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Emerging Opportunities and Challenges

Data and Analytics 

Innovation

Report to Congressional Addressees 

United States Government Accountability Office

September 2016

GAO-16-659SP

Convened by the Comptroller General

of the United States

HIGHLIGHTS OF A FORUM
Why GAO Convened 

This Forum

Massive volumes of data are increasingly 

being generated at unprecedented rates 

as a result of advances in information 

technology and developments such as 

use of expanded mobile capabilities 

(supported by more powerful and 

increasingly widespread bandwidth). New 

approaches to combining and “making 

sense of” large amounts of varied 

data—methods referred to as advanced 

analytics—are helping to uncover 

patterns, identify anomalies, and 

provide insights not suggested by a priori 

hypotheses. In fact, advanced algorithms 

are enabling the automation of functions 

that require the ability to reason; for 

example, an algorithm may use data on 

weather, traffic, and roadways to estimate 

and provide real-time information to 

drivers on traffic delays and congestion.

At the January 2016 forum, participants 

from industry, government, academia 

and nonprofit organizations considered 

potential implications of these 

developments. Their discussion of 

emerging forms of data, analytics, and 

innovation pointed to a combined process 

or cycle in which relevant data are 

generated and subjected to analysis, so 

that results can be applied to innovative 

decision making or uses. We term this 

process “data and analytics innovation.” 

Consideration of related impacts spanned 

the economy and society, and included 

new opportunities with accompanying 

challenges, as well as potentially 

negative impacts. Following the forum, 

participants reviewed a summary of 

forum discussions, and two experts (who 

did not attend the forum) independently 

reviewed a draft of this report. The 

report does not necessarily represent 

the views of any individual participant or 

organization.

View GAO-16-659SP. For more

information, contact Timothy 

Persons, Chief Scientist, at (202) 

512-6412 or personst@gao.gov

Emerging Opportunities and Challenges

Data and Analytics

Innovation

Convened by the Comptroller 

General of the United States

What the Participants Said 

Forum discussions considered the implications of new data-related technologies and 

developments that are revolutionizing the basic three-step innovation process in the 

figure below. As massive amounts of varied data become available in many fields, 

data generation (step 1 in the process) is transformed. Continuing technological 

advances are bringing more powerful analytics and changing analysis possibilities 

(step 2 in the process). And approaches to new decision making include intelligent 

machines that may, for example, guide human decision makers. Additionally, data 

may be automatically generated on actions taken in response to data analytic results, 

creating an evaluative feedback loop. 

highlights of a forum

Forum participants 

• saw the newly revolutionized and still-evolving process of data and analytics 

innovation (DAI) as generating far-reaching new economic opportunities, 

including a new Industrial Revolution based on combining data-transmitting cyber 

systems and physical systems, resulting in cyber-physical systems—which have 

alternatively been termed the Industrial Internet, also the Internet of Things; 

• warned of an ongoing and potentially widening mismatch between the kinds of jobs 

that are or will be available and the skill levels of the U.S. labor force; 

• identified beneficial DAI impacts that could help efforts to reach key societal 

goals—through defining DAI pathways to greater efficiency and effectiveness—in 

areas such as health care, transportation, financial markets, and “smart cities,” 

among others; and

• outlined areas of data-privacy concern, including for example, possible threats to 

personal autonomy, which could occur as data on individual persons are collected 

and used without their knowledge or against their will.

The overall goal of the forum's discussions and of this report is to help lay the 

groundwork for future efforts to maximize DAI benefits and minimize potential 

drawbacks. As such, the forum was not directed toward identifying a specific set 

of policies relevant to DAI. However, participants suggested that efforts to help 

realize the promise of DAI opportunities would be directed toward improving data 

access, assessing the validity of new data and models, creating a welcoming DAI 

ecosystem, and more generally, raising awareness of DAI’s potential among both 

policymakers and the general public. Participants also noted a likely need for higher 

U.S. educational achievement and a measured approach to privacy issues that 

recognizes both their import and their complexity.

A Twenty-first Century Cycle: Data and Analytics Innovation

Source: GAO (analysis); GAO and Art Explosion (images). | GAO-16-659SP

1

0

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Aggregating, curating, and 

analyzing the data

Analytics

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Generating data in a manipulable, 

processable, or analyzable form

1 Data

3 2

Improving decisions or actions–and 

thereby extracting new economic

and societal benefits 

Innovation: Use of results
Page i GAO-16-659SP Data and Analytics Innovation

Letter 1

Foreword 4

Introduction 6

Far-reaching Economic Opportunities 20

Challenges to Realizing Economic Benefits: Uncertain Job Impacts and Potential Obstacles 32

Pathways to Societal Benefits and Accompanying Challenges 44

Privacy Concerns 52

Considerations Going Forward 64

Appendix I Forum Agenda 67

Appendix II List of Forum Participants 71

Appendix III List of Other Experts Consulted 73

Appendix IV A Brief Primer on the Internet of Things 78

Appendix V Profiles of DAI Developments in Three Areas 84

Appendix VI Scope and Methodology 99

Appendix VII A Brief Note on Federal Responses to Privacy Concerns 104

Appendix VIII List of References 107

Appendix IX GAO Contacts and Staff Acknowledgments 125

Contents
Page ii GAO-16-659SP Data and Analytics Innovation

Table

Table 1: Anticipated DAI Impacts on Selected Job Tasks 33

Figures

Figure 1: Key DAI Topics: Twenty-first Century Developments and 

Four Potential Areas of Impact 7

Figure 2: The Twenty-first Century Data Tsunami—Examples of 

Developments and Trends 8

Figure 3: The Twenty-first Century Data and Analytics Innovation 

(DAI) Cycle 12

Figure 4: Diagram: Windfarm Connected to the Industrial Internet 15

Figure 5: Open Data—Examples from Public and Private Sectors 21

Figure 6: Potential Magnitude of Global DAI Opportunities: 

Estimated Size for Two Areas Using Different 

Approaches 22

Figure 7: Key Interpretation Points for Economic Projections 

Related to Technology 23

Figure 8: Connected Sensors’ Varied Usage in Different Sectors 26

Figure 9: The New Industrial Internet Revolution 27

Figure 10: World Economic Forum Anticipates a Fourth Industrial 

Revolution 28

Figure 11: Number of Top Web Sites Hosted and Number of 

Colocation Data Centers: the United States and Other 

Nations 31

Figure 12: View of Workers in an Automated SemiconductorFabrication Facility 34

Figure 13: Rising U.S. Earnings Disparity: Median Incomes for 

Young Adults with and without a College Degree 35

Figure 14: Android Apps Sharing of ‘Potentially Sensitive’ Data 

with Third-Party Domains 40

Figure 15: Social Media Job Loss Index: Comparison to Initial 

Unemployment Claims 42

Figure 16: Health Care: Examples of How DAI Facilitates the 

Achievement of Important Societal Goals, and 

Challenges to Achieving Those Goals 45

Figure 17: Intelligent Transportation: Examples of How DAI 

Facilitates the Achievement of Important Societal Goals, 

and Challenges to Achieving Those Goals 47

Figure 18: Financial Markets: Examples of How DAI Facilitates the 

Achievement of Important Societal Goals, and 

Challenges to Achieving Those Goals 49
Page iii GAO-16-659SP Data and Analytics Innovation

Figure 19: Smart Cities and Urban Big Data: Examples of How 

DAI Facilitates the Achievement of Important Societal 

Goals, and Challenges to Achieving Those Goals 51

Figure 20: Flows of Location Data 53

Figure 21: Cost of Location Tracking by Method 60

Figure 22: A Fitness Tracker as an IoT Device 79

Figure 23: IoT World by Primary Use Case: Subcategories 80

Figure 24: Data Flows from Consumer Devices 81

Figure 25: Projected Growth of Connected Devices 82

Figure 26: Connected or Connectable Devices Used by 

Consumers and Patients 86

Figure 27: Increases in Sources of Personal Health Data and Data 

Flows, from 1997 to 2013 87

Figure 28: Potential Annual Health Care Savings Generated by 

Advances in Data and Analytics 88

Figure 29: Select Uses of ITS Technologies to Manage 

Congestion 90

Figure 30: Vehicle-to-Vehicle Communications and a Warning 

Scenario 91

Figure 31: Examples of Vehicle-to-Infrastructure Applications 92

Figure 32: Lending in the Context of Big Data Phenomena 95

Figure 33: A Credit Default Swap Market: A Network of Buyers 

and Sellers 96

Figure 34: Percentages of Large Institutions Reporting Three 

Types of Cyber Breaches 98

Figure 35: Dual-Band Authentication 98

Figure 36: Four Selected Federal Agencies: Relevance to Internet 

Privacy and Security 105
Page iv GAO-16-659SP Data and Analytics Innovation

Abbreviations

ACO accountable care organization 

ACOT2

 Apple Classroom of Tomorrow—Today 

AI artificial intelligence

BIAS broadband Internet access service

CDS credit default swap 

CFTC Commodity Futures Trading Commission 

CPS cyber-physical systems 

DAI data and analytics innovation 

DOC Department of Commerce

DOT Department of Transportation

DSRC dedicated short-range communication 

DTCC Depository Trust and Clearing Corporation 

EHR electronic health record

EULA End User License Agreement

FCC Federal Communications Commission 

FDA Food and Drug Administration

FHWA Federal Highway Administration 

FIPPs Fair Information Practice Principles 

FSOC Financial Stability Oversight Council

FTC Federal Trade Commission 

GPS Global Positioning System 

HIPAA Health Insurance Portability and Accountability Act 

IFTTT If This Then That 

IMSI International Mobile Subscriber Identity Catcher

IoT Internet of Things 

IP Internet protocol 

ISP Internet service provider 

ITS intelligent transportation system 

MIDAS Market Information Data Analytics System 

NHTSA National Highway Traffic Safety Administration 

NIST National Institute of Standards and Technology 

NTIA National Telecommunications and Information 

 Administration 

OECD Organisation for Economic Co-operation and Development

OFR Office of Financial Research

OTC over the counter 

PCORI Patient-Centered Outcomes Research Institute

PII personally identifiable information 

RFID radio-frequency identification

SCMS Security Credential Management System 
Page v GAO-16-659SP Data and Analytics Innovation

SEC Securities and Exchange Commission

STEAM science, technology, engineering, arts, and math

STEM science, technology, engineering, and math

SUV sports utility vehicle 

TTI Texas A&M Transportation Institute

V2I vehicle-to-infrastructure 

V2V vehicle-to-vehicle 

V2X vehicle-to-device

WEF World Economic Forum

This is a work of the U.S. government and is not subject to copyright protection in the 

United States. The published product may be reproduced and distributed in its entirety 

without further permission from GAO. However, because this work may contain 

copyrighted images or other material, permission from the copyright holder may be 

necessary if you wish to reproduce this material separately.
Page 1 GAO-16-659SP Data and Analytics Innovation

441 G St. N.W.

Washington, DC 20548

September 20, 2016

Congressional Addressees

This report presents the results of a Comptroller General forum on recent 

developments in the area of data, analysis, and innovation—and key 

implications for related economic opportunities, challenges to realizing 

such opportunities, new pathways to societal benefits, and privacy 

concerns.

