Section I: Big Data and Management: Theoretical Foundations
Big Data Analytics: Innovation Management and Value Creation
Anna Jasiulewicz, Piotr Pietrzak, and Barbara Wyrzykowska
Warsaw University of Life Sciences—SCCW
Innovations are one of the key areas of enterprise activity (Kotler & Kotler, 2013). Many authors (including Davenport et al., 2012; Chen et al., 2012; McAfee & Brynjolfsson, 2012) believe that BDA can be a source for creating innovative products, services, and business opportunities. Gunther et al. (2017) claim that economic value arising from the use of Big Data Analytics (BDA) can be measured by organizations in terms of profit, business expansion, or an increased competitive advantage. Other economic and social values resulting from BDA can manifest themselves in more robust decision-making, improved business processes, and the creation of innovative business models (e-commerce, security) (Manyika et al., 2011; Das Kumar, 2013), as well as tracking and monitoring various socio-economic phenomena (Wamba et al., 2015; Erickson & Rothberg, 2013; Schmarzo, 2013)-
BDA is currently counted among the most dynamically developing research areas in the world (Davenport et al., 2012; Chen et al., 2012; Lavalle et al., 2011). Tlie importance of the potential inherent in Big Data (BD) is indicated by representatives of both scientific institutions (Kailsler et al., 2013, Chen et al., 2014, Olszak, 2018), as well as those engaged in business (Manyika et al., 2011, Microsoft, 2016; The Data Warehousing Institute, 2013). They believe that the challenge for the coming years and, at the same time, one of the greatest needs of modern organizations is intelligent analytics, which allows business value to be distilled from BD (Ulru et al., 2014; Gunther et al., 2017). Until now, an overall value creation model based on BDA innovations has not been developed and validated. This issue is the focus of this chapter.
This chapter aims to present examples of innovative products derived from BD methods and to develop a model for creating value based on BDA innovations. Dissemination of knowledge on this subject is important from the perspective of modern technologies being implemented by enterprises and their innovativeness.
This chapter begins with a presentation of examples of innovative products and services created based on the implementation of BDA methods in various fields. This chapter’s second part presents innovation-based value creation and a proposed model for creating value from BDA-based innovations based on a literature review and case studies. In the end, conclusions have been formulated regarding BD’s potential in managing innovation and creating value.
The primary research method was a critical evaluation of literature on the topic (Webster Watson, 2002), which was aimed at identifying the key innovations shaping BDA-based value creation. This methodology included searching for source materials, their selection, analysis, and synthesis. It was used to develop a value creation model based on BDA innovations.
Tire critical evaluation included articles from such databases as EBSCO, ProQuest, Scopus, and Google Scholar. Over 120 different studies were collected for an initial review and subsequently subjected to a selection process (based on selected keywords, abstracts, and titles). Finally, over 90 articles were chosen for in-depth analysis. A synthesis of the collected research material made it possible to identify the research gap and propose a comprehensive BDA-based value creation model.
Big Data Analytics: The Innovation Driver
BDA has become a popular topic of discourse among industry executives, policymakers, and even academics over the last 7years (Persaud & Schillo, 2017). Madsen and Stenheim (2016) suggest that BDA may be of a longer duration and not just another short-term management fad or fashion. A range of metaphors and buzzwords have been used to promote the hype around BDA (Madsen & Stenheim, 2016). These include a new era, a management revolution, the new oil, the next frontier, and a new economic asset (Brown et al., 2011; McAfee & Brynjolfsson, 2012; Manyika et al., 2011; Syed et al., 2013). At the same time, Harford (2014, p. 14) points out that “as with so many buzzwords, ‘Big Data’ is a vague term, often thrown around by people with something to sell”.
Despite the growth of the popularity of BDA, some scholars (Rigby & Bilodeau, 2011) claim that there is presently little conclusive proof of the adoption of BDA by governments, non-profit organizations, or even firms. According to Mazzei and Noble (2017), a great number of managers are still unsure of how to correctly apply BDA within their organizations. “With a billion-plus users on the online social graph doing what they like to do and leaving a digital trail, and with trillions of sensors now being connected in the so-called Internet of Tilings, organizations need clarity (...) into what lies ahead in deploying these capabilities” (Bapna, 2015, p. vi). The same BDA has become a key organizational asset, which forms a strategic basis for business competition.
