# Conceptual Framework for Implementing Temporal Big Data Analytics in Companies
## Appendix A  
The Appendix contains detailed information on outcomes reached during the focus group research aimed at verifying the proposed conceptual framework for the temporal big data analytics implementation in companies. _Outcome 1: Validity of bringing temporality to the foreground_  
Question: _The main idea of the framework is to focus it on temporal big data analytics. Is it justified to set time as the primary dimension of big data analysis? What are your thoughts on big data and time?_  
This question was about the general opinion on the presented conceptual framework and on its most distinctive feature--temporality. The participants admitted that the time dimension is very important in business analytics, however, they have different experiences with this issue:  
_\"When it comes to so-called timestamps, data is most often stored in data warehouses. In the tables, we have information about when certain interactions were performed, we are able to extract time stamps from these interactions, and a group of analysts is able to create a solution for making a business decision.\"_\"There are different approaches to data analysis, we are currently working a lot on timestamp data. However, we do not treat time as a separate dimension.\"_  
_\"When it comes to a pragmatic, typically engineering approach, time series forecasting immediately bows down when we deal with time. There are several methods, and we must have the data ordered in time. The second topic you are talking about, which seems interesting, but may be a bit more expensive to implement, but is very interesting from the scientific point of view, is causal relationships. I imagine it in the form of graphs that will visualize the interaction you are talking about [causal relationships] as a directed graph, i.e., observations/behaviors in initial nodes and in intermediate states.\"_  
_\"I had the opportunity to participate in machine learning projects where there was data and based on that data, conclusions were drawn. It always seemed to me that without the time dimension, such research would be meaningless. So, it is very important. The time dimension, in particular the issue of variability in time, the study of what happened, especially if we talk about the sequencing of actions, this is the basis for making some kind of inference at all. So, the time dimension is really very important, very important.\"_  
_\"The second issue, related to temporality, or time--this is a canon for me, the basis of any analysis. The measurable feature of each element of our reality is the element of time. Always designing and implementing IT systems, I paid special attention to time. I believe that ordinary analyzes, such as examining the correlation between attributes, are such a primer, but we have to put some time on that. In order to be competitive, we have to operate on the time scale, in seconds. It is another matter whether it will be a point-based time period or in a more complex way (e.g., a hierarchy of dates). We have the concept of time representation in general (points, intervals, both, linear, other)--we know these things, but we are not yet fully implementing them, so it's good that you bring it up. If we look at business and competitiveness, the key is to anticipate the future, even if only to a minimal extent. Today, in the face of a volatile environment, creating a [business] strategy is incredibly difficult. So, I see the usefulness of time [temporality] in the context of business.\"_  
_\"Now I will throw the stick into the anthill: is there such a thing as non-temporal big data analytics? Because I think that maybe there are just specific business cases that require more or less emphasis on this time dimension--I run projects in the area of customer churn, where this time dimension is needed, all projects where there is forecasting, where the time dimension is necessary.