# On a Certain Research Gap in Big Data Mining for Customer Insights
## Theoretical Lens
### Big Data Issues  
In a world overflowing with information, which is particularly evident in information societies with access to the Internet, the term \"big data\" is indispensable and interconnected. Nowadays, data are being sent to the global network not only by people who do it consciously and manually (e.g., via social networks or e-mails) but also by all kinds of sensors and with the use of cloud computing. It is a Web 3.0 domain of which big data is one of the main pillars [59]. Big data, as a nascent concept, has a rather turbulent history of trying to define it, and an attempt to organize the definitions was made by [PERSON] and [PERSON] [60]. The common axis of all definitions is the perception that big data, which can be viewed as a 'new era' of the data-driven paradigm, has opened up new possibilities for the improved decision support [61]. As said in Section 1, big data can also be defined using 7V characteristics, but when examining the temporal aspects of big data, the most important is the concept of velocity. Big data is not only vast and dynamic, but it also necessitates the use of cutting-edge technologies to analyze and process [62]. Big data is distinguished from the traditional \"data\" notation, because big data, due to its stupendous size, cannot be processed and managed with conventional data mining tools [63; 64]. The concept of big data mining (BDM) is intimately associated to conventional data mining notation. These two concepts differ mainly in the methods of obtaining data, and not in the idea itself. BDM enables to obtain useful information from databases or data streams that are huge in terms of \"big data V's\", like volume, velocity, and variety [65]. The main functions of data mining in general are descriptive functions (such as clustering, association, and pattern mining) and predictive functions (such as classification, time series analysis, etc.) These functions (enablers) differ only slightly from each other, mainly in terms of the temporal reference, i.e., descriptive functions mainly concern the present and settled dependencies, and the predictive functions mainly refer to the study of the future and tentative dependencies. Having collected (big) data, it is necessary to analyze it to extract information hidden in it. In this case, it is crucial to use big data analytical tools. BDA was created in response to the need to analyze vast volumes of quickly collected complex data. As a result, data acquisition and processing occur at a high pace, which is impossible to achieve with calcareous computational methods [66]. Big data analytics, as being a big data derivative, can also be described with big data \"V\" characteristics. The ultimate and the pivotal step of the whole BDA process is \"action on insight\", as [PERSON] et al. [67] claim. The use and implementation of BDA, as one of the most principal factors for engendering meaningful insights for decision-making [68,69], is crucial to extract value from the multitude of data being obtained. An organizational capability to handle BDA has recently become mainstream to create value [70]. It should be noted, however, that the blossoming of BDA potential in organizations can be withheld due to the lack of IT infrastructure, data storage facilities and organization strategy [71]. The use of BDA is seen in a variety of realms [72, 73, 74, 75, 76, 77, 78, 79]. Apart from technical uses, BDA is applicable in the economic fields among which the social media analytics (SMA) for revealing customers' behavior has a prime importance [80]. This is because of the data volume: as of January 2021, there were 2.74 billion users on Facebook, 1.22 billion on Instagram and 353 million on Twitter [81]. Considering the multitude of posts shared by each user, this constitutes a potential mine of knowledge and a significant area for analysis, including segmentation, prediction, etc.