About CNA and its Data Science Division

CNA is a nonprofit research and analysis organization dedicated to the safety and security of the nation. It operates the Center for Naval Analyses — the only federally funded research and development center (FFRDC) serving the Department of the Navy — as well as the Institute for Public Research.

CNA is dedicated to developing actionable solutions to complex problems of national importance that support naval operations, fleet readiness and great power competition. Its non-defense research portfolio includes criminal justice, homeland security and data management.

CNA’s Data Science Division provides data science expertise and support to the Department of the Navy. The program develops advanced analytics and algorithms, including machine learning and predictive analytics, to identify key drivers of both successful and poor performance to improve Navy and Marine Corps outcomes. The program also supports decision makers with design-thinking facilitated workshops, a solution-focused methodology, and dynamic, quick-turn efforts.

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Data Literacy Course Overview

This course was designed by data scientists at the Center for Naval Analyses to be easily digestible and quick to complete. It provides an overview of issues that are commonly encountered in data-centric organizations.

Real-world examples are drawn directly from the ongoing Performance-to-Plan efforts in the Naval Aviation Enterprise. Notional examples developed especially for this course are based on events experienced by the authors.
The authors have striven to balance depth of information with breadth of topics to enable you to become a data-literate leader in a data-driven United States Navy.
 

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Data Analysis as an OODA Loop 

Data-driven decision-making is an iterative process. Analogous to Boyd’s famous OODA (Observe-Orient-Decide-Act) loop, the data analysis process comprises four fundamental steps: collect all of the necessary data; process the data to make them useful and usable; analyze the data rigorously and thoroughly; and act objectively upon the output of the analysis. A key goal of any data-driven organization is reducing the time required to act decisively on new data through critical analysis.