ISR (Intelligence, Surveillance, and Reconnaissance) systems consist of infrastructure that typically includes platforms and sensors, communications and data links, databases and data management, and visualization and dissemination technology. These systems are built to help decision-makers collect data, comprehend information, anticipate patterns and courses of action, and disseminate knowledge for a variety of missions. Those missions can be characterized by their timelines. Targeting and closing air support have timelines in order of seconds; tipping and cueing sensors and situational awareness in order of minutes; force protection and special operations in order of hours; and trend analysis and intelligence preparation in order of days.

Along with infrastructure, there are a suite of analytic tools that also support ISR. Analytic tools that support collection include tools for command control that coordinate and synchronize assets across multiple domains and organizations; tools for mission planning that plan routes for multiple aircraft simultaneously and in real-time; and tools for sensor resource management that balance multiple, competing objectives while simultaneously tasking multiple sensors. Analytic tools that support comprehension include single-intelligence target tracking that filters noisy data and produces tracks based on sensor data; and multiple-intelligence correlation and fusion that takes data from two or more sensors and produces a consolidated track picture. Analytic tools that support anticipation include tools for pattern discovery that learn ordinal and temporal patterns and build environmental and normalcy models; rule-based alerts that trigger on partial and approximant matches; and behavior characterization that establishes causality, predicts intent, explains anomalies, and analyzes alternatives.

These analytic tools support a wide variety of missions, and as such, can be hosted and accessed on a variety of platforms, on ground stations, or even at remote sites. This variety provides challenges and opportunities for IT. One of the challenges is that these ISR systems are mission-specific and existing information technology (IT) infrastructure was designed to meet specific mission requirements and not designed to be shared. But, the opportunity for ISR is that, data is rich when it is shared. Initiatives across the defense and intelligence communities for enterprise and shared services include the JIE (Joint Information Environment), ICITE (Intelligence Community Information Technology Enterprise), and Defense Intelligence Information Enterprise (DI2E). IT challenges these projects are working through include updating IT infrastructure, developing a framework for shared services, and enabling cloud-based solutions.

The Big Data "five Vs"- volume, velocity, variety, veracity, and value- can be used to describe ISR. Volume - it has been reported for more than a decade that the ISR community uses only a fraction of the volume of data it collects. Velocity-any multi-intelligence ISR mission experiences the challenges of velocity with data flowing in real-time, in batches, in a stream that is required to stay on for weeks or months. Variety-ISR is rich in variety, including geospatial intelligence, human intelligence, measurement and signature intelligence, signal intelligence, and open-source intelligence, sources that deliver structured, unstructured, semi-structured, and dynamic data. Veracity-it is critical to establish veracity to the intelligence that is produced to ascribe trustworthiness to the data. Value-finally, ISR too subscribes that there is only one 'V' that really matters-providing value to the decision maker. Even though ISR fits the five Vs, compared to commercial applications, there are many opportunities for advances in Big Data analysis to impact ISR to include warehousing, discovery, content management, analytics, and governance.

With the advent of conflict in non-traditional conflict zones and cities, the IoT (Internet of Things) opens a new arena as another sensing modality for ISR. ISR begins with persistent data collection, and in gray zones and cities, IoT comes with the advantage that it is pervasive and senses nearly every aspect of daily life. The challenge relates to how to gather useful information. IoT is nascent to the ISR community and just as one would with any new sensor, the conversation will begin with: What is signal and what is noise? What's in the foreground and background? How should the data be correlated, integrated, filtered and tracked? How does the analyst integrate IoT as another sensing modality?