, production and logistics. Thus, Big Data is employed for user-centric, knowledge-driven product development (Johanson et al. 2014).

The use of digital technologies and digitalization in innovation is central for digital business model innovation (DBMI) and the disruptive business innovation tendencies of this decade (2010s)—and likely also for decades to come. Consequently, Nambisan et al. (2017, p. 224) conceptualize digital innovation as “the creation of (and consequent change in) market offerings, business processes, or models that result from the use of digital technologies.” Consequently, digital innovation management refers to the “practices, processes, and principles that underlie the effective orchestration of digital innovation.” Digitalization affects entire ecosystems, their business models (BMs) and the underlying business functions of a company’s value chain. By digitalizing business functions, data can be provided to enhance and develop each of these functions—and thereby the entire value chain.

In practice, this is seen in the dramatic shift in focus toward marketing online, on social media and via mobile marketing, and a waning focus on traditional advertising. Stronger interactions are created and data is continuously collected from existing and potential customers through social networks. The online environment renders assortment and pricing decisions easier and much more flexible. Logistics and logistics streams are key to competitive delivery and services, and the marketing and logistic functions therefore need to cooperate more effectively to deliver superior customer value—and at a lower and more competitive cost. Standards have been developed to represent different forms of data (text, numbers, pictures and video) facilitating communication via Bluetooth and the Internet, which has led to the evolution of new products and services, all of which has further contributed to the commodification of data.

With intelligent devices becoming interconnected in “the Internet of Things” (IoT), new developments have created associated infrastructure and an expanding knowledge base. These innovative combinations are reflected in enterprise digital business models (DBMs) (Kiel et al. 2016). Holler et al. (2014) propose an information-driven value chain for IoT consisting of four inputs (devices/sensors, open data, operations support system/business support system (OSS/BSS) and corporate databases), as depicted in Fig. 1.1.

Each of these four inputs undergoes value addition through production/manufacturing, processing, packaging and further through distribution and marketing as a finished product. Figure 1.1 depicts how the raw data is collected through different types of sensors, actuators, open data, operating/business systems and corporate databases, and how the data then undergoes processing and packaging through a wireless fixed network prior to becoming useful information. As stressed by Chan (2015), the variety, velocity and volume of the acquired Big Data infrastructure enablers and a large-scale system integrator are required. Consequently, different players have to overcome the interoperability issue to ensure optimal value creation and performance across the information-driven value chain.

DBMI explores how companies adopt and deploy digital technologies and BMs to improve performance quantifiably. DBMI is thus considered to be a growth engine in the area of vertical and horizontal industry. IoT provides key leverage in digitalization and in providing data for digital BM development. The definition of IoT largely depends on the target audience and reflects the different types of IoT applications. However, according to Lee et al. (2017), four main categories of definitions describing IoT can be found in the literature: (1) IoT as intelligent objects, (2) IoT as an extension of the Internet, (3) IoT as a global network infrastructure and (4) IoT as an interaction of information. Throughout this chapter, IoT is defined as a global infrastructure linking physical and virtual objects through the exploitation of data capture and communication (EU FP7 Project CASAGRAS 2009). This clarifies how IoT is more than a set of technologies comprising IoT when “glued” together; it also involves the entire ecosystem in which IoT is present. In this chapter, we are concerned with the less technical properties of IoT and their meaning in the context of BMs.

BMs seek to make sense of how businesses go about their work, and they are presented with different levels of abstraction in the literature. However, a key challenge relating to performing BM studies relates to the issue addressed by David J. Teece, who states that “the concept of a business model lacks theoretical grounding in economics or in business studies” (Teece 2010, p. 174). BMs and business model innovation (BMI) have received extensive attention from academics and practitioners alike (Amit and Zott 2001; Chesbrough and Rosenbloom 2002; Markides and Charitou 2004; Teece 2010; Zott et al. 2011; Markides 2013; Spieth et al. 2014) and have been the subject of an ever-growing number of academic and practitioner-oriented studies. Several authors make attempts at defining the BM concept, including Afuah and Tucci (2001), who explain a BM as the means by which a business “creates, delivers and captures value” in a relationship with a network of exchange partners. According to Dodgson et al. (2013), the term “business model” is used either to commercialize new technology or ideas or as a source of innovation to the BM itself, which can lead to a competitive advantage.

While the extensive stream of work on BM innovation has generated many important insights (see Spieth et al. 2014), our understanding of BMs remains fragmented, as stressed by Zott et al. (2011). One thing all of the authors in this field seem to agree on is that a BM is a model of how a business does business (Taran 2011). However, while there is consensus on t...