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The Internet of Things and Cognitive Analytics
Introduction
Today, we live in a world where technological devices (from smart phones to assisting-living devices and smart watches) are ubiquitous and where almost everything can become a computer and be linked to the Internet, leading to the concept of the Internet of Things (IoT). This interconnectivity allows the Internet of Things to collect all kinds of data about human social life (Ma, 2011; Ning & Wang, 2011; Atzori, Iera, & Morabita, 2014; Borgia, 2014; Yang, Zhang, & Vasilakos, 2014). As a result, “data are more deeply woven into the fabric of our lives than ever before” (Bi & Cochran, 2014, p. 250). However, the mere availability of data is not sufficient to improve decision making for individuals as well as organizations. What is important is how decision makers integrate data and knowledge to address particular issues. Indeed, “the value of any analytics effort lies largely in whether it can help decision making” (Kiron, Prentice, & Ferguson, 2014, p. 32).
The goal of the chapter is to call attention to the role of cognitions, interpretation and sense making in using big data analytics. The Internet of Things provides the opportunity to collect vast amounts of data and analytics and improves the ability to analyze the data collected. However, to make sense of the data available, one needs knowledge and expertise. In addition, experience and intuition still remain ‘relevant’ when individuals make decisions. Improving data-driven decisions requires that decision makers possess knowledge in the domains in which the data are collected and analyzed. Thus, the present chapter emphasizes the role of human cognition, interpretation, understanding and sense making (Daft & Weick, 1984; Weick, 1993; Starbucks, 1996; Mezias & Starbuck, 2003) in analytics and the Internet of Things. It uses the construct of cognitive analytics (Beugré, 2015) to emphasize the importance of integrating these concepts in analytics. Doing so is particularly important because “the gap between relevant analytics and users’ strategic business needs is significant” (Kohavi, Rothleder, & Simoudis, 2002, p. 45). The significance of this gap is related to a variety of factors, including cycle time, analytical time and expertise, business goals and metrics, goals for data collection and transformation, distributing analytics results and integrating data from multiple sources (Kohavi et al., 2002). Although organizations consider analytics important, they often lack adequate strategies to leverage its advantages (Barton & Court, 2012; Davenport, 2014; McAfee, & Brynjolfsson, 2012).
A cognitive analytics perspective has implications for research and practice. From a research perspective, such an approach could lead to both inductive and deductive methodologies. For example, without formulating explicit hypotheses, scholars could analyze existing data to draw inferences and develop theories. They could also test these theories using big data analytics. A cognitive analytics approach could also add to the organizational science literature on analytics. Although the use of big data and analytics is increasing in today’s corporate environments, “there is very little published management scholarship that tackles the challenges of using such tools, or better yet, that explores the promise and opportunities for new theories and practices that big data must bring about” (George, Haas, & Pentland, 2014, p. 321). The relative scarcity of organizational research on big data and analytics is astonishing because topics such as human cognition, decision making, sense making, knowledge and learning are essential in understanding and using the vast amounts of data organizations gather and analyze.
From a practical perspective, a cognitive analytics perspective could allow organizations to integrate the three concepts of the Internet of Things, big data and analytics. In fact, the Internet of Things system can be depicted as a “collection of smart devices that interact on a collaborative basis to fulfill a common goal” (Sicari, Rizzardi, Grieco, & Coen-Porisini, 2015, p. 146). As such, it allows the collection of a large amount of data through various wearable and computer devices (Gubbi, Buyya, Marusic, & Palaniswami, 2013; O’Leary, 2013a; Riggins & Wamba, 2015). Managers may focus more on the value rather than the mere collection and analysis of data to the extent that analytics is more a tool than an end in itself. As such, it is helpful when it is properly used. As Shah, Horne, and Capella (2012, p. 23) put it, “investment in analytics can be useless, even harmful, unless employees can incorporate that data into complex decision making.”
