One of the most important master data entities is the list on which all public transport stations (bus, train, and tram) are stored. The list data are obtained from an external source. To perform the update of the list and thereby harmonizing the data with the external source a lot of manual effort is required, which potentially can be automated. Therefore, a solution artifact is developed. Further, the integrity of the existing master data, the most pressing master data issues in the company, and how to solve them, is researched and evaluated in accordance with contemporary best-practices.

With the help of secondary research, the best-practices of master data management are collected. By the development of the solution artifact, that is developed in Python, and the master data integrity checks, the master data management of ETC should approximate to the identified best-practices. Conclusively, at the end of the thesis the as-is state before is compared to the as-is state after regarding the best-practices. For the evaluation of the results, the qualitative research method is applied, which mainly consists of observations, interviews, and the analyzation of documents and reports.

The solution artifact enables to continuously update the station list by tremendously reducing the manual effort for the process. Thereby, the validation of the consistency and completeness of the station list, is facilitated and can be performed regularly as the process can be performed within a matter of a few minutes. As a result, the quality of the station data will substantially increase, likely preventing errors and requests, which otherwise would cause unnecessary effort. Furthermore, the analyzations provide valuable information for the company as for instance, where is still potential for optimizations, what are the most pressing master data issues, and for the future decision-making and priority setting regarding master data in the company. Additionally, the thesis provides the basis for a future sophisticated master data documentation. Ideally, the documentation can serve as a repository for master data knowledge – enabling employees in the company to perform master data related tasks by themselves and reducing the burden of requests to the person, who is mainly responsible for the master data.