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Empowering the Defense Acquisition Workforce to Improve Mission Outcomes Using Data Science (2021)

Chapter: 7 Findings, Conclusions, and Recommendations

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Previous Chapter: 6 Preparing and Sustaining a Data-Capable Defense Acquisition Workforce

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Suggested Citation: "7 Findings, Conclusions, and Recommendations." National Academies of Sciences, Engineering, and Medicine. 2021. Empowering the Defense Acquisition Workforce to Improve Mission Outcomes Using Data Science. Washington, DC: The National Academies Press. doi: 10.17226/25979.

7

Findings, Conclusions, and Recommendations

The findings, conclusions, and recommendations from the prior chapters in this report are provided below to provide a concise review of the major points from the committee’s study.

DATA SCIENCE IN DOD ACQUISITION

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Finding 4.3: Data silos, wherein data are not shared across DoD, are a common obstacle preventing the utilization of the full potential of the data life cycle.

Conclusion 4.1: With support from defense acquisition leadership, improved data-sharing policies and support for front-end phases of the data life cycle are major short-term opportunities to improve acquisition processes.

Conclusion 4.2: A common data infrastructure and platforms, personnel training and tools, and access to DoD-wide data are major long-term data science investments that will have long-term positive impact on acquisition processes.

Conclusion 4.3: Improved data use and the data life cycle could be applied to urgent, high-profile challenges in defense acquisition. Doing so would serve as exemplars of the value of data use and data-informed decision making in acquisition, serving as a catalyst for further change.

Recommendation 4.1: The Department of Defense should continue to seek improvements in defense acquisition through the increased application of data science, including addressing shortfalls in data collection, curation, management, and sharing.

DATA LIFE CYCLE MINDSET, SKILLSET, TOOLSET: ROLES AND TEAMS

Finding 5.1: An ever-increasing majority of the acquisition workforce is participating in the data life cycle. Just as defense acquisition is most successful when it is accomplished collaboratively, so is data science. Data science is an inherently collaborative field where success requires cooperation, communication, and coordination. Phases of the data life cycle directly or indirectly depend on one another, and those participating in the data life cycle must be able to relay key details to others.

Conclusion 5.1: Defense acquisition workforce members’ awareness of their value and individual role in the data life cycle is critical for data science applications. Barriers to this awareness include (1) lack of familiarity and use of the data life cycle, (2) adoption challenges for data science technologies and practices, and (3) widespread self-doubt of their own skillsets and insecurity or anxiety with STEM (generally) and data science (in particular).

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Conclusion 5.2: Improved data use in acquisition requires a collective skillset and teamwork among the acquisition workforce members. Team members will need data skills appropriate for their functional role in the acquisition community.

Finding 5.2: Upon graduation, undergraduate students are increasingly expected to have learned and applied some level of data science knowledge and skills.

Conclusion 5.3: Defense acquisition can expect the future workforce to have a baseline set of data science skills.

Finding 5.3: While specific outcomes are being updated and refined, data literacy for many undergraduate students includes an understanding of the data life cycle and how it works, data story-telling and communication, and an ability to recognize matters of ethics, privacy, and security.

Recommendation 5.1: The Department of Defense and its components should ensure that all members of the acquisition workforce and its leadership eventually have a common baseline data literacy, which includes an understanding of the data life cycle and how it works, data story-telling and communication, and an ability to address matters of data ethics, privacy, and security—all skills that may evolve along with the use of data science in government, industry, and academia.

Finding 5.4: Data scientists are experts across the data life cycle, with special emphasis on advanced techniques for collection, curation, management, analysis, and visualization.

Finding 5.5: Due to an evolving data science curriculum in higher education, not all data science degrees prepare students with the same skillsets.

Finding 5.6: Executing the data life cycle is a collaborative endeavor and generally requires a collective skillset found in teams of data engineers, data scientists, data analysts, data users, domain experts, and leaders/decision makers.

Conclusion 5.4: A data-capable defense acquisition workforce must—as a whole—have access to the full range of data science capabilities, from data collection and curation to data analysis and visualization.

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PREPARING AND SUSTAINING A DATA-CAPABLE WORKFORCE

Conclusion 6.1: Higher education and other training are at an inflection point in their delivery of online education and training; changes are happening rapidly. It is possible to monitor these developments to take advantage of the new opportunities that higher education offers through training partnerships to provide additional data analytics and data science training for acquisition personnel.

Finding 6.1: Some data-relevant courses in DoD use oversimplified data or scenarios with limited access to real data, preventing the student from learning how to deal with the inherent uncertainties and challenges in real acquisition data with all their complexity and quality issues.

Recommendation 6.1: Institutions that provide training for defense acquisition professionals should ensure that courses integrate realistic data and challenges in currently available courses, including non-data-focused courses. Realistic data and challenges offer students the opportunity to learn about uncertainty, sampling, variability, and noise. This realism provides personnel opportunities to learn and apply data techniques in acquisition scenarios and projects.

Recommendation 6.2: The defense acquisition system should continue to leverage and expand the variety of data science training options available both within and outside of the Department of Defense for the acquisition workforce. Trends include higher education, certificate programs, data “bootcamps,” micro-credentialing, online courses, and executive and leadership training. These options include data analytics and data science training programs offered by the Military Services, internal and external higher-education institutions, and the commercial sector.

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Finding 6.2: Evaluations and assessments are essential to pilot training efforts.

Recommendation 6.3: New training programs for acquisition leadership and personnel in data science should incorporate assessment parameters to evaluate their success. Successful approaches and programs can be expanded to increase effectiveness and broaden the data capabilities of the defense acquisition workforce.

Recommendation 6.5: Defense acquisition leadership should take program goals and characteristics into account when selecting or developing their data science training programs. Clarifying the data goals and key data capabilities necessary for different acquisition teams are essential steps for identifying suitable training programs.

Recommendation 6.6: Collectively, these recommendations should be considered by the defense acquisition leadership as part of the larger Department of Defense (DoD) Data Strategy. While DoD has a data strategy, it lacks specific guidance for how data science can improve defense acquisition. Specific guidance should address not only phases of the data life cycle and consumption, but also the workforce that facilitates these functions and is central to the data life cycle. Tradeoffs and investment limitations abound, so a strategic plan is critical to guiding and ensuring prioritized investments to maximize payoff.

SYNOPSIS

Based on testimonies and information collected as well as its experience and expertise, the committee believes that there are viable and cost-effective opportunities for DoD to improve defense acquisition processes and decision making through expanded use of the evolving capabilities in data use and data science broadly. Exactly what areas and what level of investment are appropriate given competing demands and other opportunities for the department must be decided by a strategic review, assessment, and plan of data-science needs, opportunities, costs, and anticipated returns against the return-on-investment from other opportunities. This report can be used—in guiding such a review, assessment, and plan—to embed the data life cycle into the acquisition process, establish roles and teams for executing the data life cycle, and build training opportunities for the acquisition workforce. Attention to workforce empowerment and training throughout the data life cycle, as outlined in this report, will further enhance DoD efforts to improve data use in defense acquisition, incorporate data-informed decision making, and advance the acquisition process.