The present chapter is divided into four sections. The first section describes the concept of the Internet of Things and delineates it. The second section presents a brief overview of big data analytics. The third section discusses the construct of cognitive analytics. Finally, the fourth section explores directions for future research and provides guidelines on how organizations and managers could apply the construct of cognitive analytics.
The Internet of Things
The Internet of Things refers to the integration of several wireless technologies (Eloff, Eloff, Dlamini, & Zielinski, 2009; Gubbi et al., 2013, Li, Xu, & Zhao, 2015). “The words ‘Internet’ and ‘Things’ mean an inter-connected world-wide network based on sensory, communication, networking, and information processing technologies” (Li et al., 2015, p. 244). The word ‘things’ refers to physical objects, such as a phone, a tablet or a watch. To be included in the Internet of Things, these objects must have functionalities to communicate with human agents or with other objects. Hence, these objects must be considered ‘smart objects.’ The Internet of Things is characterized by its “comprehensiveness in terms of people, services, and things that generate information populating massive databases” (Eloff et al., 2009, p. 8).
The core concept of the IoT is that everyday objects can be equipped with identifying, sensing, networking and processing capabilities that will allow them to communicate with one another and with other devices and services over the Internet to achieve some useful objective.
(Whitmore, Argawal, & Xu, 2015, p. 261)
The Internet of Things will continue to generate massive amounts of data that need analysis before generating value for individuals as well as organizations (O’Leary, 2013a; Riggins & Wamba, 2015).
Ashton (2009) coined the term ‘Internet of Things’ (IoT) to refer to such technologies that he believes have the potential to change the world. “The IoT builds on three pillars related to the ability of smart objects: (i) to be identifiable (anything identifies itself), (ii) to communicate (anything communicates), and (iii) to interact (anything interacts)—either among themselves, thus building networks of interconnected objects, or with end-users or other entities in the network” (Miorandi et al., 2012, p. 1498). The Internet of Things can provide applications in several sectors, including environmental monitoring, health care, inventory and product management, smart homes and work-places, security and surveillance, to name but a few. It can also provide assistance to disabled people, helping them to live better and more productive lives. The Internet of Things is “going to create a world where physical objects are seamlessly integrated into information networks in order to provide advanced and intelligent services for human-beings” (Yang et al., 2014, p. 120).
According to Miorandi, Sicari, Pellegrini, and Chlamtac (2012), the term ‘Internet of Things’ is used as an umbrella keyword for covering various aspects related to the extension of the Internet and the Web into the physical realm by widespread deployment of spatially distributed devices with embedded identification, sensing and/or actuation capabilities. Ma (2011) contends that the Internet of Things has the following three characteristics. The first is that ordinary objects, such as cups, tables, screws, foods and automobile tires, are instrumented and can be individually addressed by embedding chips and bar codes. The second characteristic is that autonomic terminals are interconnected. This implies that the instrumented physical objects are connected as autonomic network terminals. The third is that the pervasive services are intelligent and represent an extensively interconnected network, thereby letting every object participate in the service flow to make the pervasive service intelligent. Ma (2011) also identified four layers of the Internet of Things (see Table 3.1): 1) object-sensing layer, 2) data exchange layer, 3) information integration layer and 4) application service layer. The object-sensing layer handles sensing the physical objects and obtaining data; the data exchange layer handles transparent transmission of data; the information integration layer handles recombination, cleaning and fusion of uncertain information acquired from the networks and integrates the uncertain information into usable knowledge; the application service layer provides content services for various users.
