Recreational freshwater fisheries face a myriad of challenges such as climate change, land use change, and aquatic invasive species. These large-scale challenges cross jurisdictions and span environmental gradients, requiring broad-scale data to understand the drivers and repercussions of these changes. Fisheries data exists for many freshwater lakes thanks to standardized agencies’ monitoring programs, but the analysis of these data is commonly limited to monitoring trends in specific lakes or regions. Further, fisheries data documenting fish abundance, size, and age are difficult to combine across geographic scales due to variability in data collection (e.g., preferred sampling gears, sampling time, sampling frequency, sampling effort). Therefore, multiple considerations are required to combine data across geographies and agencies to allow for broad-scale analyses. For example, instances of zero-catch frequently need to be added, as absences of species are often not recorded. Standard surveys that evaluate multiple species need to be treated differently than targeted surveys that may only sample certain species or age classes. To allow for broad-scale analyses of inland fisheries data, we developed a data management, filtering, and combining workflow that processes raw data files provided by fisheries agencies monitoring data to make data comparable across systems. Our approach allows users to apply their own filters and standards, and create custom aggregations that can meet multiple analytical needs. Because of the workflow structure, new data inputs can be added to the workflow with ease, allowing for database updates. The tools presented here standardize, combine, and manage broad-scale recreational fisheries monitoring data, allowing researchers to better understand landscape-level drivers and responses of fish to novel stressors, and increase the scale of adaptive management from single lakes to entire landscapes.