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Big Data and AI in Customer Support

A study of Big Data and AI in customer service with a focus on value-creating factors from the employee perspective

Written by A. Licina

Paper category

Master Thesis

Subject

Computer Science

Year

2020

Abstract

Thesis Big data: Big data is a well-known term used to explain the use of information and the exponential growth and accessibility of data (Laney 2001). In addition, big data is usually described by three Vs. The three Vs are commonly used, referring to Volume, Variety and Velocity. The massive amount of data generated from endless sources is big data. These data have occupied a large part of our daily life and our organization (ibid.). In addition, as mentioned earlier, the three Vs proposed by Laney (2001) have long been widely used as a framework for characterizing big data elements. However, in the latter, Lomotey and Deter (2014) proposed two other Vs to the framework. In addition to the three Vs of Laney (2001), the author also introduced authenticity and value. The five Vs will be described in more detail in the following sections. Volume: Related to the amount of data collected by the company. The collected data must then be processed so that basic knowledge can be obtained. Today's organizations are flooded with large amounts of various forms of data, and the amount of information per day can easily reach terabytes or even petabytes. Speed: Related to the amount of time that data can be processed. Certain tasks and processes are very important and require immediate response, such as fraud detection. Therefore, in order to maximize efficiency, fast processing is essential. Diversity: Focus on the different types of data that big data can contain. As mentioned earlier, these data can be structured or unstructured, and can take many different forms, such as text, audio, video, clickstream, and so on. Can be created to the expected process. Authenticity: refers to the degree to which managers trust the data and make decisions based on the collected information. Therefore, extracting appropriate correlations in big data sets is essential for decision making (Hiba, Hameed, Hadishaheed, and Haji 2015; Debattista, Lange, Scerri, and Auer 2015). In addition, today's businesses have access to large amounts of data, and these data are often used for analysis. In addition, as data continues to increase, it is more important than ever to be able to capture and analyze real-time data and implement real-time decision-making (Baro, Degoul, Beuscart & Charm 2015; Gandomi & Haider 2015). In addition, there are two types of big data: Structured data and unstructured data. Structured data refers to data characterized by a set of rules, which are refined and have a predictable format. 2.1.1 Big data analysis As mentioned in the previous section, the growth of big data is constantly evolving. At present, most companies are paying attention to how to manage and analyze these big data streams. BDA is regarded as the frontier of innovation, production and competition (Manyika, Chui, Brown, Bughin, Dobbs, Roxburgh & Byers 2011). Today, many companies are accepting big data analysis to improve decision making to make faster and more accurate decisions and benefit from big data. Later, when talking about the value that big data analysis can bring to enterprises and how it can help achieve the established goals in the organization, scholars and professionals have been very concerned (Mikalef, Pappas, Korgstie & Giannakos 2017). The commonly used definition of BDA is as follows: “a new generation of technology and architecture designed to extract value from a large amount of various data in an economical way through high-speed capture, discovery, and/or analysis” (Mikalef et al., 2017). Constantiou and Kallinikos (2015) also pointed out that many organizations today are adopting big data analytics to facilitate better and faster decision-making and benefit from big data. The reason for the increasing interest in big data analysis is that as the old technology based on traditional data management is no longer effective, the ever-increasing amount of data requires new analysis methods. In addition, the demand for methods and tools for big data analysis is also growing (Constantiou et al., 2015). In the field of customer service, BDA allows organizations to evaluate various complex data and data sets from many different sources to gain a basic understanding of customer behavior. These insights then help improve sales, customer service, and competitive advantage (Kitchens, Dobolyi, Li, and Abbasi, 2018). In addition, there are three types of analysis: descriptive, predictive, and prescriptive. 2.1.2 Artificial Intelligence Artificial intelligence (AI) is defined by many scholars in many different ways. Generally speaking, artificial intelligence refers to the intelligence of machines, which can be implemented in various environments such as organizations, industries, and cities to achieve specific goals such as improving overall operational efficiency (Kaplan & Haenlein 2019). The Oxford Dictionary defines artificial intelligence (AI) as "the theory and development of computer systems capable of performing tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. Read Less