In this literature review, three academic sources are analyzed to identify the main motivations of businesses to employ big data approaches. The motivations are divided into four categories: dealing with feedback, opportunities for growth, desire to control, and perceived benefits. It is shown that companies face immense amounts of information, and their reaction is the attempt to process and analyze it with the intention to gain a better understanding of how they should develop and better tools for successful operation.
Within recent years, much academic attention has been dedicated to the use of advanced data science approaches, including big data approaches, by businesses from all over the world. One of the focuses of these studies is the motivation behind the decision to employ big data approaches as tools of business intelligence. Researchers have stressed that big data is still a debatable concept, and its applicability requires further research, as well as its validity requires more definitive confirmations (Carter & Sholler, 2015).
However, in practice, many businesses appear to employ big data analysis and base their decisions on its results, which suggests that there are perceived needs for that that need to be addressed. To evaluate the intention to use big data, researchers have explored four main areas: dealing with feedback, looking for opportunities for growth, striving for control, and additional perceived benefits.
First of all, businesses recognize the need to deal with extensive feedback they receive from their existing or potential audiences. Most today’s businesses face the necessity to actively communicate via various available channels, including new media, such a social networking services. A major characteristic of such communication methods is that they produce large amounts of feedback. Businesses need to process feedback to ensure that they can adequately address the needs of the market and build long-term relationships with customer groups (Carter & Sholler, 2015). However, with the expanding feedback, the processing becomes increasingly difficult.
It is no longer sufficient to respond to direct appeals from customers; instead, businesses become motivated to find and analyze information that is related to what they do and can be considered feedback from customers despite the fact that is not delivered to the businesses by their customers directly. In order to “build a feedback mechanism to encourage managers to read information for good decision-making” (Chang, Hsu, & Wu, 2015), managers employ data science and big data approaches.
Second, a company that is committed to improvement and strategic development constantly seeks opportunities for growth, and big data can help in detecting those. Carter and Sholler (2015) list creativity and curiosity among the main motivations of data scientists. Despite having been explored in thousands of studies, big data still remains obscure, and it reflects many phenomena about the way the modern world functions that researchers do not fully understand today.
This is why the area of data science if attractive: it is innovational and insightful, providing new solutions and ideas. Proper data analysis allows businesses to identify directions and aspects of operation in which they can improve. Another motivation explored by Carter and Sholler (2015) is solving interesting problems. It can be argued that individuals with many different professions may be driven by this same motivation, but data science provides special opportunities for this because it inherently explores problems of various kinds and identifies many challenges the can promote growth. Therefore, for businesses, big data becomes an area for research and a source of recommendations
Third, the willingness to employ big data approaches can be explained by the businesses’ desire to control their development and processes affecting it. Opportunities are not the only thing companies pursue; moreover, their behaviors can be explained by the attempts to control things more often than by the search for new opportunities. With the immense amounts of information, an entity operating in the market can feel vulnerable because it does not understand complex factors that may affect it. As Janssen, van der Voort, and Wahyudi (2017) put it, “[T]he amount of data influences the possible level of control” (p. 342).
Chang et al. (2015) address this issue by exploring the businesses’ intentions of “reading information, exchanging reports, and creating reports” (p. 281). Big data approaches allow structuring available data into intelligible and manageable knowledge systems that present an integrated understanding of a business’s operation, and such systems are needed to ensure control. However, it should also be acknowledged that this control can be illusory and deceitful because of many challenges associated with the use of big data. Janssen et al. (2017) identify three such challenges: processing and manipulation, noise, and errors.
There are various threats to businesses associated with cases where data is misinterpreted, presented in an inaccurate or incomplete form, or simply miscalculated. This is why companies should not heavily rely on the control over their operation provided by big data.
Finally, companies that employ big data approaches can expect various benefits that are not directly connected to the essential needs, such as those listed above. To explain such perceived benefits, Chang et al. (2015) use the expectancy theory and the social exchange theory. Concerned about benefit factors, including tangible and intangible rewards and reputation, companies may demonstrate intentions to describe, process, and comprehend as much data around them as possible.
With the growing amount of available information, processing it becomes increasingly challenging, which is why big data approaches, as they essentially facilitate processing, attract more attention. One of the main perceived benefits is improved decision-making (Janssen et al., 2017). It is acknowledged that lacks of information cause poor quality of decisions made by managers and leaders; however, mere availability of information does not ensure that this quality will be high.
Even when a lot of information is available, it may not be helpful because the important things for decision-making are appropriate interpretation of information and its application. Data science and big data approaches provide tools that can translate available information into particular practices and decisions.
Upon reviewing academic sources, it can be concluded that businesses tend to use big data because they want to deal with feedback from their audiences more effectively, to develop with the right considerations, to control information flows that affect them, and to improve their internal operation, which particularly includes better decision-making practices. It is widely stressed that further research on big data is required, as some suggest to pay more attention to the perspective of data science analysts (Carter & Sholler, 2015), and others recommend comparing examples of big data use and intentions behind it in different industries (Janssen et al., 2017) and countries (Chang et al., 2015).
Also, in addition to perceived motivations of businesses, objective factors should be studied that may drive them to use big data, which will contribute to a more profound understanding of big data utilization.
References
Carter, D., & Sholler, D. (2015). Data science on the ground: Hype, criticism, and everyday work. Journal of the Association for Information Science and Technology, 67(10), 2309-2319.
Chang, Y. W., Hsu, P. Y., & Wu, Z. Y. (2015). Exploring managers’ intention to use business intelligence: The role of motivations. Behaviour & Information Technology, 34(3), 273-285.
Janssen, M., van der Voort, H., & Wahyudi, A. (2017). Factors influencing big data decision-making quality. Journal of Business Research, 70(1), 338-345.