Table 3.1 The Architecture of the Internet of Things
| Layers | Characteristics |
|
|
|
| Object-sensing layer | Handles sensing the physical objects and obtaining data. |
| Data exchange layer | Handles transparent transmission of data. |
| Information integration layer | Handles recombination, cleaning and fusion of uncertain information acquired from the networks. |
| Integrates the uncertain information into usable knowledge. | |
| Application service layer | Provides content services for various users |
It is worth acknowledging that the core concepts of the IoT are not new. In fact, the idea of communication between machines themselves and between machines and humans is not also new. What is new, however, is that the IoT has expanded the notion of communication between technological devices of all kinds. As Whitmore et al. (2015) note, “what the IoT represents is an evolution of the use of these existing technologies in terms of the number and kinds of devices as well as the interconnection of networks of these devices across the Internet” (2015, p. 262). In this regard, the Internet of Things is a technological revolution in computing and communications and one of the main drivers of big data analytics.
Big Data Analytics
Although the two terms big data and analytics are often used in the same phrase, they are different (McAfee & Brynjolfsson, 2012). “Big data refer to large and varied data that can be collected and managed” (George, Osinga, Lavie, & Scott, 2016, p. 1493), whereas analytics refers to “the extensive use of data, statistical and quantitative analysis, explanatory and cognitive models, and fact-based management to drive decisions and actions” (Davenport & Harris, 2007, p. 7). Big data is characterized by high volume, high velocity and high variability (Tien, 2013; Davenport, 2014). The combination of the two terms gives rise to big data analytics, which is defined as technologies and techniques that a company can employ to analyze large-scale, complex data for various applications intended to augment firm performance in various dimensions (Kwon, Lee, & Shin, 2014, p. 387).
Big Data
Although size is one of the key attributes of big data, other attributes, such as velocity, variety and value, are equally important. Volume refers to the magnitude of the data, whereas velocity indicates the speed with which data are collected, stored and analyzed. Variety indicates the multidimensional nature of data sources. For example, data are generated from several sources, including online transactions, emails, videos, images, logs, posts, search queries, health records, social networking interactions, science data, sensors, mobile phones, home appliances and any other technologies that can communicate with humans or other technologies. The Internet of Things adds a multitude of data sources throughout organizations and society (Loebbecke & Picot, 2015). Big data can also be structured, unstructured or semi-structured.
Value refers to the extent to which the data can be used to benefit decision makers. One attribute of value is the veracity of the data; because there are a variety of data sources, it is important to assess data reliability. Zakir, Seymour, and Berg (2015) note that big data analytics reflect the challenges of data that are too vast, too unstructured and too fast moving to be managed by traditional methods (p. 81). The data collected must be “aggregated, fused, processed, analyzed, and mined in order to extract useful information to enable intelligent and ubiquitous services” (Yang et al., 2014, p. 120). Relying on big data and analytics requires the organization to change its decision-making culture (Barton & Court, 2012; McAfee & Brynjolfsson, 2012).
Analytics
Analytics is defined as a group of approaches, organizational procedures and tools used in combination with one another to gain information, analyze that information and predict outcomes of solutions to business problems (Bose, 2009). It has been applied to several areas of organizations, including decision making, finance, human resources management, marketing and supply chain management (Davenport & Harris, 2007; Davenport, Harris, & Morrison, 2010). Analytics is transforming how decisions are made in organizations (Davenport, 2006, 2014; Davenport & Harris, 2007; Davenport et al., 2010; McAfee & Brynjolfsson, 2012; Barton & Court, 2012; Chen, Chiang, & Storey, 2012).
To realize the benefits of analytics, managers need to master the data and the analysis. Analytics can help an organization “better understand its business and markets” and “leverage opportunities presented by abundant data and domain-specific analytics” (Chen et al., 2012, pp. 1166–68). Indeed, insight must be translated into strategic decisions that can benefit the organization. Sharma, Mithas, and Kankanhalli (2014) propose that the first-order effects of business analytics are likely to be on decision-making processes and that improvements in organizational performance are likely to be an outcome of superior decision-making processes enabled by business analytics. “Insights do not emerge automatically out of mechanically applying analytical tools to data. Rather, insights emerge out of an active process of engagement between analysts and business managers employing the data and analytic tools to uncover new knowledge” (Sharma et al., 2014, p. 4). Lavalle, Lesser, Shockley, Hopkins, and Kruschwitz (2011) note that top-performing organizations “make decisions based on rigorous analysis at more than double the rate of lower performing organizations” and that in such organizations analytic insight is being used to “guide both future strategies and day-to-day operations” (p. 22).
