# Conceptual Framework for Implementing Temporal Big Data Analytics in Companies
## 5 Temporal Big Data Analytics Implementation Framework
### Phase III: Emergence of the TBDA Ecosystem  
As said earlier, the first two phases lead to the emergence of the TBDA ecosystem (Phase III). The ecosystem should be validated and verified against the business challenges, processes, and requirements identified by the company in the previous stages. As described in Section 4, the agile approach to TBDA implementation should be combined with lean principles to form a leagile TBDA implementation framework. As [PERSON] et al. [66] pointed out, there are six categories of lean applications in agile software development. Hence, it might be assumed, given the context of the TBDA implementation framework, that lean principles can be used to:  
* Guide the implementation of the agile (DSDM and Scrum) methodology/methodologies;
* Ensure a continuous flow of subsequent elements of the TBDA implementation framework;
* Adapt the TBDA to business and market changes;
* Lead activities at the team level. Shortly, the leagile approach enables lean and agile goals in the TBDA implementation process and identifies the best scenarios for it in response to the analytical needs of the company [71]. This helps to achieve flexibility in the developed framework (agile method) and extend the agility to make the framework more efficient (lean method) [73]. Some lean practices seem to be particularly useful when applied to the agile TBDA implementation projects:  
* Create incentives/rewards for development teams;
* Focus on people rather than machines;
* Continuous improvement (Kaizen);
* Link VoC (Voice of Customer) to requirements (Kano)--in the context of TBDA, \"customers\" means \"the managers and analysts of the company\";
* Measure and manage implementation projects;
* Pragmatic governance--enabling first, then directing and managing;
* Value stream mapping--analyzing and designing the workflow required to deliver the TBDA, to bring projects to clients (understood again as managers and data scientists). Figure 5 summarizes the leagility of the proposed TBDA implementation framework. As shown in Figure 5, the five principles of lean thinking are all used to improve agile processes for TBDA implementation and interaction with business units that require  
Figure 5: The leagility of the TBDA implementation framework. temporal big data analysis. The lean principles are described in the context of TBDA implementation as follows:  
* Value: the TBDA should create value for the company, so the ultimate goal of the TBDA implementation is to produce value for the company;
* Value Streams: every analytics/data science activity implemented should provide value to the company; both value and value stream generation can benefit from Kano analysis used in lean management;
* Flow: the TBDA implementation process should be executed continuously without interruptions;
* Pull: implement the TBDA ecosystem elements only when really needed; both flow and pull principles can benefit from Jidoka and Kanban practices used in lean management;
* Perfection: continuously improve the TBDA implementation and analysis process. Lean practices that can be used here: Hansei and Kaizen. Lean, agile, and leagile methodologies have many benefits. In the most pervasive manufacturing setting, these benefits are listed in [56]. In the context of ad hoc implementation of big data analytics, the use of lean methods as a common process management framework is expected to bring the following benefits:
* Reduced development time--the TDBA ecosystem implementation is smooth and non-disruptive;
* A better understanding of the analytical process in a company--as the requirements of the TBDA ecosystem are based on the company's management and analytical needs;
* Saved money--because of the \"zero waste\" policy and the pull approach of ecosystem elements;
* Improving the quality of IT solutions (part of the TBDA infrastructure);
* Improved customer satisfaction (customers are understood as managers and data scientists) and decision-making efficiency.