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Data Scientists at the Center for Naval Analyses develop innovative predictive and prescriptive analytics to tackle the most important challenges in the Department of Defense. These efforts rely on a collaborative partnership between Department of the Navy personnel and data scientists here at the department’s federally funded research and development center (FFRDC). Close collaboration ensures that insights can be developed iteratively and acted upon quickly. CNA’s Data Science Division, led by Vice President of Data Science Tim Kao, consists of approximately 30 data scientists with graduate degrees in data science, operations research, computer science, statistics, economics, and other sciences. They develop solutions for Department of the Navy leadership at forums such as Performance to Plan (P2P), which is co-chaired by the Vice Chief of Naval Operations and the Assistant Secretary of the Navy for Research, Development and Acquisition. CNA presents senior Navy leadership at the P2P forum with forward-looking performance forecasts, a foundation to progress toward readiness and capability goals.

The P2P forum accelerates the Navy's data-driven decision-making


Our approach has three fundamental steps:

  • Predictive analytics. CNA data scientists use modeling and simulation tools to construct a set of models that accurately forecast future performance.
  • Drivers. We build models that identify internal and external factors that significantly influence performance.
  • Prescriptive analytics. We determine the optimal combination of driver levels to achieve target performance.

Our iterative analytic methodology delivers quick insights to senior leaders so they can take action to achieve objectives to ensure a competitive advantage over adversaries while ensuring effective stewardship of resources.


F/A-18 Super Hornet readiness began decreasing steadily in 2008, and fewer than half were mission-capable in 2018, when CNA became the data science partner of the aviation P2P forum. CNA’s Data Science Division built a forecasting model that could anticipate the percentage of aircraft that would be mission capable with 97% accuracy. The model became an essential tool when then-Secretary of Defense James Mattis ordered the services to fix the problem by the end of fiscal 2019, setting an ambitious target of 80% readiness.

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Distribution A. Approved for Public Release; Distribution Unlimited.
Reason: Public Release
Approved by: Tim Kao, VP and Director, Data Science Division October 2021


  • Pages:
  • Document Number: DMM-2021-U-031024-Final
  • Publication Date: 10/1/2021