The Office of the Under Secretary of Defense (Personnel & Readiness), referred to throughout this report as P&R, 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. P&R 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. P&R 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 elsewhere—exploiting techniques such as complex network analysis, machine learning, streaming social media analysis, and anomaly detection—these 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 P&R.

Table of Contents


Front Matter
Summary
1 Introduction
2 Overview of the Office of the Under Secretary of Defense (Personnel & Readiness)
3 Personnel and Readiness Data and Their Use
4 Overview of Data Science Methods
5 Privacy and Confidentiality
6 Commercial State of the Art in Human Resources Analytics
7 Identifying P&R Opportunities and Implementing Solutions
Appendixes
Appendix A: Acronyms
Appendix B: Biographies of the Committee
Appendix C: Meetings and Presentations
Appendix D: Stochastic Models of Uncertainty and Mathematical Optimization Under Uncertainty