In this special guest feature, Jim Tootell, Chief Software Architect at PMAT, discusses the importance for the defense industry to tap into big data technology. Jim leads the creation of game-changing analytics that break down data silos to solve complex business challenges for both government and industry. Using authentic DevSecOps, he integrates big data, artificial intelligence, machine learning to design C5ISR systems.
It is no secret that we live in a data-driven world now. Being able to distill actionable information from all the noise has driven the commercial industry not only to embrace Big Data analytics to great effect, but also to accelerate and expand its potential far beyond the original vision. The missions of the federal government and DoD create just as much noise as their private-sector counterparts. Only recently has the government started to overcome the limitations of the acquisition model, security concerns, domain silos, vendor lock-in, and legacy systems that were holding it back from reaping the same advantages. Coupled with a DevSecOps-based approach to software development and delivery, Big Data is now poised to give the DoD the “speed to operations” that it has so desperately come to need.
What Will Big Data Do for Defense?
Processing highly structured domain-specific data offers few benefits to a world full of asymmetric threats and multiplying attack vectors, nor does fielding purpose-built software systems based on pre-determined, hardcoded data, and business models. Today’s multi-source intelligence environments must shorten the timelines for delivering complex capabilities and application updates from months to days while remaining adaptable and scalable and integrating with legacy systems. Adding to the solution complexity and urgency is the fact that Defense industry challenges are different from most of the commercial sector due to the high risk to life, property, and peace and the incredibly compressed timelines in which our warfighters have to make decisions. Consider the classic OODA (Observe, Orient, Decide, Act) Loop: By using Big Data analytics to process data into information, we shorten the observe and orient phases so that warfighters will have more time to decide and take action. Building on Big Data foundations, artificial intelligence (AI) and machine learning (ML) are also increasing speed-to-action. For example, the US Navy and the Marine Corp can now access new classified data sets that bridge between Command & Control (C2) and the Intelligence Community (IC) to support real-time, data-driven actions and other expanded use cases the Navy.
Why Does Data have to Be Big?
Distilling actionable information requires vast volumes of data for trend analysis and model training. The more data that is analyzed, the higher the confidence will be in Big Data analytics results. Confidence in analytic results is what allows our military leaders to preemptively predict where the enemy might strike and make better decisions in near real-time.
Data does not have to be “big” all the time. There is a DoD movement to create smaller, more targeted data sets that apply more broadly to the missions at hand. These manageable data sets don’t necessarily originate from big data caches and benefit from a smaller cache. This data strategy has given rise to the need for data warehouses and even data lakes that expose orchestratable services that pull selectively from a wide array of sources based on geographic area, timelines, and mission objectives.
What were tomorrow’s projections for using Big Data are now becoming today’s realities. Governing bodies are tapping into best-of-breed technology and partners to create solutions that leverage and maximize some of the world’s largest repositories of classified data. Since Big Data, AI, and ML are already impacting the Defense industry’s future, the potential for delivering true “All Source” intelligence in a timely manner is within grasp. This transition means the future of Defense is finally starting to catch up to the rest of today’s data-driven world.
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