Abstract
Big data attracts researchers and practitioners around the globe in their desire to effectively manage the data deluge resulting from the ongoing evolution of the information systems domain. Consequently, many decision makers attempt to harness the potentials arising with the use of those modern technologies in a multitude of application scenarios. As a result, big data has gained an important role for many businesses. However, as of today, the developed solutions are oftentimes perceived as completed products, without considering that the application in highly dynamic environments might benefit from a deviation of this approach. Relevant data sources as well as the questions that are supposed to be answered by their analysis may change rapidly and so do subsequently the requirements regarding the functionalities of the system. To our knowledge, while big data itself is a prominent topic, fields of application that are likely to evolve in a short period of time and the resulting consequences were not specifically investigated until now. Therefore, this research aims to overcome this paucity by clarifying the relation between dynamic business environments and big data analytics (BDA), sensitizing researchers and practitioners for future big data engineering activities. Apart from a thorough literature review, expert interviews are conducted that evaluate the made inferences regarding dynamic and stable influencing factors, the influence of dynamic environments on BDA applications as well as possible countermeasures. The ascertained insights are condensed into a proposal for decision making, facilitating the alignment of BDA and business needs in dynamic business environments.
Highlights
While data have, at least to some extent, always played a role in the business world, the ability to gather and utilize them has dramatically increased over the course of the last two decades [1]
To evaluate how companies already handle the dynamic nature of certain big data sources and processing systems, and what consequences may result from this, a structured literature review (SLR), which “distills the existing literature [...] to summarize the state of the art in this subject field” [38], is conducted
Businesses that are reliant on highly volatile data sources or frequently change their business questions, respectively the specifics of their analytics, face particular challenges that surpass those of less dynamically positioned companies
Summary
At least to some extent, always played a role in the business world, the ability to gather and utilize them has dramatically increased over the course of the last two decades [1]. Apart from the overall increase of revenue by providing new or enhanced services, this includes many more avenues, as, for instance, improvements of organizational activities, such as monitoring, optimizations or decision making [3], resulting in noticeable competitive advantages. This esperance is supported by many researchers [8]–[12], including Müller et al [13], who conducted a corresponding study to quantify this effect. In the course of their work, they found that the use of big data is associated with an average productivity increase of approximately four percent. There is a huge number of publications dealing with the topic of big data [14]
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