# Conceptual Framework for Implementing Temporal Big Data Analytics in Companies
## 2 Background for Big Data Analytics in Companies--Literature Review
These are matching in the analytical processes all the organizational resources (human, IT, business ones); implementing analytical results as business activities; adjusting IT platforms to the needs of the BDA; generating business values by means of the BDA; managerial support and understanding the implementation of BDA [5,18,42,43]. However, among these challenges, the temporal BDA is not considered. [PERSON] [13] pointed out the importance of the time dimension in decision-making and in big data analytics. Some authors have observed temporal challenges for BDA without naming them that way. Among the challenges they cited were [44, 45, 46]:  
* Monitoring of data streams;
* Business analytics on these streams (real time);
* An ever-increasing volume of data flows;
* Significance of IoT and social network analysis throughout time. It seems therefore obvious how important it is to consider the time dimension in the BDA, which leads to temporal big data analytics, as defined in Section 1.1. Implementing analytics in this way requires a connection by the time dimension between information technology, analytical processes, the business layer, and human factors. Since management, technology, and the human component [47] all function and interact across time to generate corporate value, taking all three into account is essential for a successful temporal BDA process (output). Research on the BDA process in companies is very diverse in terms of genesis--the proposed solutions originate from the innovation process [12], the analytical needs of managerial staff [46], machine learning (ML) procedures [48,49], cloud computing [43,50,51], or transformation models [35]. However, none of them refer to time (temporal dimension) as the primary determinant of BDA. As for research on big data analytics, among numerous approaches proposed--cf., e.g., [46,52, 53, 54, 55]--only in the publications by [PERSON] et al. [55] and by Syncsort [46] the dynamic dimension and the real-time dimension of big data analytics are indicated as significant. Consequently, the overwhelming majority of the proposed solutions do not consider the temporal dimension. The only framework addressing the question of temporal big data analytics has been proposed by [PERSON] et al. [17], however, it is focused on computational issues, not on the implementation ones. In this article, it is proposed to incorporate the lean, agile, and leagile notions and concepts known from management and computer sciences into the conceptual framework for the TBDA. This is proposed as a means to address the temporal dimension of BDA. So far, these concepts have been studied and used in such areas as manufacturing [56], project and software project management [57, 58, 59], reverse logistics [60], digital entrepreneur [61], healthcare management [62], supply chain management [63, 64, 65], and last but not least, software development [66, 67, 68]. The lean, agile, and leagile concepts, and their usefulness for the implementation of TBDA, are discussed in Section 4.