# Conceptual Framework for Implementing Temporal Big Data Analytics in Companies
## Appendix A
I would say otherwise--and it will be a kind of generalization--the use of a tool/approach should always refer to the goal or the project that we are implementing. You have adopted certain methods and they will certainly work, there is no doubt; question if in any situation. Is such a universal approach as the one you proposed can be used for any purpose?\"  
\"The first topic concerns the methodologies that are used: lean, agile. Both are quite accurately described; both are used by business. Their combination is very interesting. I have not seen such a leagile connection in business [in IT implementation].\"  
\"I really like the idea very much; I was most delighted with this combination of agility and leanness. I know that the leagile approach is not your own, but its use in the framework is really a plus.\"  
\"When it comes to lean, agile and leagile methodologies, I treat them as tools to improve the implementation of what you are talking about i.e., temporality.\"  
All the statements quoted above show a very positive reception of the lean, agile, and--especially--leagile concepts in the proposed framework. One participant even emphasized the relationship of these concepts with temporality. Therefore, it can be assumed that basing the TBDA implementation framework on a combination of lean and agile is a good solution. The correctness and adequacy of the TBDA implementation efficiency measures proposed in the framework  
Question: Are the KPIs proposed in phase IV correct and adequate for the TBDA implementation task? In the proposed conceptual framework, several measures of implementation success are proposed. The success/failure is assessed from four perspectives: financial, stakeholders/customers, business processes, and innovation perspectives. For each of the perspectives, a sample set of KPIs has been proposed. The respondents were asked to give their opinion on this point. \"As for the KPIs that were mentioned, companies love KPIs. In practice, it looks like they are created by data scientists or by business, these are parameters that describe the state of the product,or additional features that describe the user. The only problem is that companies love to create these KPIs and now you have to choose the ones that affect your profits.\"_  
_\"Working with many companies as a consultant, I conclude that it is very difficult to start an analytics implementation project by collecting requirements from users, because they are not able to clearly define them at the beginning. And very often, before this first phase, we do a zero phase, where there is some kind of inspiration for users, showing them what can be achieved, how others do it, and only after this phase we are able to collect such more real requirements. Even sometimes, in this first phase, we are not able to define the KPI for the implementation, only after implementing such a proof-of-concept business is able to better understand what it will have and is only able to formulate its more precise expectations.\"_  
_\"As for the measures of effectiveness: we once created a model with a prediction accuracy of 40% and we were convinced that the project should be beaten, while the client was satisfied because internally, they had a model with a forecast accuracy of 20%. Therefore, the parameters that we use in data science should also be mapped to business KPIs.\"_  
As seen from the above utterances, the proposed set of KPIs is generally assessed as correct, because it addresses several business perspectives. However, it should be noted that while implementing the proposed framework in companies, there will be the need to consult KPIs and measures of effectiveness with a specific business. Probably doing a proof-of-concept would be helpful to managers to draw case-specific KPIs. _Outcome 5: The practical value of the proposed framework_  
Question: _Can the proposed TBDA implementation framework and TBDA analytics ecosystem be applied in practice?