# Conceptual Framework for Implementing Temporal Big Data Analytics in Companies
## 7 Discussion, Conclusions, and Future Research Directions
### Conclusions  
The research presented in this article answered the main research question (RQ1): how to effectively implement temporal big data analytics in companies along with the necessary organizational changes? It proved that there is a need for a TBDA implementation framework that coherently combines IT, strategic, analytical, and organizational issues. The conducted research also allowed us to answer the additional research questions posed in the introduction:  
* RQ2: The answer is yes; the participants of the focus study emphasized the usefulness of the framework and recognized the fact of its creation as important;
* RQ3: The answer is yes; according to the focus group participants, the framework coherently combines technological, analytical, strategic, and organizational aspects. Hence, it is a holistic approach to TBDA support and has been assessed as needed by companies;* RQ4: the answer is yes; the participants of the focus study highly appreciated the use of the leagile concept in the proposed framework. They saw the advisability of using the leagile approach in relation to the temporal dimension of big data analytics.  
The Temporal Big Data Maturity Model and its related Self-Assessment Form, as well as the TBDA Implementation Framework given in this article, provide a full solution for successful TBDA in companies. This paradigm varies from other big data implementation frameworks in that it highlights the importance of the temporal dimension for effective big data analysis. For project management, the suggested framework employs lean, agile, and leagile approaches. Adopting lean principles leads to:  
* Faster development;
* A better understanding of the company's analytical processes;
* Cost savings;
* Higher quality of generated IT solutions;
* Increased employee happiness;
* Increased decision-making efficiency.  
Adopting agile principles can result in increased employee involvement, the development of a wider range of analytical tools, and the ability to deploy the TBDA ecosystem with greater flexibility. The proposed framework's slick approach could result in:  
* Cross-trained staff;
* Quality assurance;
* Informed decision-making;
* Process integration and performance measurement;
* Market sensitivity and responsiveness;
* Analytical experience and skills of employees;
* Organizational culture focused on the TBDA.  
The research anchored in this article is innovative because it focuses on time and temporal dimensions. There is no complete conceptual framework that parallelly involves time, BDA, business results, change implementation, and technology factors, as the literature review reveals. None of the big data maturity models discussed in Section 1 consider the time factor or data variability, or the time dimension of big data analytics. Existing models, in fact, only consider the 5 Vs of big data: Volume, Veracity, Value, Visualization, and Variety. The sixth and seventh Vs: Velocity and Variability, are only included in the TBDMM model outlined in Sections 1 and 5. The idea of incorporating lean, agile, and leagile concepts into TBDA's conceptual framework can provide a new level of IT support to businesses looking to obtain a competitive advantage through real-time big data insights.