Cognitive Analytics
Hogarth and Soyer (2015) note that the usefulness of an analysis depends not only on how well it is executed, but also on how well the results are understood by the intended audience. This requires that the results be communicated to decision makers in an effective manner such that “simulated experience exploits humans’ natural ability to transform complicated information into actionable knowledge” (Hogarth & Soyer, 2015, p. 51). Managers increasingly express interest in the use of big data and analytics because of market complexity and the availability of better analytics tools and data (Kiron et al., 2014). Although analytics is now considered an important tool for businesses, the mere availability of data is not enough to provide a competitive advantage.
What is important is how organizations make sense of the data they collect. As a consequence, “big data’s power does not erase the need for vision or human sight” (McAfee & Brynjolfsson, 2012, p. 66). In this regard, analytics is more a cognitive process than a mere computation of numbers, however accurate they may be. To improve the use of analytics, it is important to combine the use of data with a deep understanding of the domain in which the data are collected and analyzed.
To explore the challenges facing the effective use of big data analytics, I discuss the concept of cognitive analytics. As indicated earlier, this perspective draws from the concepts of interpretation and sense making (Weick, 1995; Mezias & Starbuck, 2003). “Interpretation is the process of translating these events, of developing models for understanding, of bringing out meaning, and of assembling conceptual schemes among key managers” (Daft & Weick, 1984, p. 286). Likewise, the basic idea of sense making is that reality is an ongoing accomplishment that emerges from efforts to create order and make retrospective sense of what occurs (Weick, 1993, 1995). Hence, sense making emphasizes the tendency for people to try to make things rationally accountable to themselves and others. For example, people do not discard their current beliefs and methods as long as they produce reasonable results (Kuhn, 1962). As a consequence, the use of data is not likely to reduce the reliance on personal experiences, intuition and perceptions. Hence, the literature on big data analytics and the IoT must also incorporate decision makers’ cognitions. In a study on the implementation of analytics at a Fortune 500 financial services company, Barbour, Treem, and Kolar (2017) found that practitioners needed to manage existing relationships or form new relationships with experts who possessed the data they needed, who could help them make sense of them and who could, at times, collaborate with them to generate interpretations.
Figure 3.1 describes the link between the Internet of Things, big data analytics and decision making. As illustrated in the figure, data can be collected from several devices that are included in the Internet of Things. These objects can communicate with one another and/or with humans. Interactions between objects and between objects and humans generate data. As discussed earlier, the data generated are characterized by four elements: volume, velocity, variety and value. The third box in the figure relates to the analysis of the data collected. Generally, descriptive as well as predictive analytical tools could be used to analyze the data collected and display the results. However, to make sense of the analysis, it is important for decision makers to have an understanding of the domain in which data are collected and analyzed (cognitive analytics).
The construct of cognitive analytics used in this chapter includes two dimensions: human and technological. The first dimension relates to human cognition and the brain’s ability to process information. In this regard, cognitive analytics is construed as a mechanism through which people make sense of the data analyzed. This sense-making process requires an understanding of the meaning of the data as well as the patterns identified. An illustrative example is one of a marketing manager. To make sense of data related to customers, not only must the marketing manager analyze the data but he or she must also have a deep understanding of the market and customers. Lack of such knowledge would render the data unnecessary or insufficient; thus, “big data, no matter how comprehensive or well analyzed, needs to be complemented by big judgment” (Shah et al., 2012, p. 25). This big judgment requires people be well informed about making effective data-driven decisions. The human aspect of cognitive analytics includes three elements: 1) ability to process information, 2) knowledge and expertise and 3) heuristics and cognitive biases.