The forum agenda (contained in app. I) structured forum discussions to 

allow each participant to comment openly and interact with others on any 

issue, without the need to comment on all issues. With the assistance of 

the National Academies, we selected forum participants (listed in app. II) 

to represent

(1) a range of backgrounds, covering academia, government, industry, 

and nonprofit organizations, and

(2) relevant experience across varied areas, including health care, 

intelligent transportation, financial markets, the Internet of Things, 

privacy, and cybersecurity.

Developed under the leadership of our Chief Scientist, this report 

summarizes the ideas and themes that emerged from the forum’s 

discussions. The report does not necessarily represent the views of any 

individual participant or organization. Similarly, the report does not 

necessarily represent GAO’s views.

Appendices III to VII of the report

• list the experts we consulted, in addition to forum participants (app. 

III);

• reprint a brief primer on the Internet of Things (app. IV) and three 

forward-looking profiles which describe data and analytic 

developments in health care, intelligent transportation systems and 

connected driving, and financial markets (app. V) and which we sent 

to forum participants in advance of the January 2016 meeting;

• explain the scope and methodology of our work (app. VI); and

Letter
Page 2 GAO-16-659SP Data and Analytics Innovation

• present a brief note on some federal responses to privacy concerns 

(app. VII).

We plan to send copies of the report to other appropriate congressional 

committees and interested parties. In addition, the report will be available 

at no charge on the GAO website at http://www.gao.gov.

We thank all the forum’s participants for taking the time to share their 

knowledge and insights and explore the topic’s implications.

Questions may be addressed to Timothy M. Persons, Chief Scientist, at 

(202) 512-6412 or personst@gao.gov. Contact points for our Office of 

Congressional Relations and Office of Public Affairs appear on the last 

page. Major GAO contributors are listed in appendix IX.

Gene L. Dodaro

Comptroller General of the United States
Page 3 GAO-16-659SP Data and Analytics Innovation

List of Congressional Addressees

The Honorable Fred Upton

Chairman

The Honorable Frank Pallone

Ranking Member

Committee on Energy and Commerce

House of Representatives 

The Honorable Lamar Smith

Chairman

Committee on Science, Space, and Technology

House of Representatives

The Honorable Jeff Flake

Chairman

The Honorable Al Franken

Ranking Member

Subcommittee on Privacy, Technology, and the Law

Committee on the Judiciary

United States Senate

The Honorable Barbara Comstock

Chairwoman

Subcommittee on Research and Technology

Committee on Science, Space, and Technology

House of Representatives

The Honorable Christopher Coons

United States Senate

The Honorable Mark Takano

House of Representatives
Page 4 GAO-16-659SP Data and Analytics Innovation

Recent trends in data development and analysis (for example, expanding 

Internet usage, the advent of the Internet of Things, advanced artificial 

intelligence, and “cognitive computing”) are creating innovation 

possibilities that carry the promise of far-reaching economic and societal 

benefits. Areas such as health care, transportation, and financial markets, 

to name just a few, may be improved or even transformed by innovations 

stemming from new data or new analyses. Moreover, the United States 

seems uniquely positioned to lead the way forward. At the same time, 

however, the specific nature and the full scope of the potential 

opportunities and benefits—and the obstacles to fully realizing them—

may not be adequately understood. There are also widespread concerns 

about the potential for negative impacts or side-effects of progress in this 

area, such as economic disruption and the diminishment of privacy, 

however unintended these may be.

The forum that the Comptroller General convened in January 2016 to 

explore these issues—with the goal of better informing policymakers and 

the nation—offered a series of facilitated discussions. (The agenda is in 

app. I.) This report summarizes key ideas and themes that emerged from 

those discussions. Included here is the notion of data and analytics 

innovation or DAI, which as detailed in the Introduction to this report, 

explains how value is added and why data are considered “the new oil.” 

The main body of this report presents forum participants’ views on DAI’s 

potential impacts, which concern

(1) far-reaching new economic opportunities;

(2) challenges to fully realizing economic benefits, including uncertainty 

about the net impact on jobs (given that technological advances may 

bring both job creation and job loss);

(3) future pathways to key societal benefits in areas such as health care 

and transportation, among others; and

(4) significant and complex privacy issues.

Finally, we discuss considerations going forward, based on the overall 

forum discussions as well as participants’ views on possible future actions 

or next steps. The goal is to help lay the groundwork for maximizing 

potential benefits and minimizing potentially negative impacts.

Foreword
Page 5 GAO-16-659SP Data and Analytics Innovation

All of us at GAO who have been involved in creating this report are 

grateful to the forum participants (listed in app. II) and reviewers and 

interviewees (listed in app. III), who contributed their ideas and efforts. 

We also acknowledge the invaluable support provided by the National 

Academies’ staff.

Timothy M. Persons, Ph.D.

Chief Scientist

U. S. Government Accountability Office
Page 6 GAO-16-659SP Data and Analytics Innovation

Recent technology trends and related developments, such as expanding 

Internet access and usage, are generating increasingly massive volumes 

of data at unprecedented rates and have been termed a virtual “data 

tsunami.” Exponential increases in the scale, scope, and speed of 

information generation, storage, and dissemination are changing the 

kinds of data that can be collected, the available means of analysis, and 

related innovation possibilities. As a result, data have been termed the 

“new oil.” And some observers anticipate a wave of innovation with 

transformative impacts across diverse economic and societal issues. 

Such observations and developments prompted the Comptroller General 

of the United States to convene the Forum on Twenty-first Century Data 

and Analytics, held in January 2016 at GAO headquarters in Washington, 

D.C.

Based primarily on forum discussions, this report describes an evolving 

phenomenon that we term data and analytics innovation. Data and 

analytics innovation (DAI) uses twenty-first century data and advanced 

analytical tools to revolutionize a basic innovation process—and thus 

creates the potential for a powerful twenty-first-century innovation 

megatrend.1 Both positive and negative impacts are possible (see fig. 1), 

and the overall goal of this report is to help lay the groundwork for efforts 

to maximize DAI benefits and minimize related drawbacks.

 1

A megatrend has been defined as a “long-term, transformational process with global 

reach, broad scope, and a fundamental and dramatic impact” (Vielmetter and Sell 

2014, 6).

Introduction
Page 7 GAO-16-659SP Data and Analytics Innovation

Figure 1: Key DAI Topics: Twenty-first Century Developments and Four Potential Areas of Impact

Note: New analytics are tools for examining large amounts of varied data to uncover subtle or hidden 

patterns, correlations, and other insights (e.g., market trends, customer preferences). Cognitive 

computing represents the ability of computing systems to perform human cognitive functions like 

memory, recall, judgment, inference, and learning. Advanced machine learning is an artificial 

intelligence (AI) discipline that allows computers to handle new situations via analysis, self-training, 

observation, and experience—with minimal “supervision” by humans.

The virtual data tsunami is associated with a number of recent datagenerating trends and developments. Although the full scope of the datatsunami phenomenon is difficult to convey, five key examples are shown 

in figure 2.

Background on the Data 

Tsunami
Page 8 GAO-16-659SP Data and Analytics Innovation

Figure 2: The Twenty-first Century Data Tsunami—Examples of Developments and Trends

Note: Examples listed are intended to be illustrative and are neither mutually exclusive nor 

exhaustive.
Page 9 GAO-16-659SP Data and Analytics Innovation

a

Internet users’ key-strokes, clicks, swipes, etc., represent digital information that is saved (and may 

be shared with others) by Internet-related companies.

b

Global Positioning System.

c

Includes online platforms for services based on labor or capital, or both, such as Uber, Lyft, and 

Airbnb (Morath 2016).

d

Datafication refers to the process of putting information in an easily analyzable format.

e

In this report, we use the term “connected sensors” to refer to the Internet of Things (IoT), the 

Industrial Internet, and cyber-physical systems. In each case, a device or system collects data—

typically through the use of sensors—and then transmits that data to a person or a machine for 

analysis, decision making, or action. (See also “A Brief Primer on the IoT” in app. IV of this report.)

f

Smart thermostats may be controlled remotely.

g

A smart traffic light is part of a system in that it receives information from a roadside sensor 

indicating traffic flows, and then it uses this information to adjust the timing of red and green signals 

(see the profile on intelligent transportation systems in app. V).

Internet-related companies now automatically collect online user data 

spanning almost the entire U.S. population—87 percent of adults, up from 

47 percent in 2000, according to Fox and Rainie (2014). Smartphones 

now provide anywhere, anytime access to an estimated two-thirds of U.S. 

Internet users.2

Commonly used online services have expanded from e-mail, search, 

navigation, shopping (or selling, through for example, eBay, Craigslist, or 

Etsy) to now include: (1) varied forms of social networking such as 

Facebook, Twitter, and Instagram; (2) online entertainment such as 

Netflix, Pandora, and Spotify; and (3) ride-sharing or space-sharing 

services (such as Uber, Lyft, and Airbnb), which are associated with the 

gig economy and connect consumers to online platforms that allow other 

individuals to earn income, based on their labor or capital (Morath 2016).

Cameras or other sensors (which in some cases may be mounted on 

drones or coupled with facial recognition software) are generating data 

on, for example, consumers in shopping areas and malls (for marketing 

purposes) or individuals in airports (for surveillance purposes).

Data derived from documents, records, maps, and other sources are 

being put into more easily machine-readable and analyzable formats with 

the purpose of facilitating various new services. To illustrate this process, 

the content of vast numbers of documents must be indexed to allow 

Internet searches. The scope of this effort is suggested by a Google 

 2

A. Smith et al. (2015); the iPhone was introduced in 2007.
Page 10 GAO-16-659SP Data and Analytics Innovation

fellow who has said that an index of the content of about 50-million pages 

is now sometimes accomplished in about a minute or less—whereas in 

1999 it took perhaps a month (J. Mitchell 2012). Another set of major 

efforts creates ever more detailed maps for navigation services. Such 

efforts have been termed datafication—that is, putting information into 

easily searchable and analyzable formats.3

The recent open data “movement” (Chignard 2013) includes (1) cities’

releases of real-time data—which may provide information on, for 

example, open parking spots, air quality, or pollen counts; (2) federal 

agencies’ releases of volumes of detailed data, such as local-area 

weather information that dates back many years; and potentially, (3) other 

kinds of data releases (for example, scientific data).

