# Data-Based Stakeholder Identification in Technical Change Management
## 7 Discussion
### Implications for Industrial Practice  
The introduction and application of the methodology are associated with various expenses that must be taken into account. On the one hand, the time required for the training cycles of the models must be taken into account. One training cycle took about 2 h. In the practical use case, 35 models with different configurations of additional data attributes were trained, resulting in a total amount of time required for the training cyclesof about 70 h. On the other hand, the preparation and implementation, as well as the evaluation of the results of the workshops for the collection of expert knowledge, should be considered. The time required for each workshop was one hour of preparation, one hour of execution, and two hours of post-processing of the findings. In total, this resulted in a time expenditure of 70 h across all workshops carried out together. It should be noted that this effort to create and improve the model is only to be undertaken initially and that the company benefits from a long-term perspective by reducing the manual analysis of CRs. However, these efforts can only be justified if an improvement of the model quality can be achieved and therefore have an added value for operational TCM.  
In the considered use case, the manual SI by the change requestor in the period from January 2018 to April 2021 reached a recall of 81.32%, with 3303 FN in total or in terms of workdays 23 FN per week on average. Figure 5 shows that higher values for the recall were achieved through the training cycles with different additional data attributes and the selection of the most suitable model. In addition, the integration of the word combinations identified in the workshops with business unit employees led to an increase in recall. Overall, the best model would result in 2112 FN, a total of 1191 fewer than compared to the change requestor equal to a reduction of 8 FN cases per week on average. Compared to the model with just textual data as input, the HAN with additional data attributes can avoid 11 FN per week compared to the HAN without additional data attributes. The additional use of the expert knowledge in the form of word combinations can avoid 3 FN per week. Overall, the application of the method can thus avoid an average of 14 FN per week. Overall, the use of machine data analysis with the aid of HAN and the methodology can reduce the risk of FN and thus of unexpected change impacts.  
The overall quality of the results is higher than the benchmark set by manual SI that the task of SI can be completely taken over by the algorithm, which either relieves employees or enables a reduction in the number of employees required for change management. The approach developed is thus distinguished from manual approaches to SI by its complete automation. After the initial efforts for data extraction, data preparation as well as model building and improvement, this allows the identification of affected stakeholders to be carried out almost effortlessly, which can lead to significant economic savings in the long term. Due to the in-depth analysis of the textual information by the HAN, which allows the consideration of all details and nuances of the changes to be analyzed, it can be assumed that the developed approach allows more precise statements about affected stakeholders than all comparable data-based approaches. However, a quantitative comparison cannot be made at this point due to the uniqueness of the use cases in this contribution as well as in the previous contributions. However, a disadvantage of the developed approach is the need for a comprehensive database and thus the need for comparable historical changes. As a result, all data-based approaches are likely to produce imprecise results when analyzing particularly novel changes that have not yet occurred in this form. As a result, it must be concluded that a suitable data-based or non-data-based approach should be selected to support SI in companies, depending on the type of change.