## 7 Conclusions  
Even though the best practices were meant to be applied for the production industry projects, we were also thinking about the possible transmission of it to other fields from the very beginning.  
After stating the specifics of the original field, it is clear that there are parts of the proposal which cannot be fully used for other fields, but there are definitely some that could be used, either fully or with just minor adjustments. By the other fields are meant any other areas prone to data analysis, such as biology, medicine, social sciences, marketing, etc.  
The parts of the proposed best practices which would not need major changes are 'Business Understanding', 'Data Preparation', 'Modelling', and 'Evaluation'.  
'Business Understanding', despite its name, can be understood as Project Understanding or even Defining Goals. Similarly, if we substitute'management' with terms such as 'institution', 'university', or even 'client', the proposal is still viable. In cases where the team decides about the project independently, the sub-steps connected to the management may be omitted.  
'Data Preparation' deals with preprocessing the raw data into the desired format; the origins and meaning of the data are not important in this context. The process can be applied basically to any other data.  
'Modelling', even though it can mean very different things for different fields, is generalized to the extent where only the path is defined (e.g., the decision between alreadyexisting DM methods and designing own solution), but the proposal is general enough to be usable for projects with completely different nature.  
'Evaluation' validates the model(s) designed in the previous phase and summarizes other discoveries made during the analysis. Again, the proposal describes mostly the sequence of steps, and is therefore applicable in other areas as well.  
Then there are two phases which cannot be so easily applied out of the production process: 'Data Understanding' and 'Deployment'.  
'Data Understanding' in industry production works with records of a process designed by human and performed by machines; the conditions are controlled, tasks predefined and purpose clear, the process is divided into clearly distinguishable cycles and sequences. Our proposal of getting to know previously unknown process builds on these features. Choosing the most suitable representation in, for example, behavioral biology, can be far more complex and therefore needs a very different treatment, according to specifics of the field. Another issue is data acquisition: in industry production, we can often work with data recorded by a company and stored in the company's data warehouses. However, the situation can be very different for other fields; for example, sociological research may demand a proposal not only for the data storage but also their gathering via manually filled answer sheets.  
'Deployment' can mean fundamentally different things for other fields. While in production industry, it usually means deploying the solution (e.g., setting up predictive maintenance), followed by a delivery to the company's management; in more scientific fields, the main goal may be publishing a paper/book; in ecology, the focus can lie on acquiring reasoning strong enough to persuade government to strengthen nature preservation, etc. Therefore, the phase should be reshaped for any new field and type of project.  
Conceptualization, J.A. and P.T.; methodology, J.A.; software, J.A.; validation, J.A. and S.R.; formal analysis, J.A.; investigation, J.A.; resources, S.R.; data curation, J.A.; writing--original draft preparation, J.A.; writing--review and editing, J.A. and P.T.; visualization, J.A.; supervision, P.T.; project administration, S.R.; funding acquisition, P.T. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
The authors declare no conflict of interest.