Obtaining and preparing the right data for M&S-based activities is a huge consumer of resources, regardless of the activity supported by M&S (training, testing, etc.). The Rapid Data Generation (RDG) project, sponsored by the US Department of Defense (DoD) M&S Coordination Office, on behalf of the Under Secretary of Defense for Acquisition, Technology and Logistics, was therefore established to improve the visibility and accessibility of data, as well as to reduce the time and effort necessary to integrate the necessary data for an M&S event. This paper presents the RDG Common Data Production Environment (CDPE) system architecture. This architecture defines a service-oriented design that specifies how data provider and data consumer systems integrate to enable net-centric discovery, assessment, and retrieval of M&S-relevant data. The architecture has been implemented in “order of battleâ€� data capabilities in addition to the “environmental representationâ€� data capabilities. These two capability releases focus on the sharing of military force structure datasets, such as orders of battle, scenarios, and entity-type enumeration data, as well as geospatial imagery, elevation, feature, and weather effects datasets and 3D models. The CDPE system architecture design makes use of DoD enterprise standards with industry best practices and design patterns to achieve a solution that is agnostic to the types of data exchanged. Through the use of reference architectures with implementation-independent and -specific designs, the design is resilient and adaptive to evolving technologies. The architecture also incorporates design alternatives that mitigate the variety in data producer and consumer system architectures. As a result, the architecture can be applied by others to develop capabilities for data discovery and sharing across diverse, loosely connected communities. The CDPE system architecture enables the rapid use and improved reuse of the data necessary for simulation-enabled training and mission readiness exercises for multiple tiers of training, all while incorporating and enabling data sharing with peer communities.