## 6 Discussion
### Most Common Problems  
Even though most errors which served as a base for our best practices were phase-specific, there were a couple of them which affected various phases and arose during several projects. Once more, we classified the most prominent and the most often occurring errors into groups based on data quality dimensions. Accuracy:  
Results do not confirm the hypothesis;  
The business/data-mining goal not fulfilled. Completeness:  
Vaguely defined/incomplete business/data-mining goal;  
Missing documentation;  
Permitted/limited access to a source;  
Lack of suitable data sources;  
Not enough data for certain visualizations. Consistency:  
Process changing over time;  
Missing common unambiguous identifier;  
Structure changing over time;  
Naming, spelling and language inconsistencies;  
Validation on a different period/part unsuccessful. Timeliness:  
All attributes cannot be monitored at once. Validity:  
Figure 6: Top diagram proposal for integration of phases. * Sensitive data;
* Structure not suitable for software tools;
* Desired analysis not possible;
* Analysis took a different course. Uniqueness:
* Too many combinations leading to a big number of visualizations. Many of the problems come from inadequately defined requirements and a lack of documentation. The former problem can be usually helped by enough communication (with company management and within team). Specifically, it is important to know beforehand which sources will be available and what information can be extracted from them. The latter problem is something the data scientist cannot influence; however, early knowledge about the state of the documentation can be very helpful in estimating the time needed for the project. Moreover, it can lead to incorporating the building of documentation into the project's goals. In any case, knowing as much as possible about the project before starting it is a key to finishing it successfully and on time. We can find parallels to these problems in many of the papers we mentioned in the literature overview. [PERSON] et al. (2018) remark that introducing industrial Big Data is a big challenge for the company--that is the same struggle we observe many times, e.g., in incompatibility of data sources. Inconsistencies of data are also addressed by [PERSON] et al. (2021). [PERSON] et al. (2019) determined that one of the problems with choosing the right data to be collected is the lack of suitable data sources; we also identified this problem. [PERSON] et al. (2021) focus on the importance of generating Smart Data by enriching Big Data--and we discuss the general lack of documentation. Solid documentation is the base for such enrichment. [PERSON] et al. (2021) came to a conclusion that more complex methods may prove less useful, similarly to one of the possible problems we identified for the 'Modelling' phase. [PERSON] et al. (2021) describe problems with working close to real time, what is also reflected in our experience with not being able to access certain data sources in real time or monitor all attributes at once. Human factor is another aspect which we addressed often in our projects (in issues such as missing documentation, vaguely defined business goal, or denied access to a database). Importance of involving employees into Industry 4.0 was accentuated by [PERSON] et al. (2021) and [PERSON] et al. (2021). [PERSON] et al. (2019) also focuses on extensive internal knowledge in a company. As we stated before, summarizing best practices or extending existing methodologies is rather unusual topic among the studies on key terms of this paper. However, we were still able to find similarities with our work. [PERSON] et al. (2021) also build their best practices on CRISP-DM--they focus on a single phase, 'Deployment'. [PERSON] et al. (2019) do something similar for 'Data Acquisition' subphase. That proves that the problems we were discussing are not limited to a small group of projects. However, even though they are often present, they are but scarcely summarized and discussed in other studies.