More than three decades after its introduction, Role-Based Access Control (RBAC) continues to be one of the most widely used access control models in organizations. This popularity stems from its simplicity, the reduced risk of errors, and its clear alignment with business processes. However, the primarily manual nature of data management in RBAC systems, coupled with a lack of oversight, can lead to various inefficiencies over time. These may include roles that are not assigned to any users or roles that have identical sets of permissions. Such issues can slow down systems that rely on these data and, more critically, complicate auditing processes, increasing the risk of security gaps and compliance violations.In this paper, we present a taxonomy of inefficiencies that can arise in RBAC data over time and propose a framework for detecting these inefficiencies. We specifically focus on the most resource-intensive inefficiencies, namely roles that share the same or similar users or permissions. To address these issues, we propose three detection methods, including a custom algorithm we developed. We evaluate these methods using synthetic datasets, demonstrating that our algorithm significantly outperforms baseline approaches. Its efficiency allows us to identify these inefficiencies even on a standard laptop used by large organizations. Furthermore, we applied our framework to real RBAC data from a large organization with over 60,000 employees and uncovered a substantial number of inefficiencies, highlighting its practical value in real-world scenarios.