Defining the Roles, Responsibilities, and Functions for Data Science Within the Defense Intelligence Agency

by Bradley M. Knopp, Sina Beaghley, Aaron B. Frank, Rebeca Orrie, Michael Watson

This report addresses and recommends potential methods for the Defense Intelligence Agency (DIA) to identify, hire, and organize data scientists. The authors examine data science activities in the private sector and university-level data science training and also explore hiring and retention options for creating a data science capability within DIA. They also examine the results of interviews with DIA employees. The authors recommend that DIA create its own data science capability with a mix of government experts and contractors capable of managing activities unique to military intelligence operations and that DIA establish a center of excellence to oversee and promote data science activities, development, and training.

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Key Findings

Collaboration is essential to data science.

DIA must decide on what it wants to achieve through data science. It must decide whether to have an organic data science capacity or acquire data science products from outside.

DIA must also figure out the workforce skills it needs to achieve the desired output. It would be sensible to employ contract personnel, government civilians, and active-duty military personnel to ensure the broadest representation of technical skills and subject-matter expertise.

Training managers and leaders will be critical to embedding data science into the organization.

Recommendations

DIA should build a data science capability with a mix of government experts and contractors capable of managing activities unique to military intelligence operations.

DIA should establish a center of excellence or other centralized body to coordinate data science activities; develop tradecraft and methodologies; promote projects; and advocate for resources, support, and training.

A combination of contractors and government civilians/active-duty military will allow DIA to build an agile workforce with fungible capabilities.

DIA will need to establish a training structure for data science.

DIA should establish a Data Science Roundtable to achieve the internal cultural changes to ensure data science is an enduring agencywide capability.

Table of Contents

Chapter One

Introduction

Chapter Two

Data Science Activities in the Private Sector

Chapter Three

Data Science Education

Chapter Four

Identifying and Defining Data Science Specialties

Chapter Five

Data Science Capability in DIA Today

Chapter Six

Building and Maintaining a Data Science Capability

Chapter Seven

Organizing Data Science at DIA

Chapter Eight

Building DIA’s Data Science Capability: Findings and Recommendations

Appendix A

Interview Protocol

Appendix B

Notional Data Science Position Descriptions

Appendix C

Methodology

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Committee on Improving Defense Acquisition Workforce Capability in Data Use

Board on Mathematical Sciences and Analytics Committee on Applied and Theoretical Statistics

Air Force Studies Board

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Computer Science and Telecommunications Board Division on Engineering and Physical Sciences

Board on Higher Education and Workforce

Policy and Global Affairs

Committee on National Statistics

Division of Behavioral and Social Sciences and Education

A Consensus Study Report of The National Academies of Sciences, Engineering, and Medicine

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AMERICAN ARTIFICIAL INTELLIGENCE INITIATIVE

Lynne Parker, Ph.D.

Assistant Director for Artificial Intelligence

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Office of Science and Technology Policy

The White House

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Virtual, Digital and Hybrid Twins

A new paradigm in data-based engineering and engineered data

Francisco Chinesta Elias Cueto

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Emmanuelle Abisset-Chavanne Jean

Louis Duval Fouad El Khaldi

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Digital Twins and the Emerging Science of Self: Implications for Digital Health Experience Design and “Small” Data

Steven M. Schwartz 1*, Kevin Wildenhaus 2, Amy Bucher 3 and Brigid Byrd1

OPTIMIZATION-DRIVEN PRODUCT DEVELOPMENT TO SUPPORT DIGITAL TWIN CREATION ON THE SMALL UAV CASE STUDY

Mikhail P. Gusev

Sergei M. Nikolaev

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Anatolii P. Papulov

Sergei D. Belov

Daniil I. Padalitsa

Ighor K. Uzhinsky

Center for Design, Manufacturing and Material

Skolkovo Institute of Science and Technology

Bolshoy Boulevard 30, bld. 1, Moscow, Russia

{m.gusev,s.nikolaev,anatolii.papulov,sergei.belov,d.padalitsa,I.Uzhinsky}@skoltech.ru

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Using Digital Twins and Intelligent Cognitive Agencies to Build Platforms for Automated CxO Future of Work

John-Thones Amenyo

Department of Mathematics and Computer Science

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York College, CUNY

jtamenyo@york.cuny.edu

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Lab Session: Data Analytics/ AI

siemens.pt/digitalizacao