The increasing digitalisation of machinery enhances production facilities by laying the foundations for advanced data analysis. To ensure effectiveness, it is essential that the collected data is of the highest quality for optimal use in various applications. The quality of data is subject to a variety of influences. This includes the design and operation of data acquisition for production systems. The implementation of Failure Mode and Effect Analysis (FMEA) and/or Failure Mode and Symptom Analysis (FMSA) has been proven to be challenging due to the time-consuming and labour-intensive nature of the process. In addition, the results can vary depending on the knowledge and expertise of the team performing the analysis. To address these challenges, a methodology based on the FMEA/FMSA framework is developed using historical and operational data. Consequently, the assessments made during FMEA/FMSA became objective, eliminating reliance on the expertise and background of the team conducting the evaluation. To illustrate the feasibility of our approach, we utilise the case study of an intelligent machine test bed. From Art to Science: Our contribution advocates for a paradigm shift in FMEA/FMSA frameworks, moving from more or less subjectively designed individualistic concepts towards objectively established, harmonised solutions.