The term connected sensors is used here to refer generally to the 

emergence of the Internet of Things (IoT), the Industrial Internet, and 

cyber-physical systems (CPS)—all of which generate and transmit data 

and some of which include “actuators” or machines that take action based 

on data and analyses.4 Connected sensors may alternatively be:

• embedded in consumer devices, such as wearable fitness trackers 

that sense heart rate, provide feedback to the user, and allow 

comparison to other users;

• used in industry, including manufacturing, agriculture, and areas such 

as research, management, and sales; or

• employed by the public sector in varied applications—for example, 

smart traffic lights that collect sensor data on traffic flows and use 

 3

Another example of datafication is U.S. physicians’ adoption of electronic health records 

(EHR), discussed later in this report. Such adoption has been increasing—going from 

about 40 percent in 2008 to about 80 percent in 2014 (Office of the National Coordinator 

for Health Information Technology 2015)—and is anticipated to further increase in the 

coming years. 

4

As described later in this report, cyber-physical systems combine physical systems and 

cyber systems.
Page 11 GAO-16-659SP Data and Analytics Innovation

such data to alleviate congestion through adjustment of the timing of 

red and green lights in each direction.5

The data tsunami described above is fueling an evolving innovation 

process—or cycle—that extracts value from data with potentially very 

powerful results. This innovation process begins with the generation of 

data as depicted in the top box of figure 3 on the following page.

6

Additionally, the understanding of data, or its analysis, is being 

transformed by today’s advanced analytics (such as data-mining 

approaches) that can facilitate the analysis of very large amounts of 

varied data to uncover patterns, correlations, anomalies, outliers, and 

other insights not suggested by a priori hypotheses or explicit 

assumptions (see the right-hand box in fig. 3). The resulting insights 

might apply to, for example, market trends and future scenarios, customer 

preferences and behaviors, and the interactions, evolution, and 

trajectories of people, places, and things. New approaches to 

visualization are also relevant. To illustrate this point, varied data from 

thousands of online “posts, mentions, followers, fans, page views, 

reviews, [and] pins” can help evaluate a marketing campaign when 

combined in a single view to show “what [marketing efforts] worked...and 

what didn’t work” (Ingram MicroAdvisor 2016).

Further, advanced algorithms in computing systems are enabling the 

automation of functions that appear to require the ability to reason; for 

example, an algorithm may use data on weather, traffic, and road service 

capabilities to provide commuter alerts and suggest alternative routes.

 5

These five data-generating trends and developments, which are highlighted in figure 2, 

are presented as illustrative. We note that they are not mutually exclusive; for example, 

available data may result from a government’s making specific information more machine 

readable and easily analyzable and releasing that data to the public (items 3 and 4 in fig. 

2)—thus reflecting two combined or overlapping trends or developments. Additionally, the 

five trends are not necessarily exhaustive with respect to new data developments; for 

example, none of the five may adequately cover developments within specific areas of 

effort, such as new scientific data developments in genomics and other areas, which have 

been summed up as a scientific data deluge (Bell et al. 2009). 

6

An earlier version of the three-step process shown in figure 3 dates back at least to the 

1840’s,when the codification and analysis of data amassed from ships’ logs revealed more 

efficient routes—allowing sea captains to cut the length of long sea voyages “usually by 

about a third” (Mayer-Schönberger and Cukier 2013, 75).

Background on Data and 

Analytics Innovation
Page 12 GAO-16-659SP Data and Analytics Innovation

Figure 3: The Twenty-first Century Data and Analytics Innovation (DAI) Cycle
Page 13 GAO-16-659SP Data and Analytics Innovation

This means that the use of analytic results to improve actions or decisions 

is being transformed (see the left-hand box in fig. 3). That is, a machine 

may now contribute to decision making or, in some cases, may be the 

decision maker or action taker that responds to analysis results. For 

example:

• In some kinds of situations, a machine and a person may work 

together in using results, as would occur if a machine with cognitive 

computing capabilities7 assisted a person who was making a decision 

or taking an action. To illustrate this, IBM Watson’s cognitivecomputing capabilities are being used in a New Zealand bank to (1) 

provide online customers with “a timeline view of all their finances”

and “real-time personalized finance intelligence” and (2) help bank 

employees to “better gauge customer financial needs and engage 

them with offers to help” (Warner 2015).

• In other kinds of situations, advanced machine learning—an artificial 

intelligence (AI) discipline that allows computers to handle new 

situations via analysis, self-training, observation, and experience, with 

minimal “supervision” by humans—is being used to train fully 

automated vehicles to make driving decisions, based on sensor data 

about surrounding road conditions; see Stavens (2011).8

Finally, a further transformation in the use of results is occurring because 

new data on use of results (that is, data on decisions made or actions 

taken) may now be automatically generated. To cite a simple example, 

consider an online-shopping service that covers the three basic steps—

collecting data on customer purchases, analyzing those data, and using 

results to provide targeted advertisements (such as “people who bought 

the item in your cart also bought” other specific items9

). That online 

shopping service might also (1) retain data on shoppers’ purchases of 

advertised items and then (2) use that data to evaluate the overall 

effectiveness of the advertising approach. As stated in an Organisation 

 7

Cognitive computing systems are capable of performing human cognitive functions such 

as memory, recall, judgement, inference, and learning. 

8

In automated decision making of this type, monitoring and assessment of data quality 

and outcomes are needed to gain and maintain trust in DAI processes.

9

See Linden et al. (2003).
Page 14 GAO-16-659SP Data and Analytics Innovation

for Economic Co-operation and Development report (OECD 2015) on 

data-driven innovation:

“Decisions taken [based on results] can in turn lead to [the 

generation of] more or different data . . . and thus trigger a new 

data value cycle” (emphasis added).

An industrial example of such a cycle is provided in a discussion of 

systems that use connected sensors and analysis to help increase 

efficiency (Kalakota 2013). As illustrated for a connected windfarm in 

figure 4, initial data are generated by self-monitoring wind turbines with 

embedded sensors; an analysis is performed to indicate whether 

maintenance is needed; and technicians then use the results to guide 

appropriate action. In addition, technicians’ data on their use of results 

are sent—via the “Industrial Internet”— to a central management point, 

thus essentially creating a feedback loop. In other words, the new data on 

actions taken can be further analyzed by humans and machines—and 

perhaps combined with other data from the sensors, going forward, to 

evaluate and further improve the overall efficiency of the maintenance 

system.
Page 15 GAO-16-659SP Data and Analytics Innovation

Figure 4: Diagram: Windfarm Connected to the Industrial Internet
Page 16 GAO-16-659SP Data and Analytics Innovation

We note that:

• Most of the developments and trends discussed above are based on 

advances in underlying technologies, as well as the creation of 

extremely large data centers that (because they are connected to the 

Internet) can harness the power of connectivity as a force multiplier 

for computing. These data centers allow, for example, the efficiencies 

of cloud computing,10 as well as the storage and analysis of 

exponentially increasing amounts of data at lower prices (that is, 

commodity prices).

• Attempts to realize the promise of DAI may face certain barriers or 

challenges. One example is that if new kinds of data are used to 

represent a related—but somewhat different—type of occurrence, the 

results might indicate a pattern that seems relevant but is actually 

unrelated to what is being studied. Another is that if artificial 

intelligence (AI) is used by itself without sufficient oversight, there is a 

potential for the kinds of errors that “no human would ever” make—

even with advanced forms of AI.11 Issues such as these would seem 

to indicate the importance of monitoring, review, and assessment, 

going forward.

Major technological innovations carry a potential for both positive and 

negative impacts—on the economy and society. Innovation is generally 

viewed as both a driver of economic growth and a tool for addressing 

social issues. Despite recent debates among economists concerning the 

productivity paradox associated with economic data gathered over the 

past 10 years (that is, measures of productivity seem paradoxically lower 

than would be expected given apparent levels of technological innovation 

 10This is sometimes termed distributed processing.

11This issue was recently highlighted by Dr. Arati Prabhakar, Director of the Defense 

Advanced Research Projects Agency; see B. Mitchell (2016). Prabhakar further suggested 

that future intelligent machines might contribute to monitoring, review, and assessment by 

checking themselves and working together with humans to a greater extent than currently.

Background on DAI’s 

Potential Economic and 

Societal Impacts
Page 17 GAO-16-659SP Data and Analytics Innovation

during this time12), a dominant view is that innovation is a key driver of 

economic growth and can contribute to the achievement of important 

societal goals, such as increasing life expectancy. At the same time, there 

are concerns that major technological developments may generally—

perhaps typically—produce some negative consequences, which may 

occur as side effects of otherwise successful and beneficial advances; 

see Tenner (1997).

In this report, we highlight two concerns that have been voiced by experts 

such as Brynjolfsson and McAfee (2011), Murray (2016), and Bedoya 

(2015) and that are of some concern to many Americans; see A. Smith 

(2016); Turow et al. (2015); and National Cyber Security Alliance (2015):

• The first concern is that fast-paced or accelerating innovation may 

lead to job loss, potentially on a level exceeding key historical 

instances of technological change.13

• The second concern is that the ongoing expansion of data on 

individuals (such as Internet users) may be associated with a 

significant erosion of privacy.14

 12Different economists see the productivity paradox as stemming, to varying degrees, 

from factors such as the following, among others: (1) inadequate definition or 

measurement of productivity or lags in indicators (Nathan 2015c, quoting Joel Mokyr; 

Hatzius 2015); (2) current economic headwinds such as rising inequality, stagnating 

education advances, and an aging population (Gordon 2016); and (3) the issue of whether 

recent innovations (such as smartphones and social networking) fail to address basic 

human needs (Nathan 2015b, quoting Robert Gordon).

13While forum discussions of DAI’s potential for economic disruption focused on its 

possible impacts on jobs, we recognize that economic changes may directly impact firms 

as well. Concern about possible impacts on firms is reflected in, for example, the Trade 

Adjustment Assistance Program, which attempts to help U.S. manufacturing, production, 

and service firms remain competitive in the global economy; see GAO (2012b).

14Pertinent to this concern, no single federal law governs all use or disclosure of personal 

information; see GAO (2013a). GAO’s high risk list (GAO 2015a) now includes privacy 

issues. Among the many federal agencies concerned with addressing issues of data 

privacy and cybersecurity are the Federal Trade Commission (FTC) and the Federal 

Communications Commission (FCC), and at the Department of Commerce, both the 

National Telecommunications and Information Administration (NTIA) and the National 

Institute of Standards and Technology (NIST). See appendix VII.
Page 18 GAO-16-659SP Data and Analytics Innovation

The potential for DAI benefits and the existence of concerns about side 

effects framed a balanced approach to both the January 2016 forum and 

our specification of report objectives, as stated below.15

The objectives of this report are to summarize views of participants in the 

forum of January 27–28, 2016, convened by the Comptroller General, 

supplemented by the views of other experts and relevant literature, 

concerning the following topics:

1. The nature and significance of DAI economic opportunities.

2. The potential for future impacts on jobs (as a by-product of future DAI 

advances) and other challenges to realizing economic benefits.

