The existing parallel I/O stack is complex and difficult to tune performance due to the interplay of multiple factors that impact the performance of data movement between storage and compute systems. When performance is slower than expected, end-users, developers, and system administrators rely on I/O profiling and tracing information to pinpoint the root causes of inefficiencies. However, there is a gap between the currently available metrics, the issues they represent, and the application of solutions and optimizations that would mitigate slowdowns. An I/O specialist often checks for common problems before diving into the specifics of each application and workload. Streamlining such analysis, investigation, and recommendations could close this gap without requiring a specialist to intervene in each case. In this paper, we propose an interactive, user-oriented visualization and analysis framework to pinpoint various root causes of I/O performance problems and provide a set of actionable recommendations to improve performance based on the observed characteristics of an application. We evaluate its applicability and correctness in four use cases from distinct science domains and demonstrate its value to end-users, developers, and system administrators when seeking to improve an application’s I/O performance.