Tliis evolution is making companies look at new contemporary tools and methods on maximizing the potentials of BDA along with the challenges it generates (Wielki, 2013)- However, the success of numerous organizations requires new abilities, as well as new perspectives on how the era of BD could accelerate the speed of business processes.
Many organizations are seeking better approaches to continuously innovate and develop their products and services in today’s aggressive business environment (Piatetsky-Shapiro, 2013)- Generally, innovation is seen as a factor of regular research and improvement processes using common manual procedures (Gobble, 2013). Due to the increase of BDA, companies are “gradually depending on accumulated computerized data acquired from different sources such as their suppliers, customers and shareholders for recognizing innovative products and service systems” (Morabito, 2015, p. 125).
Several experts have offered suggestions and guidelines for organizations to create innovative products and services from BDA applicable to different fields (e.g., Wielki, 2013; Groves et al., 2013). Of course, some industries have more potential in BDA than others (Manyika et al., 2011). The highest potential occurs in the insurance and finance industry (Sadovskyi et al., 2014). However, BDA can also be successfully used to accelerate innovation in the following industries: household appliances, automotive, healthcare and medicine, and even government. Selected examples are presented below.
There has been enormous advancement in sensor technologies that go into automobiles, machines, utility grids, or mobile devices. This has led to the generation of machine-to-machine (M2M) data at a unique speed and in real time (Ehret &C Wirtz, 2017). For instance, Whirlpool, the home-appliances manufacturer, uses sensors in their products to follow how clients use their products, connect these data with usergenerated content from social media platforms, and create insights into their clients’ behaviors and preferences (Woerner & Wixom, 2015). Also, Volvo Cars Company, one of the most prominent players in the automotive industry, implements automatic fault monitoring through the analysis of data collected from sensors located inside the vehicles (Sadovskyi et al., 2014). This data, integrated with information acquired from maintenance workshops and results of customer analytics processes in social media, generates an important basis for the improvement of higher-quality products that better fit the needs of the clients (Van Horn et al., 2012).
In turn, BDA in healthcare and medicine includes various types and massive amounts of data generated from hospitals such as omics data, biomedical data, and electronic health records data (Ristevski &C Chen, 2018). In other words, BDA in medicine is generated from historical clinical activities (Tsumoto et al., 2013) and has significant effects on the medical and healthcare industry. For instance, it can assist in processing clinical decision support, planning treatment paths for patients, and improving healthcare technology and systems (Jee &C Kim, 2013). Moreover, Raghupathi and Raghupathi (2014) point out that BDA in healthcare can contribute to evidence-based medicine, genomic analytics, pre-adjudication fraud analysis, and patient profile analytics. In turn, Manyika et al. (2011) estimate that BDA can enable more than $300 billion in savings per year in U.S. healthcare. Also, in 2017, the Ministry of Health Malaysia has launched the Malaysian Health Data Warehouse to share information and medical records between private/public hospitals and clinics (Fatt & Ramadas, 2018). Besides, North York General Hospital in Toronto has implemented a scalable real-time analytics application to improve patient outcomes and acquire better insight into the operations of healthcare delivery (Raghupathi & Raghupathi, 2014).
Finally, BDA can also be used to create innovation in the government sector. Kim et al. (2014) have proposed ways governments can use BDA to help them serve their citizens and deal with national challenges involving the economy, job creation, natural disasters, and terrorism. The U.K. government was one of the earliest implemented among EU countries of BD programs, establishing the U.K. Horizon Scanning Centre (HSC) (Sherry, 2012). It began work in December 2004 with the aim to “feed directly into cross-government priority setting and strategy formation, improving Government’s capacity to deal with cross-departmental and multi-disciplinary challenges” (Habegger, 2009, p. 14). Besides, in 2009, the U.S. government launched Data.gov as a “step toward government accountability and transparency” (Kim et al., 2014, p. 82). It is a warehouse containing 235,959 datasets (as of September 2019), covering education, agriculture, healthcare, or transportations. As we can read on the website, government data are “accessible, vetted,
and available; and are, for the majority, free and do not require registration to use” (https://www.data.gov/. Download: 16.09.2019). Because it should be noted that corporations use BDA to pursue profits, “governments use it to promote public goods” (Kim et al., 2014, p. 78).
To sum up, BDA has the great potential to accelerate innovation and, consequently, to create value. Persaud and Schillo (2017, p. 10) admit that “the path from data to value creation is not automatic, as more data does not naturally lead to greater value. Several technical and organizational challenges must be overcome along the way”. In the next part, the authors will focus on value creation.