3. The nature of pathways for progressing toward societal goals across a 

number of areas, as well as the potential challenges that may impede 

such efforts.

4. The nature and significance of concerns about privacy impacts 

associated with current and future DAI developments.

We note that

• The economic opportunities and societal benefits provided by 

technology may overlap. For example, technologies that represent 

economic opportunities may also further societal goals such as 

improving health. Therefore, the first and third objectives are not 

completely independent. However, separate consideration of the two 

topics allows each to receive an appropriate focus and full discussion 

within the scope of this report.

• Our goals are limited to providing a broad overview of key 

developments, trends, issues, and concerns for the set of objectives 

enumerated above. This report may potentially stimulate further 

review, evaluation, or consideration of policy approaches, if needed 

and appropriate. However, we did not attempt to comprehensively 

 15Views expressed in this report are not necessarily those of GAO but are to be attributed 

to expert participants who attended the forum or other experts, such as authors of relevant 

articles.

Objectives
Page 19 GAO-16-659SP Data and Analytics Innovation

cover all potentially relevant topics, nor did we evaluate any current 

government effort.16

We selected forum participants from academia, industry, government, 

and non-profit organizations, with the assistance of the National 

Academies. The forum, as noted, was held January 27–28, 2016, with 25 

expert participants.17

In advance of the forum, we developed a background Reading Package, 

based on interviews with experts and relevant literature, and distributed 

this document to forum participants. The Reading Package featured (1) a 

brief primer on the emerging Internet of Things (IoT), which includes 

devices that use sensors to collect data and then transmit that data via 

the Internet; and (2) three profiles of DAI activities in health care, 

intelligent transportation/connected vehicles, and financial markets. The 

primer and the profiles are included in appendixes IV and V of this report.

Following the forum, we sent participants (in early March) an initial 

summary of the forum discussions for their review. Of the 23 forum 

participants external to GAO, 21 responded based on an overall review of 

the summary, and 20 of the 21 indicated their overall agreement. Forum 

discussions and, as appropriate, participants’ post-forum comments were 

then combined with supplemental material, such as the interviews and 

literature that had contributed to the Reading Package. The resulting draft 

of the report was reviewed by two experts external to GAO who had not 

participated in the forum, as a final measure of quality control. Please see 

appendix VI for a fuller description of our scope and methodology and 

information on our data reliability steps.

We conducted our work from December 2014 through September 2016, 

in accordance with all sections of GAO’s Quality Assurance Framework

that are relevant to our objectives. The framework requires that we plan 

and perform the engagement to obtain sufficient, appropriate evidence to 

meet our stated objectives and to discuss any limitations in our work. We 

 16Some forum participants and other experts did make statements concerning government 

officials and staff, generally, which we summarize in the report—and we briefly cover four 

federal agencies’ efforts in discussing privacy concerns (in particular, see app. VII).

17Appendix II contains the list of forum participants.

Scope and Methodology
Page 20 GAO-16-659SP Data and Analytics Innovation

believe that the information and data obtained and that the analysis 

conducted provide a reasonable basis for any findings and conclusions.

Forum participants discussed far-reaching DAI economic opportunities, 

highlighting three specific areas of opportunity:

• open data’s potential to stimulate significant innovation and economic 

growth, both globally and in the United States;

• connected sensors18 as (1) an additional stimulus of innovation (which 

can produce growth or significant “economic impact”19)—again, 

globally and in the United States—and (2) the centerpiece of a new 

industrial revolution; and

• international use of U.S. commercial support systems for DAI (such as 

colocation centers20).

Participants indicated that data releases can stimulate innovation and 

thus can drive economic growth. The overall levels of such growth—

globally and in the United States—may be suggested by a McKinsey 

Global Institute estimate (Manyika, Chui, Groves, et al. 2013). The 

McKinsey analysis includes large and small datasets from governments, 

enterprises, other organizations, and individuals (see fig. 5 for examples) 

and considers innovations across seven widely varying domains. The 

focus is on the potential total value, per year, of innovative initiatives in 

which open data would be “necessary but not sufficient for realizing 

value” (Manyika, Chui, Groves, et al. 2013, 2).21

 18As previously noted, this report uses the term connected sensors to refer generally to 

the Internet of Things (IoT), the Industrial Internet, and cyber-physical systems (CPS).

19Economic impact has been defined, in this context, as including “consumer surplus [and] 

. . . new revenue that . . . will contribute to GDP growth” (Manyika, Chui, Bughin, et al. 

2013).

20Colocation centers (sometimes referred to as carrier hotels) rent out facilities such as 

space for servers, networking equipment, bandwidth, and so forth and “enable 

interconnection between carriers and content providers, web-hosting companies, and 

multi-system operators” (Colocation America 2016).

21That is, the relevant data might be one of multiple components needed to create the 

value counted.

Far-reaching 

Economic 

Opportunities

First Opportunity: Open 

Data’s Potential to 

Stimulate Innovation and 

Economic Growth
Page 21 GAO-16-659SP Data and Analytics Innovation

Figure 5: Open Data—Examples from Public and Private Sectors

a

Liquid or open data are defined by Manyika, Chui, Groves, et al. (2013, 3) as data that (1) have a 

high degree of accessibility by a wide range of users and minimal limitations on use, (2) are machine 

readable, and (3) have minimal cost.

The McKinsey analysis of the potential value associated with open-dataenabled innovation

• estimates a lower figure of about $3 trillion per year globally (see fig. 

6, upper bar), although the relevant time frame is not clear, and

• indicates that the U.S. share of this lower-bound figure would be over 

$1 trillion.
Page 22 GAO-16-659SP Data and Analytics Innovation

Figure 6: Potential Magnitude of Global DAI Opportunities: Estimated Size for Two Areas Using Different Approaches 

Note: The estimates shown above are presented here to illustrate the potentially far-reaching nature 

of varied DAI opportunities. Given possible overlap of the two areas, and possible time-frame issues, 

these estimates are not additive.

a

For the open data estimate, Manyika, Chui, Groves, et al. did not specify a future date. 

b

In this report, we use the term “connected sensors” to generally refer to the Internet of Things (IoT), 

the Industrial Internet, and cyber-physical systems.

c

For this estimate, Manyika, Chui, Bughin, et al. defined economic impact as including “consumer 

surplus [and] . . . new revenue that . . . will contribute to GDP growth.”

While these estimates are illustrative, difficulties in establishing the full 

scope of current DAI efforts and in anticipating the pace for adoption of 

new technologies (among other issues) mean that estimating DAI’s future 

economic potential is an uncertain endeavor. Please also note the 

general interpretation points highlighted in figure 7, which suggest caution 

in interpreting projections.22

22A related caution or caveat is that current-era technologies may not bring the same level 

of economic gains as technologies that emerged in previous eras, such as the midtwentieth century; see Gordon (2016).
Page 23 GAO-16-659SP Data and Analytics Innovation

Figure 7: Key Interpretation Points for Economic Projections Related to Technology

A forum participant illustrated the power of open data by detailing how 

data releases stimulated development of an information service for U.S. 

farmers and agri-business. He said he viewed many current goal-oriented 

efforts as “under-optimized” in terms of potential levels of progress toward 

those goals—due, in part, to information gaps. He then described a U.S. 

private-sector service that combines data released by (1) the U.S. 

Department of Agriculture (data on crop-yields for millions of specific farm 

fields) and (2) the National Weather Service (records for local areas, 

dating back many years). The service combines and analyzes these and 

other data in order to derive localized insights designed to help farmers 

achieve more efficient use of resources (such as fertilizer) and improve 

their planting decisions—thus potentially increasing productivity and 

income. The service, which farmers can access via an app on 

smartphones, would logically provide users with a potential competitive 

edge. Participants also emphasized that releases of sets of state and 

local data—including real-time data on, for example, open parking 

spaces—can be extremely valuable in stimulating innovation with goals 

such as better decision making and more efficient use of resources by 
Page 24 GAO-16-659SP Data and Analytics Innovation

consumers and government officials (as illustrated by some of the 

examples in fig. 5).23

Participants linked connected sensors24 to future economic opportunity

and a potential new industrial revolution. One expert emphasized that 

analyses provided by such systems—especially, analyses of the ways 

that many machines work together within an ecosystem of connected 

components—constitute a key area of further efficiency and productivity 

gains. In fact, an industry analyst (Ritchie 2015, 12) said that “in the 

context of the current productivity paradox,25 [the] IoT offers a potential 

solution to increase productivity both for industrial companies themselves 

as well as for their customers.” Some studies suggest the following:

• First, connected sensors may substantially raise productivity, creating 

major growth on a global scale (Evans and Annunziata 2012)26—with 

a McKinsey Global Institute projection putting the annual “economic 

impact” at about $3 trillion to $6 trillion by 2025 (Manyika, Chui, 

Bughin, et al. 2013),27 as shown in the lower bar of figure 6 (see also 

Lueth 2014).

 23Another example is the app Beagle Score, which helps businesses choose specific 

locations within Colorado by providing data on taxes, regulations, near-by businesses, etc.

24We use the term connected sensors to refer to devices and systems variously termed 

the Internet of Things (IoT), the Industrial Internet, and cyber-physical systems—all of 

which include devices that may use sensors to collect data, which are then transmitted via 

the Internet or other networks. Examples include fitness trackers, connected vehicles, and 

potentially in the future, fully automated vehicles—as well as connected machines in 

industrial settings, and systems related to public utilities. (See app. IV, “A Brief Primer on 

the IoT.”)

25As explained in the Introduction to this report, the productivity paradox refers to 

economists’ observations that during the past 10 years, measures of productivity have 

yielded lower values than would be expected based on the apparent level of technological 

innovation during this time (see Nathan 2015a).

26This General Electric report also anticipated higher U.S. incomes; however, others 

(Stiglitz 2016; Haskel et al. 2012; Mishel et al. 2013; and Mishel 2012) have provided 

evidence that over the past few decades, income gains have not matched productivity 

gains.

27This McKinsey projection for 2025 (Manyika, Chui, Bughin, et al. 2013) defines 

economic impact as including “consumer surplus [and] . . . new revenues that will 

contribute to GDP growth.” A subsequent McKinsey report projected even higher figures 

(see Manyika, Chui, Bisson, et al. 2015). In interpreting these projections and analyses, 

readers are encouraged to consider the four points presented earlier in figure 7.

Second Opportunity: 

Connected Sensors, 

Economic Impact, and a 

Potential New Industrial 

Revolution
Page 25 GAO-16-659SP Data and Analytics Innovation

• Second, the United States may be a leading—perhaps the leading—

country in terms of producing economic value related to connected 

sensors, according to reports from Accenture (Purdy and Davarzani 

2015) and Cisco (Bradley et al. 2013).

Forum discussions highlighted two areas of development for connected 

sensor systems:

• The first area consists of connected vehicles that are designed with 

(1) data-gathering sensors, (2) analysis capabilities to support driving 

decisions, and (3) the ability, in some cases, to take action. Advanced 

vehicles’ decision-making capabilities are based on machine learning. 