Figure 3.1
The Internet of Things and Cognitive Analytics
The second dimension of cognitive analytics deals with technological tools and their capacity to reproduce functions of the human brain. Here, cognitive analytics relies on technological systems to generate hypotheses, drawing from a wide variety of potentially relevant information and connections (Ronanki & Steier, 2014). The main technological aspect of cognitive analytics is represented by artificial intelligence. Cognitive analytics takes the view that data analysis is necessary, but not sufficient, to improve decisions. The use of cognitive analytics requires that people become not only experts in their particular areas but also experts in analytical tools. Hence, it implies a combination of the Internet of Things (to collect data), analytics (to analyze the data) and cognitions (to make sense of the data).
Making sense of the data generated requires the availability of qualified personnel; according to McAfee and Brynjolfsson (2012), “the power of big data does not erase the need for vision or human insight” (p. 66). However, as Manyika et al. (2011), from the McKinsey Global Institute, report, the United States faces a shortage of 140,000 to 190,000 people with deep analytical skills as well as a shortage of 1.5 million managers and analysts to analyze big data and make decisions based on the findings. The best data scientists are also comfortable speaking the language of business and helping leaders reformulate challenges in ways that big data can tackle; it is important to combine domain expertise with data science (McAfee & Brynjolfsson, 2012). It is also equally important to combine the Internet of Things and analytics with cognitions, experience and intuition.
Implications for Research and Practice
This chapter presented the perspective that the Internet of Things has the capacity to generate vast amounts of data that can be analyzed by organizations and individuals to improve decisions. In this regard, the Internet of Things and big data analytics can add value to organizations and individuals. The Internet of Things and big data can help to improve decision making and strategies in organizations (McAfee & Brynjolfsson, 2012), while facilitating personal augmentation (Wilson, Shah, & Whipple, 2015). However, decision makers need to master the data and their analysis if they want to reap the potential benefits of this tool. Indeed, analytics can help an organization “better understand its business and markets” and “leverage opportunities presented by abundant data and domain-specific analytics” (Chen et al., 2012, pp. 1166–68). Insight must be translated into strategic decisions that can benefit the organization or the individual decision maker. The cognitive analytics perspective provides opportunities for research not only on the link between the Internet of Things and big data analytics, but also on the necessity to emphasize the importance of expertise and sense making. It also offers opportunities for management practice.
Implications for Further Research
A cognitive analytics perspective provides opportunities for research on two dimensions: the human dimension and the technological dimension. In considering the human dimension, organizational scholars could focus on the role of expertise, knowledge, cognitions and heuristics in research on the Internet of Things and big data analysis. For example, they could explore the extent to which expertise in a particular domain leads to better decisions. Particularly, they could determine whether organizations that have data scientists with deep knowledge in business and management tend to make better use of big data analytics than those organizations that do not have such data scientists. It is also important to consider whether patterns found in data are really significant, meaningful and relevant; the volume of the data may render minor relationships statistically significant. However, a clear understanding of the domain may help differentiate truly significant and relevant relationships from data noise.
The human dimension may also consider the role of cognitive biases in data-driven decisions. It is important to acknowledge that the availability of data does not compensate for human errors and biases. Kahneman, Lavalo, and Sibolly (2011) noted that when making decisions managers often faced three types of biases: confirmation bias, anchoring bias and loss aversion bias. Confirmation bias leads managers to look for information and trends that validate previously held assumptions. Anchoring bias leads decision makers to weigh heavily one piece of information over another, and loss aversion bias leads decision makers to be more cautions in weighing options. To some extent, acknowledging these biases may help managers reduce them.
