Technological advances have led to an increase in data available to commanders and their staffs. Sorting through collected data and identifying what is present that is relevant and what is relevant-but-not-present is necessary to identify and eliminate information gaps that hinder a commander's decision-making ability. This thesis developed and assessed a model illustrating the creation and propagation of information gaps in a command structure in order to provide a tool to identify and mitigate organizational blind spots. Literature describing approaches to naturalistic decision-making; individual processes for collecting, filtering, and interpreting specific data; and the effect of staff member roles on their interaction with data formed the basis of the model. Internal analysis revealed potential causes of information gaps. The model's utility in identifying information gaps was evaluated using dissimilar historical cases with varying command attributes and environmental parameters including technological context, situational tempo, and level of war. The thesis concludes by describing the model's limitations and suggesting techniques to further validate the model using live exercises and a way to apply the model offensively in deception operations.This compilation includes a reproduction of the 2019 Worldwide Threat Assessment of the U.S. Intelligence Community.The command DMSC's utility in identifying information gaps should be thoroughly tested using both a rigid, hierarchical command structure and a more fluid, ill-defined command structure. The command DMSC must be able to handle the unique attributes of both types of commands. Additionally, the command DMSC should be functional across the spectrums of several environmental attributes. Application of the model to past and modern examples will demonstrate its effectiveness in a variety of technological contexts, showing it can still apply as technology evolves. Use of strategic and tactical cases will assess t