As a developer of an early version of the fully automated car wrote, 

“Our work does not rely on manual engineering or even supervised 

machine learning. Rather, the [fully automated] car learns on its own, 

training itself without human teaching or labeling” (Stavens 2011). In 

line with this observation, Kakaes (2013) said that the new generation 

of machine-learning techniques promises to create machines that not 

only match—but may actually exceed—human performance in areas 

that were previously seen as difficult to automate, such as driving.

• The second area consists of industrial systems that bring together 

cyber systems with physical industrial systems. The windfarm 

example discussed in the Introduction to this report is one concrete 

illustration of an industrial cyber-physical system or CPS (refer back to 

fig. 4). Another example, provided by Cisco Systems, is based on a 

precious-metals mining company that contracted for the development 

of an in-mine communication network (using custom antennae for 

communications through underground tunnels in which traditional WiFi would not work). The system links the following, among other items,

(1) wireless Internet Protocol (IP) phones28 and video surveillance 

cameras; (2) radio-frequency identification (RFID)29 tags on miners’

caps and vehicles, which are used to track their location; (3) sensors 

in vehicles that track engine and oil temperature; (4) programmable 

logic controllers on the conveyor system; (5) data on numbers of 

buckets filled; and (6) connected lights, fans, and the mine’s blasting 

system. By connecting people, equipment, processes, and data, the 

 28Such phones use the Internet as the medium for transmitting calls.

29RFID refers to “a technology that uses tiny computer chips to track items”

(Dictionary.com Unabridged 2016).
Page 26 GAO-16-659SP Data and Analytics Innovation

mining company—which had set a 30 percent increase in production 

as its goal—was, according to Cisco, able to achieve a four-fold 

increase in production without increasing manpower or vehicles 

(Cisco Systems, Inc. 2014).

The wider range of areas in which connected sensors are being applied is 

illustrated in figure 8. We note that these areas may overlap; for example, 

not only do connected vehicles (including current assisted-drive cars and 

the fully automated cars anticipated for the future) represent a consumer 

product, their development also reflects government efforts to achieve 

safer highways.

Figure 8: Connected Sensors’ Varied Usage in Different Sectors

Note: Icons depict varied uses of connected sensors (cyber-physical systems, the Internet of Things, 

or the Industrial Internet):

(1) by consumers in their vehicles and homes, and as wearable consumer devices such as fitness 

trackers;

(2) by industry in varied applications involving computer analysis of data generated by sensors, 

robotic machinery, and connected production systems such as the windfarm illustrated earlier in 

figure 4; and

(3) by the public sector in terms of worldwide connected systems such as GPS navigation 

(symbolized by the globe) and government involvement in connected utilities (that is, systems for 

water and electricity, symbolized by the drop of water and the lightning bolt).
Page 27 GAO-16-659SP Data and Analytics Innovation

Overall, participants viewed connected-sensors developments as 

potentially representing a coming Industrial Internet Revolution. Figure 9, 

adapted from a slide presented at the forum, depicts cyber-physical 

systems (CPS) that combine (1) innovations from earlier physical-system 

industrial revolutions with (2) developments associated with the relatively 

recent (late twentieth-century Internet) cyber systems revolution. The 

result is the creation of a new Industrial Internet Revolution for the twentyfirst century.

Figure 9: The New Industrial Internet Revolution

Notes: (1) The concept of three revolutions, which is illustrated in this figure, was earlier discussed by 

Evans and Annunziata (2012). (2) A forum participant commented that early developments related to 

computing spanned more than 50 years prior to the late twentieth century Internet revolution; this 

included, for example, the use of Hollerith punch cards to tabulate the 1890 census and the 

development of transistors in the 1950s; this participant anticipated the continuing importance of 

twenty-first century developments such as cognitive computing to the coming Industrial Internet 

Revolution.

We note that other experts (i.e., experts in addition to those present at the 

forum) also view the world as on the cusp of a new industrial revolution, 

based primarily on the advent and spread of CPS. Such systems are 

seen as representing a shift from simple digitization (or cyber systems) to 

innovation based on combinations of technologies (Schwab 2016a). 

Specifically, participants at the World Economic Forum (or WEF) held in 

Davos, Switzerland, January 20–23, 2016—close to the dates of the 

forum reported here—highlighted the coming of a “Fourth Industrial 

Revolution” (see fig. 10).
Page 28 GAO-16-659SP Data and Analytics Innovation

Figure 10: World Economic Forum Anticipates a Fourth Industrial Revolution

The WEF founder wrote that current technology trends are more than a 

prolongation of earlier developments (such as electronics, IT, and 

automated production); rather, current technology trends are 

characterized by an unprecedented speed of continuing breakthroughs:

“When compared with previous industrial revolutions, the [coming 

revolution] . . . is evolving at an exponential rather than a linear 

pace. Moreover, it is disrupting almost every industry in every 

country. And the breadth and depth of these changes herald the 

transformation of entire systems of production, management, and 

governance” (Schwab 2016a).

Additionally, a participant in our CG forum emphasized the role of

cognitive computing in some cyber-physical systems, and Schwab 
Page 29 GAO-16-659SP Data and Analytics Innovation

(2016b) stated that the fourth industrial revolution is “not only about smart 

and connected machines and systems” but includes developments 

ranging from nanotechnology to gene sequencing. Nevertheless, cyberphysical systems were seen as the new revolution’s hallmark or 

centerpiece.

A forum participant referred to rapidly advancing developments in natural 

language-processing, data mining, algorithmic sciences, and the real-time 

digital processing of unstructured data. He said that these developments 

are not only contributing to the creation of the Industrial Internet 

Revolution but will also create a new era of cognitive computing or 

artificial intelligence that may, in the future, take industry beyond the 

anticipated Industrial Internet or CPS revolution. And another expert 

emphasized the potential importance of advances in genomics, which 

might eventually create yet another technological revolution. In general, 

forum participants emphasized the potential for a very wide range of 

applications for DAI with respect to consumer devices and vehicles, 

industrial systems, public utilities, and infrastructure, as suggested earlier 

by the illustrations in figure 8. In some instances, connected sensors may 

provide a simple feedback loop. In others, advanced analytics may 

provide new insights or guide complex systems. In the words of one 

forum participant, we are currently in the “first inning” of connectedsensors development, which involves building upon and integrating earlier 

as well as new developments.

Developments such as those described above are, ultimately, powered by 

advances in underlying technology and technical services. Importantly for 

future developments, the pace of such advances has been described as 

showing no signs of stabilizing (Hagel et al. 2013). For example, the 

White House announced a competition to create “a new type of computer 

that can ... interpret and learn from data, solve unfamiliar problems ... 

and operate with the energy efficiency of the human brain” (Whitman et 

al. 2015).

Various Internet-related and other technical services can add value in 

their own right, including through leveraging U.S. competitive advantage 

in global markets. One participant highlighted advanced data services 

based on innovations in data science, new analytics, and software 

development—services that he said can be essential in supporting 

interoperability, visualization, and other capabilities needed to realize the 

potential benefits of data at scale for consumers, businesses, 

Third Opportunity: U.S. 

Provision of Technical 

Services in the Global 

Market
Page 30 GAO-16-659SP Data and Analytics Innovation

communities, and governments. Also relevant to the generation of value 

and revenue are:

• technical data-related services involving broadband communications, 

website-hosting, storage, curation, analysis, and dissemination (i.e., 

networks, devices, cloud, mobile, security), and

• extremely large data centers that are connected to the Internet and 

that harness the power of connectivity as a force multiplier for 

computing—resulting, for example, in the efficiencies of cloud 

computing, economies of scale, and commoditization of data storage.

As illustrated in a slide shown at the forum, the United States dominates 

in both (1) hosting top websites (see fig. 11, upper bar chart), and (2) 

providing colocation data centers—also known as carrier hotels (fig. 11, 

lower bar chart).

A forum participant emphasized that providing data services 

internationally (including, for example, hosting, storing, and processing 

data) represents a near-term strategy for U.S. economic growth, as this is 

an area in which the United States is currently dominant.

Carrier hotels rent out facilities such as space for servers, networking 

equipment, bandwidth, and so forth and “enable interconnection between 

carriers and content providers, web-hosting companies, and multi-system 

operators.”30 The global demand for such services is anticipated to grow 

in the coming years as the first and second Internet waves (that is, nearubiquitous Internet access and widespread mobile access) spread from 

leading countries to other nations, further around the globe.

One participant emphasized that with generally slow growth in the U.S. 

economy, the possibility of tapping into the higher growth rates 

characteristic of some other nations is an opportunity that should not be 

ignored. He said, in essence: “We want to benefit from growth elsewhere 

in the world.”31

 30Colocation America (2016).

31A logical caveat, however, noted by another participant, is that international efforts may 

be affected by issues such as U.S. and other nations’ policies concerning data privacy, 

especially as this may interface with national security. And, a different participant said that, 

increases in data center services would add to the environmental challenge.
Page 31 GAO-16-659SP Data and Analytics Innovation

Figure 11: Number of Top Web Sites Hosted and Number of Colocation Data Centers: the United States and Other Nations
Page 32 GAO-16-659SP Data and Analytics Innovation

Forum participants discussed varied challenges to fully realizing the 

potential economic benefits that might stem from DAI developments. 

These challenges include understanding:

• DAI’s impacts on jobs—including, for example, (1) differential impacts 

across types of jobs, (2) a potential mismatch between new skill 

requirements and current U.S. labor force characteristics, and (3) the 

uncertain future balance of job creation and job loss, and

• obstacles to realizing DAI opportunities—including four noted by 

participants: (1) limited data access for some, (2) limited support for 

DAI development, (3) uncertainties about the validity of data and 

models, and (4) limited awareness of DAI opportunities and 

challenges.

Participants generally agreed that current and upcoming DAI 

technological changes—including the anticipated new industrial revolution 

outlined in the prior section—would affect the types of jobs that are 

available and the specific job skills required. As outlined in table 1, DAIrelated developments would have limited impact on manual task jobs 

such as flight service and restaurant jobs (see row 1). By contrast, DAI 

could be associated with automating and possibly bringing extensive job 

losses for routine tasks such as bookkeeping and assembly-line work 

(see row 2). Abstract problem-solving jobs, which involve mental flexibility 

and often require a high level of education (for example, jobs in scientific 

research—see row 3), would likely be complemented by the new 

technology, rather than replaced by it; for this category, participants 

anticipated growth in both jobs and wages as possible or even likely.

