Empowering the Defense Acquisition Workforce to Improve Mission Outcomes Using Data Science (2021) / Chapter Skim
Currently Skimming:
5 Data Life Cycle Mindset, Skillset, and Toolset: Roles and Teams
5 Data Life Cycle Mindset, Skillset, and Toolset: Roles and Teams
Pages 42-61
The Chapter Skim interface presents what we've algorithmically identified as the most significant single chunk of text within every page in the chapter.
Select key terms on the right to highlight them within pages of the chapter.
From page 42... ...
Extracting value from data requires a collective data life cycle mindset, skillset, and toolset. In this chapter, the committee explores the constantly evolving ways industry, government, and academia are shaping the mindset, skillset, and toolsets of its employees and data science teams as well as how these best practices and trends yield opportunities for defense acquisition.
|
From page 43... ...
government-affiliated efforts have found that a strong, visible leadership commitment has begun to help overcome institutional inertia regarding new data tools and applications. In remarks given at the National Academies of Sciences, Engineering, and Medicine in March 2020, Sezin Palmer articulated challenges and lessons learned as a Johns Hopkins Applied Physics Lab data science team created a Precision Medicine Analytics Platform.
|
From page 44... ...
Unfortunately, some people may not recognize their often significant roles and value within the data life cycle of a program as anything different from their usual tasks in the acquisition process, or inaccurately believe that data science is only the purview of data scientists with extensive technical backgrounds. The bottom line is that an acquisition professional does not have to be a data scientist to have a significant role in the data life cycle.
|
From page 45... ...
Without an understanding of how they fit in a broader data life cycle and how their actions affect other parts in the cycle, the defense acquisition workforce may be missing or slowing opportunities to improve acquisition as well as advance their careers. Connected to these challenges in institutional and functional culture -- and lurking in the background -- is a perceived distinction between those that have data science skills and those that do not.
|
From page 46... ...
. 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.
|
From page 47... ...
Team members will need data skills appropriate for their functional role in the acquisition community. Data Literacy In response to the growing importance and ubiquity of data and datarelated tasks, the private sector is increasingly prioritizing data literacy for all employees, not just those in data analytics or data science roles.
|
From page 48... ...
For example, during the April 2020 workshop on "Improving Defense Acquisition Workforce Capabilities in Data Use," Matthew Rattigan (University of Massachusetts Amherst) stated that data literacy includes exploratory data analysis skills, data visualization and summarization skills, familiarity with experimental design, conceptual comparison of prediction vs.
|
From page 49... ...
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. Six Roles for the Data Life Cycle Workforce-wide data literacy is necessary but not sufficient for im proved data use within the defense acquisition workforce.
|
From page 50... ...
Data Scientists Data scientists are also specialists. They sometimes lead a data science team and have a technically specialized skillset and deep analytics knowledge.
|
From page 51... ...
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.
|
From page 52... ...
The section below titled "Team Structures for Data Science" addresses the skills for supervision and management of data science projects and teams. Finding 5.6: Executing the data life cycle is a collaborative endeavor and generally requires a collective skillset found in teams of data engi neers, data scientists, data analysts, data users, domain experts, and leaders/decision makers.
|
From page 53... ...
in hundreds of organizations spending more than $300 billion annually2 are unlikely to be addressed by a single official who is largely limited to trying to persuade others to invest in data resources and data sharing. Significant investment -- on the order of billions of dollars a year that DoD currently spends trying to achieve an auditable financial statement -- will likely be needed to develop the structured databases and modern analytic tools needed to build a modern data-centric environment inside DoD.
|
From page 54... ...
While all six roles are critical for defense acquisition communities for executing the data life cycle, the vast majority of the data-centric workforce will not need complex technical skills. Typically, organizations of all types and sizes should have many data analysts, data users, and domain experts and fewer data engineers, data scientists, and leaders/decision makers.
|
From page 55... ...
TABLE 5.1 Workforce Roles within the Data Life Cycle Phases in the Data Life Cycle Data Science Capability Level Question Define Coordinate Generate Collect Analyze Visualize Assess Curate and Manage Disseminate and Interpret Role Data engineer implementation specialist Data scientist assess/refine science and theory deeper capabilities acumen Data analyst assess/refine deeper capabilities Data users assess/refine Domain experts pose Leaders, decision makers, and pose literacy managers 55
|
From page 56... ...
Leaders must identify the critical data skills needed for their organization and each project by assessing current staff capacity, performing a data skills gap analysis, identifying ways to meet those needs, and making investments to right-size the team. Conclusions 5.5: Management of data science projects uses strategies and approaches for leading collaborative, cross-functional, technical projects with specific attention paid to the development of a team that has skills across the data life cycle and to asking questions specific to the quality and utility of data.
|
From page 57... ...
However DoD or individual military departments or acquisition programs choose to organize its data science teams, the acquisition workforce should have a collective data life cycle mindset, skillset, and toolset. Each member of the workforce will need well-defined roles within the data life cycle for any given project, and a set of accompanying skills for that role.
|
From page 58... ...
Recommendation 5.2: The Department of Defense should prioritize the utilization of data and the data life cycle by appropriate and judi cious investment in the data science mindset, skillset, and toolset of the acquisition workforce. This chapter concludes with Box 5.4, which revisits the defense acquisition examples first introduced in Chapter 1, with additional focus on the data-related skills and roles necessary in each situation that contributed to its success.
|
From page 59... ...
In this way, the question phase of the data life cycle is paramount. Test and evalu ation also rely heavily on skills from data engineers, to design data collection and prepare data for analysis, and data scientists, to perform statistical design experiments and formal inference and uncertainty quantification to address re quirements.
|
From page 60... ...
2020. Presentation at the Workshop on Improving Defense Acquisition Workforce Capability.
|
From page 61... ...
2018. Data Science for Undergraduates: Opportunities and Options.
|
Key Terms
This material may be derived from roughly machine-read images, and so is provided only to facilitate research.
More
information on Chapter Skim is available.