Databases have been an integral component of Data Fusion from the outset when the JDL model was introduced. As advances in High-Level fusion using Multi-Int data have been made, the original concept of databases as a static repository of Level 0/1 content has evolved to support heterogeneous data, and as a necessary enabler of High-Level fusion processes. Relatively recent database technologies now support specialized storage for complex content such as multi-media, geospatial, and semantic data types. Additionally, database functionality has been extended from what was once almost exclusively storage and retrieval, to include integrated forensic and predictive algorithms, as well as decision support frameworks such as the data cube. These data mining capabilities provide a rich tool-set from which to tailor a fusion application. However, due to their inherent trade-off space, they present a significant design and integration challenge when implementing an enterprise architecture, which has to provide a comprehensive and cohesive framework across the entire fusion workflow, and which has to meet the needs of various Communities-of-Interest. This paper expounds on the role of data architecture as a key discipline to help analyze and synthesize an enterprise fusion system-of-systems, and presents selected principles to maximize heterogeneous data exploitation.