Challenges to 

Realizing Economic 

Benefits: Uncertain 

Job Impacts and 

Potential Obstacles 

Uncertain Job Impacts

Differential Impacts on Job 

Tasks
Page 33 GAO-16-659SP Data and Analytics Innovation

Table 1: Anticipated DAI Impacts on Selected Job Tasks

Three types of job tasks Anticipated differential DAI impacts

Type of task and 

education level 

Task features or 

characteristics

Occupational

examples Type of impact Level of impact

Manual task with 

relatively low 

education required

• tends to involve 

environmental adaptability

• tends to involve 

interpersonal adaptability

flight attendant

waiter

cleaner

complementary or 

substitution

limited

Routine task with 

low to medium 

education required

• is rules-based

• is codifiable

• is procedural

bookkeeper

assembly line worker

direct substitution; job loss extensive 

Abstract task with 

very high education 

required in many 

cases

• tends to involve abstract 

problem solving

• tends to involve mental 

flexibility

scientist

attorney

manager

doctor

complementary; possible 

growth in jobs and wages 

extensive

Source: GAO, based on forum discussions and literature (Richard 2016; Autor 2015; Autor et al. 2003; Katz 2015). | GAO-16-659SP

Note: Some jobs cannot be easily categorized as one of the three types of job tasks highlighted in the 

table above.

As noted in table 1, some jobs cannot be easily categorized according to 

the three types of tasks highlighted (manual, routine, and abstract). To 

illustrate this, our earlier report on nanomanufacturing (GAO 2014a) 

discussed the need for new skills in high-tech, automated manufacturing 

(as illustrated by the semiconductor fabrication facility shown in fig. 12).

As our earlier report noted, today’s requirements for a high-tech 

manufacturing job may include an associate’s degree plus an aptitude for 

training in areas such as math and statistics (whereas previously, skilled 

manufacturing jobs may have required only a high school degree).
Page 34 GAO-16-659SP Data and Analytics Innovation

Figure 12: View of Workers in an Automated Semiconductor-Fabrication Facility

Additionally, recent advances in areas such as machine learning and 

robotics have been viewed by some as suggesting that going forward, 

computerization may be substituted for labor in “a wide range of 

nonroutine cognitive tasks” (emphasis added); see Frey and Osbourne 

(2013, 44, citing Manyika, Chui, Bughin, et al. 2013); see also 

Brynjolfsson and McAfee (2011).

Despite U.S. leadership in key areas of data and analytics, forum 

participants said that portions of the U.S. labor force are potentially not 

well positioned for developments involving increased opportunity for 

abstract-task jobs. This mismatch is highlighted by international 

comparisons. Notably, U.S. achievement in higher education now lags 

behind that of several other nations—with the United States ranking 14th 

among Organisation for Economic Co-operation and Development 

countries32 for the young adult (25 to 34) age group (OECD 2012). 

 32Thirty-four countries are members of the Organisation for Economic Co-operation and 

Development, including, for example, the United States, France, Germany, Greece, 

Japan, and Australia.

A Potential Mismatch
Page 35 GAO-16-659SP Data and Analytics Innovation

Additionally, the expected U.S. high-school graduation rate ranks near the 

bottom when compared to other OECD nations.

At the same time, median incomes have increased for U.S. young adults 

with a college degree—while decreasing for those with less education (as 

shown in fig.13). 

Figure 13: Rising U.S. Earnings Disparity: Median Incomes for Young Adults with 

and without a College Degree

Note: Refers to median annual incomes of U.S. workers age 25 to 32 in 2012 dollars.

In line with the above, one analyst (Autor 2015, 16) views recent U.S. 

growth in “the stock of workers with college and graduate degrees” as 

“sluggish”—at least when considered in relation to the apparently growing 
Page 36 GAO-16-659SP Data and Analytics Innovation

labor demand in this area.33 Participants observed that workers with the 

skills needed to develop, maintain, and operate the new technology are 

likely to be in high demand as employees.34 And as noted below, 

participants emphasized the importance of developing new business 

cases for newly released data or new IoT applications—efforts that 

require creativity. Thus, there appears to be a need for both:

1. Greater educational achievement for U.S. students, in general.

2. More extensive STEAM education and training at various levels.

STEAM refers to science, technology, engineering, arts, and math. The 

recent addition of arts—meaning design thinking in innovation—has 

shifted the term from science, technology, engineering, and math (STEM) 

to STEAM. (See, for example, GAO 2015b, which discusses STEAM in 

relation to additive manufacturing.)35

Different participants—and some other experts—envision different jobloss and job-creation scenarios. In some cases, they cited historical 

precedents, as follows:

• Scenario A: Sharply negative impacts on jobs in certain sectors. 

Some participants emphasized that new technology may destroy 

many old jobs. One participant illustrated this point with two examples, 

each within a specific sector: (1) The decline in U.S. farm jobs during 

the twentieth century, when the percentage of the U.S. workforce 

employed in farm labor dropped from 41 percent in 1900 to about 2 

percent in 2000—a drop that was associated with huge increases in 

productivity per farm worker;36 and (2) the possibility that fully 

automated (or autonomous) vehicles might eliminate or greatly reduce 

 33Warren Buffett has been quoted as observing that “economic rewards flowing to people 

with specialized talents have grown dramatically faster” than those flowing to people with 

“more commonplace skills,” which is “simply a consequence of an economic engine that 

constantly requires more high-order talents while reducing the need for commodity-like 

tasks” (Sinclair 2016).

34It has long been recognized that technological change can bring new educational 

requirements; see Clogg (1979). 

35Additionally, education may be specifically designed to foster creativity; for example, the 

Apple Classroom of Tomorrow—Today (ACOT2

) highlights a culture of innovation and 

creativity; see Apple, Inc. (2008).

36See Dimitri et al. (2005).

Job Creation and Job Loss
Page 37 GAO-16-659SP Data and Analytics Innovation

the need for taxi drivers, including those associated with ride-sharing 

companies such as Uber and Lyft. Additionally, one participant 

referred to others who have raised issues concerning a potential 

future “end of work.”

• Scenario B: Uncertain net impact, retraining required. One participant 

said that employers will require retraining of many lower and middlewage workers—which may include some currently skilled workers—to 

acquire the skills needed, going forward. Those who are not retrained 

or not re-trainable will be left behind (or left to find other work). This 

scenario appears to be playing out at a major company that is 

transforming itself and newly emphasizing computing services—

”managing all sorts of digital things: phones, satellite television, and 

huge volumes of data, all sorted by software managed in the cloud,”

with the transformation anticipated to be “well on its way” by 2020 

(Hardy 2016). The company is essentially requiring its employees to 

be “retrained to deal with the technology”—although the company’s 

executives estimate that eventually it “could get by with one-third 

fewer workers.”

• Scenario C: A potential for positive (or zero) net impact on jobs 

generally—and U.S. jobs in particular. A participant who held this view 

said that when craft manufacturing was replaced by the scientific 

manufacturing associated with Taylorism,37 in the early twentieth 

century, subsequent job growth outweighed job losses overall. The 

reason cited was that more goods (for example, more cars) were 

produced more cheaply, thus opening new markets and thereby 

creating jobs. (The same forum participant also suggested that nearterm U.S. job losses—which apparently would be occurring during a 

time of relatively slow U.S. GDP growth—might be offset by a strategy 

to “grow the size of the [U.S.] pie” by, as suggested in an earlier 

section of this report, pursuing DAI export opportunities in fast-growth 

areas of the world.)

The positive or neutral view in Scenario C is held by some other experts, 

as well; see Nanterme (2016) and Stewart et al. (2015). They say, for 

example, that technology’s role in job creation may be overlooked 

 37Taylorism refers to a theory of management aimed at increasing production efficiency by 

breaking down complex jobs into simple ones; measuring everything that workers do; and 

linking pay to performance. Its leading proponent in the late nineteenth and early twentieth 

centuries was Frederick Winslow Taylor.
Page 38 GAO-16-659SP Data and Analytics Innovation

because technology-related job losses are “more conspicuous” (Stewart 

et al. 2015), and that for reasons such as this, anxiety about automation 

will likely continue (Autor 2015, citing Akst 2013). Some forum 

participants and other experts have suggested that the issue of net job 

loss versus job creation is not well understood.

A final note on job trends and technological advances is that job trends in 

recent decades have been associated with a “hollowing out” of the middle 

class; see Rotman (2013). This may result, in part, from off-shoring 

related to both technology and trade deals (Reich 2016). If future 

technological developments related to DAI intensify the contraction of 

state-side job opportunities for those who do not obtain post-secondary 

education, the result could essentially create a new kind of digital divide—

one that applies to work and income opportunities. Although a full 

consideration of the challenges presented by economic disruption is 

beyond the scope of this report, as is the full range of potential societal 

responses, we note that a technology-related blog (Robbins 2016) 

observed:

“We have much to learn from the past three industrial revolutions. 

In each case, the disruption of the various industries ... led to a 

disruption of the workforce where new skills were necessary for 

economic survival. Oftentimes, the reskilling of workers lagged far 

behind the revolution, creating significant societal challenges and 

even leaving much of an entire generation stranded.”

Forum participants and other experts illustrated difficulties achieving DAI 

opportunities by noting four potential obstacles, including:

• limited data access (for example, new start-ups might not be able to 

access major portions of private-sector data);

• the difficulty of DAI innovation and the current lack of a sufficiently 

supportive ecosystem;

• possible validity problems in the data and models used in DAI efforts; 

and

• a lack of sufficient (or sufficiently up to date) awareness of DAI on the 

part of some government officials and staff.38

 38These four potential obstacles are not a complete list. For example, a forum participant 

briefly mentioned another potential issue concerning spectrum allocation for connected 

vehicles and highway safety.

Four Potential Obstacles 

to Achieving DAI 

Opportunities
Page 39 GAO-16-659SP Data and Analytics Innovation

With respect to the first obstacle—limited data access—participants said 

that currently, a few dominant entities already control (directly or 

indirectly) huge amounts of private-sector Internet-generated consumer 

data. Specifically, they said that a dominant company may

• collect data generated by its own widely used services, and

• receive data generated by various apps owned by other companies, 

as illustrated by a concentration of black lines going to the right-hand 

side of figure 14 (which uses the example of data from Android apps).

This situation was seen as potentially representing a barrier to entry and 

thus making the marketplace less dynamic with fewer start-ups.39

Forum participants also said that effectively disseminating pre-existing 

government data—to encourage the full potential of open data efforts—

requires facilitating business and citizen access and appropriate use. For 

example, such releases would ideally include metadata that adequately 

convey the strengths and weaknesses of released data when used for 

various purposes. However, achieving high-quality, easily accessed 

metadata would require thoughtful government efforts at the federal, 

state, and local levels—which in turn, may require either new funding or a 

shift in priorities at relevant levels. Forum participants felt the latter might 

be difficult to achieve because, in their view, some key government 

officials and staff might not sufficiently understand DAI developments or 

their implications.

