Internet of Things and Big Data-Driven Data Analysis Services for Third Parties: Business Models, New Ventures, and Potential Horizons

Internet of Things and Big Data-Driven Data Analysis Services for Third Parties: Business Models, New Ventures, and Potential Horizons

Izabella V. Lokshina (SUNY Oneonta, USA), Barbara J. Durkin (SUNY Oneonta, USA) and Cees J. M. Lanting (DATSA Belgium, Belgium)
DOI: 10.4018/978-1-5225-8188-8.ch014

Abstract

Ubiquitous sensing devices, enabled by wireless sensor network (WSN) technologies, cut across every area of modern day living, affecting individuals and businesses and offering the ability to measure and understand environmental indicators. The proliferation of these devices in a communicating-actuating network creates the internet of things (IoT). The IoT provides the tools to establish a major global data-driven ecosystem with its emphasis on big data. Now business models may focus on the provision of services (i.e., the internet of services [IoS]). These models assume the presence and development of the necessary IoT measurement and control instruments, communications infrastructure, and easy access to the data collected and information generated. Different business models may support creating revenue and value for different types of customers. This chapter contributes to the literature by considering, for the first time, knowledge-based management practices, business models, new ventures, and new business opportunities for third-party data analysis services.
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Introduction

The much-discussed Internet of Things (IoT) provides a set of tools enabling a major, global data-driven ecosystem to develop devices (or Things) encompassing everything from pedometers to seismographs, collect data and produce unprecedented amounts of information about the parameters and items in the world around us. When put in the hands of people and businesses, this information can make every area of life, including business, more data-driven.

Things are not really a new concept. We've been using sensors to collect scientific data for centuries. What's different now is the interconnection of all these devices, producing ever more granular data sets, all while that data is becoming more and more accessible, potentially to everyone. To put that data to work, we need to make sense of it. When massive amounts of data become accessible and understandable, the implications are enormous for civic life, personal health and business.

Until now, much attention and effort have gone in the development of business models for the provision of services in this data-driven ecosystem in the context of the IoT, sometimes referred to as the Internet of Services (IoS). These business models assume the existence and development of the necessary IoT measurement and control instruments, communications infrastructure, and easy access to the data collected and information generated by any party. However, not every business model may support opportunities that generate revenue or value or are suitable for different types of customers. Other business models should also be considered.

In this chapter, the authors investigate knowledge-based management practices, business models, new ventures and potential business opportunities for third-party data analysis services. The goal is to give a reasonable, qualitative evaluation, from theoretical and practical viewpoints, of knowledge-based management practices and business models, strategic implications and new business opportunities for American and European small and medium enterprises (SMEs) that use IoT and Big Data techniques to support their innovative performance.

A discussion of the infrastructure is outside the scope of this chapter. The authors assume that a significant time will be needed for deployment. Regulatory clarity and appropriate permissions in addition to possible privacy and national security issues must be addressed.

This chapter makes several important contributions to the literature. First, the chapter considers knowledge-based management practices, business models, new ventures and potential business opportunities for third-party data analysis services. It complements other research about the positive effect of knowledge management on companies’ innovative performance. Second, the chapter discusses access to information generated by third parties in a new context of analysis, i.e. as a prerequisite to data analysis services and in relation to Big Data techniques and new business opportunities. It complements prior research done by the authors on access to data from third parties by Big Data analyzers. Third, the chapter evaluates strategic implications and business opportunities for American and European small and medium enterprises (SMEs) that use the IoT in their data-driven ecosystems. It complements previous research about the positive effects of information and communications technology (ICT) tools and competencies on business performance.

Finally, this chapter also has some social implications since governments are interested in using the latest technologies to promote the best social climate for their citizens. The chapter assumes that an obvious consequence of using the latest technologies will be a broader scope of deliberative democracy. However, the legal framework of the latest technologies is still considered rather vague or absent to a certain extent. Such issues as standardization, service design architecture and models, as well as data privacy and security, create management and governance problems. These issues have not been solved completely by the current service architectures. The latest technologies can also become subject to power politics with the attendant risks of cyberwar, cyber-terrorism and cyber-criminality.

The current research has several limitations. First, the study of a single context of analysis could reduce and limit the generalizability of results. There is, therefore, a need for additional research to determine whether knowledge management and ICT capabilities play the same role in other high-tech contexts. Second, the research is limited by the choice of companies involved. Further research could document additional quantitative and qualitative examples where SMEs have developed strategic opportunities by providing technical and consulting services to other businesses.

Key Terms in this Chapter

Business Venture: A small business. One or more individuals or groups may invest in a venture with the expectation of the business bringing in a financial gain for all backers. Most business ventures are created based on demand of the market or a lack of supply in the market.

Smart World: A concept of a digital world that evolves from the definition of ubiquitous computing and promotes the ideas of a physical world that is richly and invisibly interwoven with sensors, actuators, displays, and computational elements, embedded seamlessly in the everyday objects of our lives, and connected through a continuous network.

Data Analysis: A process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making.

Digital Society: A modern, progressive society that is formed as a result of the adoption and integration of information and communication technologies (ICT) at home, work, education and recreation, and supported by advanced telecommunications and wireless connectivity systems and solutions.

Small and Medium-sized Enterprises (SMEs): Non-subsidiary, independent firms which employ fewer than a given number of employees. This number varies across countries. The most frequent upper limit designating an SME is 250 employees, as in the European Union.

Knowledge Management: The process of creating, sharing, using and managing the knowledge and information of an organization. It refers to a multidisciplinary approach to achieving organizational objectives by making the best use of knowledge.

Big Data: Extremely large volume of data—both structured and unstructured—that inundates a business on a day-to-day basis and may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions.

Big Data Analytics: The process of examining Big Data to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful information that can help organizations make more-informed business decisions.

Ethics: The branch of knowledge that deals with moral principles.

Business model: A design for the successful operation of a business, identifying revenue sources, customer base, products, and details of financing.

Internet of Things (IoT): A system of interrelated computing devices, mechanical and digital machines, objects, animals or people that are provided with unique identifiers (UIDs) and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction.

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