Data collection processes supporting Intelligence, Surveillance, and Reconnaissance (ISR) missions have recently undergone a technological transition accomplished by investment in sensor platforms. Various agencies have made these investments to increase the resolution, duration, and quality of data collection, to provide more relevant and recent data to warfighters. However, while sensor improvements have increased the volume of high-resolution data, they often fail to improve situational awareness and actionable intelligence for the warfighter because it lacks efficient Processing, Exploitation, and Dissemination and filtering methods for mission-relevant information needs. The volume of collected ISR data often overwhelms manual and automated processes in modern analysis enterprises, resulting in underexploited data, insufficient, or lack of answers to information requests. The outcome is a significant breakdown in the analytical workflow. To cope with this data overload, many intelligence organizations have sought to re-organize their general staffing requirements and workflows to enhance team communication and coordination, with hopes of exploiting as much high-value data as possible and understanding the value of actionable intelligence well before its relevance has passed. Through this effort we have taken a scholarly approach to this problem by studying the evolution of Processing, Exploitation, and Dissemination, with a specific focus on the Army's most recent evolutions using the Functional Resonance Analysis Method. This method investigates socio-technical processes by analyzing their intended functions and aspects to determine performance variabilities. Gaps are identified and recommendations about force structure and future R&D priorities to increase the throughput of the intelligence enterprise are discussed.
Submarine commanders must make decisions rapidly to carry out increasingly complex missions. However, the rate of information delivery has outpaced the capacity of the command and control systems that prioritize and filter it. Technology could help commanders filter through data to make decisions, but this decision support must be carefully engineered to support the development of resilient courses of action (COAs). This paper details our experiences applying resilience engineering to submarine decision support. It focuses on designing two features that are essential for a resilient decision support system: (1) user interaction with a decision support system, which blends the user's operational insights with the technical aspects of the decision support system; and (2) visual representations of trade-offs. The paper ends with a discussion of the lessons learned from this work and a set of recommendations for designing decision support systems.
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