||Data obtained from electronic health records (EHR) has great potential towards solving clinical research questions. However, the ability to reproduce a successful request for data from one scenario to another can vary significantly. Successful delivery of useful data requires effective communication between the clinical research team, data query experts, and data analysts. It is important that the high level vision of the researcher is translatable to each member of the project team in such a way that the verbal conversations, electronic communications, and other interactions, can be structured or distilled into discretely understood variables. Over the past 3 years, the Biomedical Informatics Core, Center for Clinical and Translational Science, University of Utah has developed a Research Data Service (RDS) to deliver data to researchers covering a broad spectrum of clinical topics. In order to make RDS work in a reproducible manner we conceptualized relevant processes and designed workflows that streamline the data requesting, researcher engagement, query mediation, abstraction, data extraction, delivery and support with analytics. In addition, we developed methods for reusable data extraction, delivery (such as iterative sample data), storage and data quality analysis. This kind of approach provides a consistent workflow for tracing back provenance through the life-cycle of a data request and reproducing steps within the a given request or a new request. Another important benefit to this approach is that researcher expectations can be managed early on in the process leading to a productive allocation of resources. For example, a researcher's ideal scenario may not be possible, however, a scenario that is still of clinical significance, and a useful step in the right direction, may still be worth pursuing. Finally, we will discuss the ongoing challenges working with teams of various technical and clinical backgrounds and how we use each data request to improve the RDS process.