Environmental monitoring programs can generate actionable data that informs management and policy, but their long-term success requires a high level of multi-institutional and interdisciplinary collaboration. Data co-produced under a shared organizational umbrella can strain quality management throughout the data lifecycle. From a technical perspective, challenges include varying technological aptitudes among personnel, decentralized or nonstandard data collection, lack of version control, and/or incomplete metadata. More broadly, the design of shared data infrastructures must consider the timeline of decision-making activities and primary audiences they serve. Careful consideration of potential pain points and plans for effective solutions are critical to ensure robust data-to-action systems, and those strategies are often best forged through thoughtful engagement with a variety of stakeholder perspectives. The H2Ohio Wetland Monitoring Program (WMP) was established in 2020 to evaluate the nutrient removal effectiveness of varied wetland restoration projects implemented by the Ohio Department of Natural Resources (ODNR) as part of a state-wide water quality improvement program. The WMP is implemented by principal investigators and technicians from multiple Ohio universities who frequently interact with agency personnel, land managers, and wetland practitioners in a variety of capacities. The information created by the WMP informs future wetland restoration approaches and management activities by providing a community of practice with transparent and trusted science. To do so, the data stewardship and governance policies for the WMP have focused on strategies and tools that ensure data preservation, guarantee scientific rigor, and optimize efficiencies from data collection to dissemination by adopting FAIR principles. Our data infrastructure has been built in a stepwise iterative process, adapting and improving systems using on-the-ground feedback from data collectors. We have used custom digital surveys to centralize data entry and continuous integration systems to automate and document critical data lifecycle steps. We built standardized quality checks to produce verified and integrated datasets. These approaches ensure robust data toward actionable results and repeatable analyses, while also promoting the usability of these datasets in other applications.