Exploring the Role of Digital Twins in Data-Driven Decision-Making : A Qualitative Study

Digital twins have evolved as a tool for optimizing complex systems through simulation and realtime analysis. The telecommunications industry is characterized by high technical complexity, where decision making often relies on the information stored in large volumes of data. As data driven decision making becomes increasingly common, questions arise regarding how digital twins actually support these processes in practice, particularly in relation to process optimization, which involves streamlining workflows and resource use. At the same time, scientific literature shows that technical tools do not automatically generate value, without requiring context, understanding of the system and human interpretation. An absence of knowledge remains regarding how digital tools such as digital twins affect workflows, decision processes, and process optimization in operational settings. Therefore the aim of this study was to investigate how digital twins are used to support data driven decision making for process optimization. The study was based on qualitative semi-structured interviews with experts in the field. The results were analyzed using the DIKW framework and Decision Support Systems (DSS). The findings showed that digital twins enabled more efficient decision-making through increased data availability, simulation, and visualization. However, human expertise remains essential at both the beginning and end of the process to interpret and validate decisions. The study highlights the interplay between technology, data, and human expertise where the demands for optimization and reliability are high.