# On a Certain Research Gap in Big Data Mining for Customer Insights
## 1 Introduction  
Obtaining a sustainable competitive advantage largely depends on the organization's analytical skills, including the ability to analyze customers' needs and demands [1, 2]. Research has shown that organizations which rely on advanced data analysis perform much better than others, both financially and operationally [3]. For several years, organizations have been gaining a new source of data for analysis, known as big data. [PERSON] et al. [4] show in a detailed manner how various resources and contextual factors lead to performance gains from big data analytics (BDA). However, organizations are still facing the problem of efficient analysis and understanding of this data. This is due to the features of big data, which are the most often described as the so-called 7V, i.e., Volume, Velocity, Variety, Veracity, Variability, Visualization, Value [5]. In a modern fast-changing economy, the Velocity and Variability dimensions become particularly important--the features that cause the most problems in the analysis process are the speed of data inflow and their variability and thus the temporal dimension of big data. This dimension is interconnected with the fundamental assumption that time is an inseparable element influencing the phenomenon of big data and the process of analyzing this data. The temporal dimension manifests itself in three ways: as the fourth dimension of space-time, as the logical sequence of events (which are described by big data), and as a direct determinant of these events (cf. [6]). The time dimension concerns both the phenomenon of data inflow and the reality reflected in these data, because the organization's environment is changeable. The formal description of time and temporality is given, e.g., in [7]. One of the most important volatile elements influencing the reality of an organization is the image of its customers. This image comes in the form of customer insights, defined as the profound \"knowledge about customers\" [8] that is knowledge about their needs,expectations, and opinions which drives organizations' innovative efforts and business processes [9] (cf. Section 2.1). Extracting value from customer insights is identified as a pronounced challenge for organizations which becomes even greater when these insights are searched for in the vast area of big data [10; 11]. To effectively analyze big data about customers, organizations must therefore adapt to their dynamics. Only in this way will they obtain current and valuable knowledge about trends and market expectations [11]. Such advanced analysis of clients is the way to acquire, maintain, engage, and satisfy them in an effective manner [11; 12]. Customer analytics is strictly related to time and dynamics as it brings behavioral insights about customers by finding hidden patterns in (big) data. Hence, big data consumer analytics may be defined as \"the extraction of hidden insights about consumer behavior from big data and the exploitation of that insight through advantageous interpretation\" [13]. The dynamic characteristics of customers' opinions, expectations and behavior are already subject of research [12; 14], but the temporal dimension of customer data is not addressed directly. As [PERSON] et al. [15] point out, big data needs to be analyzed in a time-sensitive manner. This applies also to customer big data. If customer behavior and opinions were tracked as they change in time, the company would be more customer-oriented, which is the vital step on the road to market success. Our previous research [16] also revealed the need for explicit incorporation of time aspect into big data analytics. Processing and analyzing big customer data are possible thanks to the use of big data analytics tools. BDA, which has transformed the world of business management, has been dubbed a \"game-changer\" [17]. BDA is defined in the literature as \"methodologies used to interpret and obtain information from big data-helps\".