Justin Neroda is Senior Associate with expertise in Mission and Performance Analytic solutions. He uses his deep technical knowledge of a wide range of modeling and simulation tools, as well as our Weapons System Life Cycle Management Process, to develop solutions for our clients across the DoD.
- What's happening at the intersection of cloud computing and mission, performance, and policy analytics? What kind of complex decisions are you helping your clients to inform?
- Over the last several years the cost of collecting data has been decreasing significantly, and our clients have been leveraging enterprise IT approaches to capture more detailed information about their organizations. Meanwhile, the cost of computing is decreasing and the capability of computing resources is increasing. The intersection of these two trends has resulted in our clients having large data volumes, but not having time and resources to analyze data sufficiently. This is where Booz Allen Hamilton’s mission, performance, and policy analytics capability can help. By combining our cloud computing knowledge with our analysis tools and approaches, my clients will have the tools and infrastructure to analyze growing data volumes in a timely manner in order to support rapid decision-making.
For example, my mission, performance, and policy analytics team has focused on structuring data and algorithm development to analyze data in near real-time that determines trends for the Department of Defense. This has included using data collected during operations on maintenance activities, maintenance requirements, asset utilization, operational tempo, and developing decision support tools to identify and drive new efficiencies. In addition we have provided actionable, fact-based information to support future planning and forecast future resource requirements. - From your perspective, how can “big data” and data driven discovery help organizations better understand the impact of decisions and future actions?
- By combining big data, cloud computing technology, and analysis tools, we obtain additional fidelity into current operations and these insights will allow my clients to make more informed decisions. In addition, these decisions will be backed up by quantitative data and analysis and tools that can more accurately forecast what affect those decisions will have on future performance. In my opinion, the key to success in the use of big data will be the ability to collect accurate data and implement newly available technology and analysis infrastructure that can support quantitative fact decision-making. Moreover, it will be critical to continually incorporate updated data into these approaches and tools to determine if conditions are changing and alert decision makers of those changes so they can make informed, proactive decisions.
My team and I are continuing to develop tools and approaches to allow tracking of operational data, presentation of the data in intuitive formats, and use of automated alerts to minimize the time from data collection to analysis to actionable information that support proactive decision-making as conditions change. - While cloud technology is nothing new, how is its practical application today driving operational efficiencies, improving data analysis and fueling new innovation?
- Cloud technology has been in development for several years, but only recently organizations have increased their adoption rate of cloud computing approaches. This was made possible by refinements to the types of cloud architectures along with establishing security mechanisms that provide improved information assurance for cloud users. Recently, most modeling, simulation, and analysis were executed using stand-alone computers. With the proliferation of both public and private cloud computing resources the ability to analyze more complex simulation problems for increasing numbers of scenarios has been possible by pushing these activities into the cloud resulting in greatly reduced time to execute modeling, simulation, and analysis.
From an innovation perspective, big data has allowed for more accurate modeling assumptions. In the past organizations were required to make assumptions on current system performance whereas now we can utilize data repositories of operational data to determine these assumptions with greater speed and accuracy. The combination of these elements has resulted in more capable models that more accurately represent systems that ultimately enable faster decision-making. It has also resulted in the ability to use models and simulations that leverage big data on a continuous basis to support ongoing operational decision-making. By establishing the right infrastructure, modeling and simulation tools can now support identification of issues and forecast potential courses of action using near real-time data.
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