Rock engineering activities, such as underground constructions, mining works, and excavation of rock slopes, are responsible of a wide range of changes in the surrounding environment. These effects can significantly alter the rock mass behavior over time, triggering different processes that negatively affect its stability. For this reason, monitoring activities aimed at improving the knowledge of the rock mass response are extremely relevant to ensure safety during construction and operation, check the validity of design hypotheses, and assess the necessity of risk mitigation measures. Overall, the design of effective monitoring systems requires a multidisciplinary approach that considers a range of factors, including geology, engineering, and data science, due to the inherent complexity of the subject. All these expertise play a key role in the development of a procedure able to provide critical information for safe and effective management of rock mass infrastructure. The first challenge involves the selection of the appropriate parameters to monitor. This requires a deep understanding of the observed element and the potential hazards that may arise, as well as the knowledge of the available technology for measuring and recording data. Due to the wide variety of physical quantities to sample, more advanced systems nowadays are designed to integrate multi-parameter devices, able to gather various information at the same time. The second challenge relates to the design of a system that can effectively collect and process the data. This requires careful consideration of factors such as the type and location of sensors, the frequency of data collection, and the method of data transmission and storage. The last key challenge in designing monitoring systems in rock masses involves the selection of the best approach for data integration, elaboration and management. This aspect is especially important when automatic instrumentation is implemented in the monitoring activity. These devices are, in fact, able to reach extremely high sampling frequencies, and the large volumes of data generated by such systems can be difficult to process and interpret. Additionally, it is important to ensure that the data is properly stored and easily available to facilitate its use in decision-making processes. This requirement can be fulfilled with the implementation of appropriate visualization platforms, designed to manage all collected information and to provide a reliable tool for their representation.