# Conceptual Framework for Implementing Temporal Big Data Analytics in Companies
## 1 Introduction  
### Motivation  
In the intervening years, it has been already widely recognized that a company's ability to perform advanced analytics is a prerequisite for achieving a competitive advantage [1, 2]. Advanced analytical skills also result in financial and operational gains [3]. The role of analytics has grown immeasurably with the advent of big data as a source of insight, and big data analytics (BDA) has attracted researchers' attention for several years already. However, the efficient processing and analysis of this kind of data is still a problematic issue for a number of companies [4]. This is due to features of big data such as Volume, Velocity, and Variety of data (the so-called 3V), and other distinguishing features, such as Veraicity, Variability, Visualization, and Value--together forming the 7 Vs [5]. Rapid changes in a business environment that companies have to cope with make it necessary to emphasize two dimensions in particular: Velocity and Variability. The problems caused by big data variability are addressed by many big data platforms (Hadoop ecosystem and others) [6]. However, the most problematic feature of big data is its speed of inflow, referred to as Velocity. This is the temporal dimension of big data meaning that big data, as a concept, and the method of interpreting this data are both influenced by the passage of time. Temporal manifestations include the fourth dimension of space-time, the logical sequence of events (defined by big data), and the direct determinant of events (cf. [7]). Because of the ever-changing nature of a company's external environment, the passage of time is an important consideration for both the phenomena of data intake and the realityrepresented in these data. [PERSON] and [PERSON] [8] introduced the notion of temporal big data analytics (TBDA), i.e., the analytics focused on the time dimension of the analyzed field. The proper representation of the time dimension is, _inter alia_, indispensable to [9]:  
* Reason about causal relationships among (business) phenomena;
* Consider changes occurring in time in relations between phenomena or objects;
* Arrange phenomena in time, even if they overlap;
* Learn the dynamics of the development of the phenomenon over time;
* Model the concept of \"possibility\" and therefore infer about possible worlds and/or states;
* Simulate common sense reasoning in, e.g., artificial intelligence (AI) systems. As with any other IT solution, the TBDA implementation in a company requires a set of clear and repeatable processes [10]. It requires an appropriate implementation framework. Unfortunately, researchers working on this issue most often focus on the technical side of BDA implementation [11] whereas clear strategies for value generation from BDA are often missing [12]. Moreover, the existing BDA implementation frameworks do not refer to the big data time dimension whereas the role of time in business and in business analytics is critical. Naturally, temporal big data analytics requires a company to implement new approaches to data processing. Companies need to coordinate activities in several areas simultaneously. The author's previous research has shown that although companies are aware of the big data usefulness, they do not implement BDA or TBDA because they lack professional knowledge on how to conduct it aptly [13]. They are in strong need of a clear framework showing how to put temporal big data analytics into practice and thus adapt the business model to recent data sources and new market challenges. There are already numerous methodological and conceptual works on implementing BDA in companies [11; 14; 15; 16]. However, none of them refers to its temporal dimension. In addition, analysis of literature in the field of big data analytics shows that it is difficult to find approaches focused on BDA's dynamic/temporal dimension. Admittedly, [PERSON] et al.