 39The researchers who prepared the figure adapted here as figure 14 noted that they were 

not able to detect all forms of sharing—and thus, the figure likely understates the actual 

extent of sharing. A forum participant also emphasized that it is difficult for professionals to 

attempt to fully uncover data practices.
Page 40 GAO-16-659SP Data and Analytics Innovation

Figure 14: Android Apps Sharing of ‘Potentially Sensitive’ Data with Third-Party 

Domains

Note: Android apps are listed at left. Domains with which apps share data are on the right. Data 

shared include search terms and location data, which are considered “potentially sensitive” by Zang 

et al. (2015). The black lines indicate third-party sharing.
Page 41 GAO-16-659SP Data and Analytics Innovation

The second obstacle concerns the difficulty of identifying and developing 

new DAI use cases or business cases and the lack of sufficient support 

for such efforts. Given a context of cultural barriers40 and the lack of a 

supportive ecosystem, one participant emphasized the creativity and work 

required to (1) define a new use case in terms of a new or innovative IoT 

device or cyber-physical system and (2) demonstrate that the innovative 

approach will work in the real world as well as the lab. This participant 

illustrated the difficulty of these steps by stating his understanding that 

GPS navigation devices and services spread very quickly once the GPS 

location system was released for general use, because the military had 

already demonstrated the use case for GPS navigation. Additionally, 

considerable creativity and effort may be required to articulate business 

cases that are sustainable over time.

Various experts said investments in the future might speed the realization 

of economic opportunities—for example:

• increasing agency funding to support open data efforts (or, 

alternatively, shifting agency priorities to support open data);

• providing government support for innovative efforts by firms—or 

creating, for example, public-private partnerships; and

• helping create a more broadly based supporting framework or 

ecosystem for the development of data-based innovations by, for 

example, establishing prizes41 for creative innovations or developing 

networks for sharing information or obtaining resources.

The third obstacle concerns uncertainties about the validity of data and 

models. For example, problems may occur if a certain kind of data or 

model is suited to a particular policy or business purpose, but possibly not 

others, or when indicators change over time. The latter suggests that 

 40One participant emphasized that cultural shifts may be required to achieve envisioned 

DAI outcomes. This participant described the “deception phase” in the progression of a 

new technology in society (a concept that has been articulated by Diamandis and Kotler 

2015). Briefly, at relatively early points in technological development, progress is building 

but appears to be slow. As a result, some may underestimate the power and import of 

early technological developments—and fail to appreciate the need for change, or the 

change itself.

41An example is Go Code Colorado—a contest in which “teams create app and business 

ideas that solve a business challenge using public data” (Marino and Spyker 2015). Prizes 

of $25,000 are awarded to winners. One winning innovation, Beagle Score, is briefly 

described in a prior footnote in this report.
Page 42 GAO-16-659SP Data and Analytics Innovation

ongoing evaluation of the validity of data and models could be an 

important component, going forward. To illustrate this challenge, currently 

ongoing work (Shapiro and Cafarella 2016) is evaluating a social-mediabased index of unemployment trends, in terms of its value as a

meaningful measure of economic activity over time. The index, which 

draws on tweets such as lost my job, closely tracked new unemployment 

claims for a few years, starting in mid-July 2011, and appeared useful for 

policymakers and others who need to make decisions in advance of the 

release of official statistics (Antenucci et al. 2014). The accuracy of 

prediction with respect to initial unemployment claims began to deviate 

around mid-2014 (see fig. 15), but this phenomenon is still being tracked.

Figure 15: Social Media Job Loss Index: Comparison to Initial Unemployment Claims

Across a variety of application areas, the importance of attending to the 

monitoring and evaluation of data and models would logically increase 

with the importance of the decisions or actions that are expected to be 

based on the results.
Page 43 GAO-16-659SP Data and Analytics Innovation

Finally, the fourth potential obstacle that participants highlighted concerns 

a possible lack of sufficient awareness on the part of some government 

officials and staff—and a related potential for difficulties achieving 

appropriate or sufficient policy and government actions in this arena. 

Although at least one agency (the Department of Commerce) appears to 

have made data dissemination a top priority,42 multiple participants—at 

different points in the forum discussion—said that in the United States 

and other nations, some government officials and staff may not 

sufficiently understand current DAI developments. One participant said 

that in some cases, an agency may give its efforts a “Big Data” overlay or

icing. He further said that in some cases: “They don’t get it. . . . and they 

don’t get that they don’t get it.”

 42“Maximizing the positive aspects of Commerce data” is one of five strategic goals 

identified by the U.S. Department of Commerce (2014).
Page 44 GAO-16-659SP Data and Analytics Innovation

Participants identified new pathways to societal benefits in the areas of 

health care, intelligent transportation, financial markets, and smart cities 

and community planning, while also noting challenges to achieving these 

benefits. Participants emphasized that these new approaches are not 

simply data, but data that are viewed as an asset, and analyzed and used 

to make decisions about the optimal allocation of resources to achieve 

significant individual and societal goals. Moreover, participants reflected 

that society overall is at a very early stage in understanding and 

developing the infrastructure required to obtain the potential benefits from 

DAI, and there are significant challenges to be overcome if these benefits 

are to be achieved in a more timely manner than currently seems likely.

As we describe below (see section on Considerations Going Forward), 

forum participants identified actions that could be taken to address 

challenges to achieving DAI benefits. At the same time, however, 

participants noted the importance of ensuring that actions taken now do 

not have the potential to hinder advancements in human productivity and 

prosperity made possible by developments associated with DAI. Using 

examples identified in forum discussions and in the Profiles presented in 

appendix V, we next highlight both the potential benefits and the 

challenges to achieving those benefits.

As described in the health care profile and discussed at the forum, 

advances based on DAI seem poised to generate major benefits. These 

benefits span the areas of (1) research; (2) delivery of care, including 

diagnosis, treatment and follow-up; and (3) preventative care and lifestyle 

changes that may contribute to an overall reduction in health care costs, 

among other benefits. In forum presentations and discussion, participants 

highlighted:

• improvements in personalized medicine and health care outcomes 

resulting from the integration of data from personal fitness trackers, 

genomics, and the relative effectiveness of medical interventions, and

• improvements in public health through increased understanding of risk 

factors, early warning systems for epidemics, fraud modeling and 

detection, risk modeling, and management.

As described by one forum participant, building on digital health 

information about each individual and based on the move toward 

electronic health records (EHR) in the U.S. and other nations, with this 

information then shared across the population of health practitioners and 

Pathways to Societal 

Benefits and 

Accompanying 

Challenges

DAI Pathways to Health 

Care Benefits
Page 45 GAO-16-659SP Data and Analytics Innovation

others involved in the provision and delivery of health care services, the 

ultimate goal is to achieve more optimal health outcomes for all.

Figure 16 illustrates the DAI process in health care as well as challenges 

to achieving the potential benefits. As noted in the health care profile in 

appendix V, there are many ways to enter and store EHR data, and

multiple EHR systems are available for use. These systems may not be 

technically compatible in structure or format, and in the absence of 

agreed-upon standards for information entered as well as mechanisms for 

sharing and aggregating the data, a truly interoperable system would be 

impossible. However, with the potential for annual savings in the range of 

$300 billion to $450 billion in health care costs in the U.S., according to 

one estimate (Groves et al. 2013), the value of increasing standardization 

and technical interoperability could be substantial.

Figure 16: Health Care: Examples of How DAI Facilitates the Achievement of Important Societal Goals, and Challenges to 

Achieving Those Goals
Page 46 GAO-16-659SP Data and Analytics Innovation

As described in the intelligent transportation profile, data and analytics 

have begun to affect the efficiency and safety of surface transportation 

through the research, development, and deployment of intelligent 

transportation systems (ITS), including emerging technologies; the Global 

Positioning System (GPS); and the Internet of Things (IoT). ITS platforms 

enable vehicles to transmit data to other vehicles or to roadway 

infrastructure, and analyses of these data may help make driving private 

vehicles both safer and more efficient. In forum presentations and 

discussions, participants identified a range of emerging and potential 

benefits from developments in ITS, including:

• increased efficiency in transportation resulting in improved air quality;

• improved vehicle coordination and safety in merging and following 

and other driver behaviors that may lead to automotive accidents, 

resulting in fewer casualties and other associated costs;

• improvements in the quality of information about road infrastructure or 

safety, which can allow for better decisions about the allocation of 

resources for maintaining or upgrading road infrastructure or 

improving safety; and

• reinforcement for safer driving by tying insurance costs to individual 

driving preferences and behavior.

As described by one forum participant, developments in intelligent 

transportation provide the opportunity for road transportation to function 

as a system comparable to air and rail transportation systems, although 

there remain significant challenges to achieving the benefits in safety and 

efficiency that could result. Figure 17 illustrates the DAI process in 

intelligent transportation, as well as the challenges to achieving the 

potential benefits. As described in the intelligent transportation profile 

(see app. V), technologies such as advanced wireless communications, 

on-board computer processing, advanced vehicle sensors, and GPS 

navigation are now being networked into what is known as the connected 

vehicle, which includes vehicle-to-vehicle (V2V) and vehicle-toinfrastructure (V2I) technologies. Both V2V and V2I technologies are 

designed to transmit data to warn drivers about the potential for collisions 

and to provide other information about traffic congestion or road 

infrastructure that can alert drivers to circumstances they may be able to 

avoid.

Turning to challenges, forum participants identified issues of data 

ownership, access rights, privacy, and cybersecurity that must be 

addressed before greater progress in achieving improvement in road 

DAI Pathways to 

Intelligent Transportation
Page 47 GAO-16-659SP Data and Analytics Innovation

transportation safety and efficiency may be made. One participant also 

noted that automated vehicles may, to some extent, tend to increase, 

rather than decrease, traffic congestion and/or commuting times; for 

example, persons working in urban areas might decide to live further from 

city centers if vehicle occupants were freed from driving tasks and thus 

could engage in other activities during a commute. Participants 

mentioned that another challenge stems from these technologies’

dependence on the 5.9 GHz band radio-frequency spectrum—a limited 

resource also in demand by non-transportation users.

Figure 17: Intelligent Transportation: Examples of How DAI Facilitates the Achievement of Important Societal Goals, and 

Challenges to Achieving Those Goals

Note: V2V refers to vehicle-to-vehicle, and V2I refers to vehicle-to-infrastructure.

In the financial sector, developments in DAI are changing the way that 

banks and insurers conduct financial business and are improving the 

ability to monitor and provide information on financial markets and 

institutions in those markets. Participants in the forum emphasized the 

benefits of data and analytics in terms of creating:

• a database for tracking and analyzing stock market trades to identify 

potential improprieties and better understand how the market works, 

and

DAI Pathways to Improved 

Financial Markets
Page 48 GAO-16-659SP Data and Analytics Innovation

• a sounder foundation for the financial industry and world economy, via 

global standards, such as the Legal Entity Identifier.43

As described in the financial markets profile, credit default swaps (CDS)

were central to the system-wide problems encountered during the 

financial crisis of 2007–2009, but new data and analytics may allow for 

addressing or avoiding such problems in the future. Prior to the crisis, 

CDSs were not well understood by regulators or market participants. 

