# Conceptual Framework for Implementing Temporal Big Data Analytics in Companies
## 2 Background for Big Data Analytics in Companies--Literature Review
It is pointed out that the business value of BDA results from its managerial, economic, and strategic aspects. Additionally, [PERSON], [PERSON], and [PERSON] [12] show the direct links between the BDA and business value generation. They point out the role of analytical competencies in the BDA, and the need of organizing the BDA process steps. Hence, the necessity to conjoin managerial, business, human, and IT aspects in the BDA implementation framework is obvious, and it has been implemented in the TBDA implementation framework described in this article. As for the BDA implementation issues, [PERSON] et al. [12] suggest adapting the linear innovation process to the requirements of the BDA, whereas [PERSON] et al. [36] developed the conceptual model based on the data-to-knowledge conversion process, and on the idea of the dashboard to convert big data into actionable knowledge. However, none of these frameworks refer to the temporal dimension of big data analytics. The problem of big data evolution is addressed in [37], however, this study deals with big data ontology only, not with its implementation framework. On the other hand, [PERSON] et al. [38] developed a RawVis framework for in-situ visualization of big raw data. This is possible by building the main-memory index on-the-fly and adapting the index structure using user-driven techniques. This is some response to changes in big data over time (Velocity), but the time dimension is not explicitly stated. The summary of the discussed approaches, and their comparison with the new conceptual framework proposed in this study, is given in Table 1.  
As can be seen in Table 1, none of the previous approaches to the implementation of BDA in organizations/companies is at the same time general, using lean/agile/leagile principles and considering the temporal dimension of big data analysis. Only the conceptual framework presented in this article has all three features. Oftentimes, it is proposed to use the existing big data maturity models as a starting point of the BDA process in companies. Some well-known models have been proposed by [27; 28]. The TDWI model [27] consists of five levels and shows the steps to be taken by a company while undertaking big data initiatives. The Big Data Business Model Maturity Index by [PERSON] [28] aims at assessing a company's business model in light of big data usage. Other big data maturity models are also proposed in the academic space [39; 40]. In none of these models is the temporal dimension of big data analytics explicit. To address \begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Solution**} & **General** & **Lean/Agile/Leagile** & **Temporality** \\  & **Framework?** & **Approach Included?** & **Included?** \\ \hline [PERSON] [28] & Yes & No & No \\ [PERSON] [29] & Yes & No & No \\ [PERSON] and [PERSON] [22] & No & No & No \\ [PERSON] and [PERSON] [30] & No & No & No \\ [PERSON] [31] & No & No & No \\ [PERSON] [32] & No & No & No \\ [PERSON] et al. [33] & No & No & Yes \\ [PERSON] et al. [34] & No & No & Yes \\ [PERSON] et al. [35] & Yes & No & No \\ [PERSON], [PERSON] and [PERSON] [12] & Yes & No & No \\ [PERSON] et al. [36] & Yes & No & No \\ [PERSON] et al. [38] & Yes & No & Yes \\ New Conceptual Framework & Yes & Yes & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of BDA implementation approaches. this gap, the author of this article has developed the Temporal Big Data Maturity Model (TBDMM). Its main assumption is to assess the company's maturity for big data analytics on the basis of the approach to the time dimension in three areas: (1) data/knowledge; (2) IT solutions; (3) IT functionalities. Thus, time becomes a major dimension in assessing the maturity of a company in the BDA area. In each of the above-mentioned areas, a company may be at one of the five maturity levels [8]. The TBDMM is accompanied by a self-assessment form through which the company can evaluate its TBDA maturity [41]. Big data literature also pays attention to the challenges of BDA efficiency.