Empowering the Defense Acquisition Workforce to Improve Mission Outcomes Using Data Science (2021) / Chapter Skim
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3 Data Science and the Data Life Cycle: The Short Version
3 Data Science and the Data Life Cycle: The Short Version
Pages 24-32
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We scroll through and choose from a list of recommended news articles selected for us by algorithms that combine our interests with current events. We make decisions that impact our national security by integrating, visualizing, and analyzing different data sources.
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In her 2020 Harvard Data Science Review article, Jeannette Wing wrote, "Data science is the study of extracting value from data. ‘Value' is subject to the interpretation by the end user and ‘extracting' represents the work done in all phases of the data life cycle" (Wing 2020)
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Figure 3.3, featured in the Federal Data Strategy: Improving Agency Data Skills Playbook and adopted from the National Institute of Standards and Technology (NIST) , depicts a comprehensive and well-managed data life cycle with similar steps to those in Figure 3.2.
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. FIGURE 3.3 NIST data life cycle.
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. Keller concluded her remarks by saying "the data science framework enables creation of repeatable and measurable processes for the use of and repurposing of all data sources." THE DATA LIFE CYCLE AND ITS PHASES Building upon these diagrams and for use in this report, the committee uses a workflow that (1)
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To better characterize how each phase in the data life cycle above contributes to data-informed decision making, the committee further describes the phases and corresponding questions and actions as follows: • Question -- Data science extracts value from data to help answer a posed question or inform a decision. Questions are often developed from engagement with stakeholders; decisions can be path-critical turning points where leadership may need to determine whether or not to move forward with a new product, process, or program.
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• Assess -- Continuously monitor and improve all processes in the data life cycle. Use the current analysis and results to refine and develop subsequent questions and decisions.
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In the opinion of this committee, the DoD acquisition workforce has been leveraging data science, even if not identified as such. Nevertheless, in Chapter 4, the committee identifies some opportunities for improved data use and describes how the data life cycle can be incorporated into common acquisition functions.
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For example, caution may be warranted in collecting data for developing artificial intel ligence analytic engines that make military decisions, such as whether to impose lethal force. Other examples may be systems that apply bias in social settings that may not yet be in line with evolving social norms (e.g., when data science may lead to racial profiling that may not be prohibited or may have conflicting policies)
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