The technological dimension of cognitive analytics provides opportunities for further research on the role of artificial intelligence in improving decision making. It is obvious that artificial intelligence is replacing humans in making certain decisions (Simon, 1995; Pomerol, 1997; Nemati, Steiger, Iyer, & Herschel, 2002). Simon (1995) contends that cognitive mechanisms involved in scientific discovery are a special case of general human capabilities for problem solving. This reasoning led to the development of artificial intelligence as both a science and a set of tools to improve decision making (Simon, 1995; Nemati et al., 2002). For example, machine learning is a concept in which computers can learn new things based on the data they analyze and can act on these learnings. Machine learning systems use previously acquired information to make sense of new data. These systems can improve the ability to analyze and use data efficiently and effectively. Artificial intelligence allows delegation of difficult pattern recognition and learning. As such, it contributes to the volume, velocity and variety of data (O’Leary, 2013b). Hence, exploring the reliability of such decision-making tools could be a particularly fruitful research avenue for organizational scholars. As a consequence, big data analytics will be needed to make sense of this large amount of data. Hence, organizational scholars must pay particular attention to the relationship between the Internet of Things and big data analytics.
Implications for Practice
A cognitive analytics perspective of the Internet of Things and big data analytics provides guidelines for management practice, policy making and the betterment of individual lives. From a managerial perspective, cognitive analytics could help organizations recruit data scientists who combine both the skills required to analyze the data and knowledge needed to put this analysis into context. Doing so could help improve decision making. From the policy-making perspective, a cognitive analytics perspective could help policy makers understand that analysis and data are only tools. As such, only a deep understanding and knowledge of societal trends and problems could help make sound policies. Data and analysis can help, but by themselves they cannot substitute for lack of knowledge, expertise and sound judgment.
At the individual level, a cognitive analytics perspective could help citizens improve their personal lives. For example, data related to personal health could be beneficial to citizens only if these citizens themselves understand the meaning of the data and are willing to integrate them in their daily routines. Doing so is important because over the next decade, big data will change the landscape of social and economic policy and research (George et al., 2014). Similarly, the Internet of Things will become more ubiquitous in the future and numerous data will be collected through various wearable devices. Collecting and analyzing this data could prove a useful means for managers and individuals to make informed decisions. For example, in 2016, AT&T started a Smart City Initiative aimed at deploying Internet of Things solutions to cities across the United States (Frost & Sullivan White Paper, 2016). One of the main objectives of this initiative is to improve the quality of life, transportation and personal security in cities. Likewise, utility companies such as Enel in Italy and Pacific Gas and Electric (PG&E) in the United States, are deploying ‘smart’ meters that provide residential and industrial customers with visual displays showing energy usage and the real-time costs of providing it (Chui, Loffler, & Roberts, 2010).
However, we must acknowledge that the ubiquitous nature of the IoT is also raising concerns for privacy and security (Weber, 2010; Roman, Zhou, & Lopez, 2013; Jing et al., 2014). For example, Jing et al. (2014, p. 2482) note that the “IoT not only has the same security issues as sensor networks, mobile communications networks and the Internet, but also has its specialties such as privacy issues, different authentication and access control network configuration issues, information storage and management.” Indeed, “applications of IoT can bring convenience to people, but if it cannot ensure the security of personal privacy, private information may be leaked at any time” (Jing et al., 2014, p. 2482). To address the security challenges, companies are turning to several technological mechanisms, such as virtual private networks and transport layer security (Weber, 2010). However, protecting the Internet of Things is a complex task because the threats that can affect it are numerous and include attacks that target diverse communication channels, physical threats, denial of service and identity fabrication, to name but a few (Roman et al., 2013). These challenges must be successfully navigated for the Internet of Things to live up to its promises of improving our lives.
Conclusion
Drawing on insights from cognitive science (Simon, 1980), this chapter developed a model of cognitive analytics to explore the relationship between the Internet of Things, big data analytics and decision making. Central to this model is the role of cognition, interpretation and sense making. Although a cognitive analytics perspective of the Internet of Things and big data analytics is promising, there is a paucity of management research addressing this topic (George et al., 2014, 2016). Hence, the chapter calls for further research on the role of cognition, interpretation and sense making in using big data and analytics in the age of the Internet of Things.
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