A significant challenge in civil engineering is the control of structural safety and the management of aging infrastructure. Structural Health Monitoring (SHM) plays a central role in this context. SHM methods allow for handling monitoring data and updating the structural reliability of the infrastructural stock. Ideally, monitoring systems should provide the best possible information; however, given the inevitable presence of uncertainties and limited manager resources, evaluating the quality of SHM information becomes a critical issue. Several metrics are available in the scientific literature to assess SHM information quality, most of which are based on the accuracy of the information or its impact on decision-making processes. In this work, the authors define a general framework based on Bayesian networks and propose two new information quality metrics based on structural reliability updating: Monitoring System Resolution and Monitoring System Importance Measure. Using a toy example, the authors show the key role of interpretation models and uncertainty quantification in the design of SHM systems.