The Navy established the Digital Warfare Office in December 2016 to lead efforts to better utilize the vast amounts of data it produces each day to further advance its competitive advantage across all mission areas. The Navy is patterning its strategy on the success of the nation’s data-driven economy. From retail giants, like Amazon, to those in the technology, heavy industry, and academic and scientific spheres, organizations have harnessed data to attract and retain customers, make smarter business decisions, and advance scientific discovery. Using raw information coupled with new advances in data science, machine learning and artificial intelligence, organizations are reaping the benefits of smart data in myriad ways.
But the United States is not the only nation successfully using smart data to invigorate its economy or strengthen national security; increasingly, near-peer competitors and regional adversaries are using the same technologies to undermine U.S. security and defense and economic advantage.
“The Future Navy” whitepaper released by the Chief of Naval Operations (CNO) in June 2017 describes the faster-paced, more complex, and increasingly competitive security environment in which military forces operate today and into the future. A key contributor to the exponential pace of change is information technology, which enables greater data sharing, better insights, and more effective decision making through digital applications than the analog age could ever deliver. The Navy must compete in this highly informationalized environment by applying digital technologies to new ways of operating and sustaining its battle fleet. The Navy established the DWO to define requirements for the information environment and other capabilities that will accelerate the Navy’s pace and expand the scale and quality of its data-driven decisions and actions.
The need for an Echelon I-level team to influence Navy requirements and resourcing processes was a key finding of Task Force Netted Navy (TF2N), which the CNO chartered in 2016 to recommend ways to improve the development of more integrated, agile, and data-driven Navy capabilities that could pace rapid changes in threats and technology. The Task Force determined OPNAV leadership is essential because requirements and resource decisions drive technical and acquisition approaches that influence system design.
TF2N found that the Navy did not often specify requirements for the kind of information environment a program or system would need to operate in and contribute to, so tradeoffs could be made during system design that would impact information sharing, interoperability, and the agility programs had to incorporate new digital technologies, like cloud computing, predictive analytics and artificial intelligence. The Navy would need to change how it articulated requirements to deliver the right data environments within and across programs, and throughout the program lifecycle, to bring the power of the digital age to its industrial force.
Vice Adm. Jan Tighe, Deputy Chief of Naval Operations for Information Warfare, stressed the Navy’s need for enhanced analytical data for better informed decision-making at the DWO stand-up announcement. “An example might be an engine on an aircraft wing,” Vice Adm. Jan Tighe said in an interview with Federal News Radio in February 2017. “How do we make decisions on how many hours that engine can fly before it needs to come off and be inspected? In the past, we’ve just used an average. Whether the plane was flown in very harsh or very favorable conditions, they were all treated the same. With the data coming off those engines, you have a better ability to predict failures in blades, etcetera. That’s an industrial example, but there are so many ways to take advantage of the data that we already have. It could be our financial systems; it could be any of our functional areas.”
The Navy studied industry best practices in digital transformation to inform its way ahead. There were a handful of important lessons the Navy applied. First, digital change must be outcome-driven. The mission owner must define the desired end state and then determine how to best apply data and analytics to achieve that outcome. Because digital approaches require making data more transparent and sharing it across silos, leadership must also empower cross-organization teams that can access and integrate data that previously existed in silos. The Navy also needs to leverage human-centered design processes to ensure that data and analytics capabilities are developed around the decision-making needs of Navy users.
Another best practice the Navy is implementing is the use of model-based systems engineering (MBSE) in the design and development of capabilities. MBSE allows the requirements, technical, and acquisition communities to have a more robust dialogue about what users need from a mission and system perspective, capture those needs in a model, and collaborate in that modeling environment as the program develops. MBSE will provide more effective requirements definition and resource allocation, and the ability to review system development iteratively, instead of only at predetermined milestones when certain design trade-off decisions may already have been made.
The final best practice applied was the need for quick, small, outcome-driven pilot projects to drive digital change. The DWO is coordinating several pilots that use data science and digital technology to improve readiness, reduce costs, increase return on investment, and improve user experiences. The pilots are beginning to show that thoughtful data collection and analysis can go a long way in addressing readiness challenges. For example, the first pilot paired a team of data scientists from the Center for Naval Analyses with Navy F/A-18 Super Hornet aircraft maintainers, operators and supply specialists to determine how to use data and analytics to improve Super Hornet readiness.
Through the pilot, the team demonstrated that better sharing and use of machine-generated error code data at Naval Air Station Oceana could improve the time to repair aircraft radar at that site by 50 percent. Such an improvement would get ready aircraft back out in the fleet faster. Along with recommendations on data sharing and analysis, the pilot also identified policy and training changes to further improve maintenance outcomes. The Naval Aviation Enterprise is now implementing recommendations from the pilot at other Navy maintenance sites, and creating a plan to build out the data environment needed to support the expanded use of analytics for Super Hornet and other aircraft readiness challenges.
The Navy expects that scaling the findings and lessons learned in the pilot projects to broader data and analytic efforts across the Navy will give them a competitive advantage against adversaries and sustain operational excellence in a dynamic, resource-constrained environment.
CHIPS senior editor Sharon Anderson contributed to this report.