Strengthening Data Science Methods for Department of Defense Personnel and Readiness Missions

Strengthening Data Science Methods for Department of Defense Personnel and Readiness Missions

EnglishEbook
National Academies of Sciences, Engineering, and Medicine
National Academies Press
EAN: 9780309450812
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The Office of the Under Secretary of Defense (Personnel Readiness), referred to throughout this report as PR, is responsible for the total force management of all Department of Defense (DoD) components including the recruitment, readiness, and retention of personnel. Its work and policies are supported by a number of organizations both within DoD, including the Defense Manpower Data Center (DMDC), and externally, including the federally funded research and development centers (FFRDCs) that work for DoD. PR must be able to answer questions for the Secretary of Defense such as how to recruit people with an aptitude for and interest in various specialties and along particular career tracks and how to assess on an ongoing basis service members' career satisfaction and their ability to meet new challenges. PR must also address larger-scale questions, such as how the current realignment of forces to the Asia-Pacific area and other regions will affect recruitment, readiness, and retention. While DoD makes use of large-scale data and mathematical analysis in intelligence, surveillance, reconnaissance, and elsewhereexploiting techniques such as complex network analysis, machine learning, streaming social media analysis, and anomaly detectionthese skills and capabilities have not been applied as well to the personnel and readiness enterprise. Strengthening Data Science Methods for Department of Defense Personnel and Readiness Missions offers and roadmap and implementation plan for the integration of data analysis in support of decisions within the purview of PR.
EAN 9780309450812
ISBN 0309450810
Binding Ebook
Publisher National Academies Press
Publication date February 6, 2017
Pages 164
Language English
Country United States
Authors Board on Mathematical Sciences and Their Applications; Committee on Applied and Theoretical Statistics; Committee on Strengthening Data Science Methods for Department of Defense Personnel and Readiness Missions; Division on Engineering and Physical Sciences; National Academies of Sciences, Engineering, and Medicine