Contents I. Introduction
With today's society producing and capturing more and more data [1], [2], its purposeful utilization has become a driving force for many industries as well as the public sector [3]. This phenomenon is usually referred to by the term big data (BD) [4]. Consequently, the development of the corresponding applications along with the improvement of the underlying tools and techniques have gained tremendous importance and interest among practitioners and scientists alike [5], [6]. Consequently, the corresponding market's value is projected to grow from 241 billion U.S. dollars in 2021 to 655 billion U.S. dollars by 2029 [7], which not only pertains to big corporations but also to small and medium-sized enterprises [8]. Yet, to reap the potential benefits [9], besides the existence of issues that can be solved by or the chance for the discovery of opportunities that can be seized through the analysis of data [10], mainly three aspects have to be regarded [11]. (i) It is important to assure the quality of the utilized data [12]. (ii) Further, the willingness of the responsible decision makers to actually make use of the BD systems and to do so in a sincere manner instead of just trying to justify decisions that were already made beforehand is needed [13]. (iii) Finally, the BD analytics applications themselves have to work properly [14]. However, while the actual creation of the necessary means for BD analytics is widely discussed, their testing is often somewhat neglected [6].