Given this lack of understanding and the role of CDS in the crisis, 

regulators have begun to collect new data and develop analytical tools to 

provide greater clarity and insight into monitoring and addressing 

systemic risk. In this case, systemic risk refers to risk that the failure of 

one large institution would cause other companies to fail or that a market 

event could broadly affect the financial system rather than just one or a 

few companies. As described by a forum participant with expertise in this 

area, there has been little motivation for standardization and transparency 

in this area in the past, because there was little economic advantage to 

be gained as a result.

Figure 18 illustrates the DAI process in the area of financial markets, as 

well as challenges to achieving potential benefits. As identified in the 

financial markets profile, the algorithms, or decision-making rules, that 

financial institutions use may not be transparent. And as emphasized by a 

forum participant, to the extent that a decision such as whether someone 

qualifies for a loan is being made by a software program (or algorithm), 

there is the potential that discrimination could be coded into the program, 

whether inadvertent or not. Although this development was presented as 

a potential drawback to innovation associated with data and analytics, 

forum participants also highlighted the use of data analytics as a tool to 

address inequities and social exclusion in access to public and private 

services. If the results of automated decision making are monitored to 

determine whether outcomes are consistent with legal or ethical 

prerequisites, and algorithms are changed as needed to correct for 

inappropriate bias, the full societal value of data and analytics is more 

likely to be achieved. Moreover, as noted above, future intelligent 

machines may contribute to monitoring, review, and assessment of 

 43The Legal Entity Identifier (LEI) is “a reference code to uniquely identify a legally distinct 

entity that engages in a financial transaction. Currently, there are many ways to identify 

entities, but there is no unified global identification system for legal entities across markets 

and jurisdictions” (U.S. Department of the Treasury 2012).
Page 49 GAO-16-659SP Data and Analytics Innovation

algorithmic processes by checking themselves and working together with 

humans to a greater extent than currently, to better ensure broader 

societal value.

Figure 18: Financial Markets: Examples of How DAI Facilitates the Achievement of Important Societal Goals, and Challenges 

to Achieving Those Goals

Forum participants identified developments in the area of smart cities or 

urban Big Data as providing opportunities for community policy and 

planning reform, by bringing together the hardware, software, 

management mechanisms, and other resources necessary to build the 

infrastructure for collecting, integrating, and analyzing data across 

domains. More specifically, these domains include transportation, 

agriculture, health, energy, water, waste, and environmental 

management, as well as social media, government administration, and 

real estate and business transactions. Such developments, in turn, open 

up the potential for a data-driven approach to planning and decision 

making in the public, private, and non-profit sectors.

DAI Pathways to Smart 

Cities and Community 

Benefits
Page 50 GAO-16-659SP Data and Analytics Innovation

In the areas of transportation and public health, data-driven approaches 

are not new. Recent and ongoing developments associated with data and 

analytics, however, greatly increase the potential for sharing data across 

domains of urban life, allowing for analysis of how communities develop 

and change over time, as well as more real-time insight into community 

life. With respect to the benefits of creating and maintaining urban BigData ecosystems, forum participants discussed:

• Improvements in infrastructure and public services planning. These 

improvements could be: (1) based on social media or other data on 

social and economic activity, and (2) used to predict where new or 

different types of services may be needed, or where new or different 

retail opportunities may exist, in advance of government census 

information.

• Increased ability to identify and monitor spatial and social disparities 

in the availability and price of housing for rent or sale.

• The creation of smart cities or smart communities that apply IoT and 

other Big Data sources to improve the quality of life for residents.

Figure 19 illustrates the DAI process in the area of smart cities and urban 

Big Data, as well as challenges to achieving potential benefits. With 

respect to the challenges of achieving benefits in this area, one 

participant emphasized the need to prepare citizens and engage 

community participation, such that people value information about 

themselves and their society. Another participant emphasized that such 

ecosystems need to be shown to work and yield replicable results across 

different cities or otherwise distinct environments. Given these types of 

challenges, forum participants identified developing a sustainable 

ecosystem that will allow for reaping the potential benefits of data and 

analytics as a key challenge.
Page 51 GAO-16-659SP Data and Analytics Innovation

Figure 19: Smart Cities and Urban Big Data: Examples of How DAI Facilitates the Achievement of Important Societal Goals, 

and Challenges to Achieving Those Goals
Page 52 GAO-16-659SP Data and Analytics Innovation

Forum participants discussed potential data privacy impacts across five 

areas of concern:

1. Internet-related companies’ collecting, using, and sharing consumer 

data with third parties.44

2. Lack of sufficiently informed consent for consumers, reflecting both 

low consumer awareness and lack of company transparency—as 

illustrated, for example, by a lack of algorithmic transparency.

3. Little or no consumer choice in certain situations.

4. The possible undermining of both personal autonomy and 

constitutional principles that may result from privacy erosion.

5. Cybersecurity issues that have implications for physical security and 

safety.

Participants also discussed the complexity of privacy issues today. 

Throughout, they considered the evolving nature of current technology 

trends and the importance of dealing with relevant issues, going forward.

Of the varied Internet-related companies that are relevant to DAI-related 

privacy concerns, three key categories are: (1) Internet service providers 

(ISP) and wireless carriers (which provide cell service);45 (2) providers of 

operating systems; and (3) makers of applications (or apps), which are 

installed on computers or smartphones. These three kinds of companies 

may collect consumer data including consumers’ locations (based on 

GPS data or other kinds of information on the location of individual 

persons, or their devices, over time), as illustrated in figure 20.

46 Location 

data are deemed potentially sensitive because such data may include, for 

example, a psychiatrist’s office—or the site of a protest rally. Tracking 

locations of multiple individuals over time can also reveal patterns such 

as two or more persons repeatedly going to the same location(s) at the 

same time.

 44As background, no overarching federal privacy law restricts private-sector companies’

collection, sharing, or reselling of consumer data (GAO 2013a).

45Some companies provide both Internet and cell service.

46See also Soltani (2014).

Privacy Concerns

Area of Concern: Internet 

Companies’ Collecting, 

Using, and Sharing Data 

with Third Parties
Page 53 GAO-16-659SP Data and Analytics Innovation

Companies such as ISPs, wireless carriers, and apps-makers—which 

collect consumer data (for example, data on consumer locations)—may 

share that data with other companies, such as advertisers, marketers, or 

third party websites; see Rowinski (2011) and Hope (2015). Certain 

dominant technology companies both (1) collect consumers’ data directly 

through their own websites, services, etc., and (2) access pipelines of 

data flowing to them from other companies, as illustrated earlier (refer 

back to fig. 14).47 The companies that collect the data and the other 

companies with which data are initially shared may, in turn, sell the data 

to data brokers or resellers that amass data from many sources for resale 

(GAO 2013a).

Figure 20: Flows of Location Data

One participant said that in some cases use of an app may result in a 

consumer’s information being shared with as many as 40 or 50 

 47A participant referred to the “Big Five” technology companies: Alphabet (parent 

company of Google), Amazon, Apple, Facebook, and Microsoft. Additional key technology 

companies include LinkedIn, Twitter, and eBay, among others.
Page 54 GAO-16-659SP Data and Analytics Innovation

companies and that neither the consumer—nor in some cases the data 

companies that are directly involved—may know to what extent third 

parties will further share that data with others, now or in future.48

While understanding that consumers are concerned about practices such 

as companies’ sharing data with third parties,49 forum participants viewed 

consumers as unaware of the full scope of many companies’ current data 

practices. For example, participants emphasized that consumers may not 

realize what kinds of data the companies are collecting about them as a 

result of their Internet usage. Three key points with respect to a lack of 

fully informed consent are:

• A typical user encounters many websites and apps, and thus may not 

devote the time needed to read and retain even the basic information 

in all relevant online-privacy policies.

• Companies’ privacy policies are not always written concisely and 

clearly, in part because (1) there is no mandate for clarity in End User 

License Agreements (EULA) and (2) in the words of one participant, 

“The only incentive [for companies] is to...disclaim as much as 

possible” to avoid potential action on the part of the Federal Trade 

Commission with respect to deceptive trade practices.

• Smartphones routinely collect location data in the background, by 

default. Various apps also collect location data, as indicated above, 

sometimes in situations where a consumer might not suspect that 

data collection is taking place. For example, the collection of 

consumers’ location data has, in at least one case, extended to the 

locations of consumers using a phone’s flashlight app.50 Logically, 

consumers might not suspect that their use of a cell phone’s flashlight 

app would result in the collection and sharing of their location data—

 48Some experts see the value of data as enhanced by the potential for currently 

unanticipated future uses. For example, one participant noted the value of the “long tail of 

use” that may be associated with data. 

49In a recent survey, 80 percent of respondents who use social-networking sites said they 

were concerned about their information’s being “accessed by third parties, like advertisers 

or businesses without [the user’s] knowledge.” (Pew Research Center 2014b). 

50See Federal Trade Commission (2013).

Area of Concern: 

‘Informed Consent’ Issues 

Reflecting Low Awareness 

and Lack of Transparency
Page 55 GAO-16-659SP Data and Analytics Innovation

and while the posted privacy policy might disclose this information, the 

consumer might not read or understand that policy.

With respect to issues concerning algorithms, participants said:

• Consumers may not realize that skillful analyses of the data they 

provide might result in additional inferences about them. To illustrate 

this possibility, some researchers are working on ways to use varied 

data on social media to infer political orientation—or to protect against 

inferences of this sort; see Kantarcioglu (2011); Heatherly et al. 

(2009); and Lindamood et al. (2009).

• Algorithmic transparency issues are significant for consumers 

because algorithms increasingly inform risk scores, which may affect, 

for example, employers’ decisions about whom to hire. Algorithms 

also affect other kinds of decisions, such as the decisions that 

financial service companies’ make about consumer loans and 

insurance rates (Olson 2011) and what kinds of advertising will be 

shown to which users (Laxton and Castro 2015).

• Consumers often cannot know how data are used in decisions about 

them because algorithms may be opaque—that is, their inner 

workings may be hidden. To illustrate the potential for hidden inner 

workings, one participant cited the distortion of emissions 

measurements in automobiles manufactured by a major company, 

which was discovered by chance. Another participant cited a Supreme 

Court decision51 saying that it supports limiting access to code (which 

would include code defining algorithms that could result in 

discrimination). This participant explained that the Supreme Court 

decision changed the ability to file discrimination lawsuits in court. He 

said that now the party alleging discrimination will need to show 

concrete details proving discrimination. But when discrimination is 

based on hidden codes or algorithms with hidden inner workings, this 

creates a catch-22 because the party needs the code to get into court, 

but cannot get the code without going to court.

• Importantly, there is a potential for unintentional discriminatory actions 

by companies using algorithms. Studies show that search engines 

using algorithms or data that reflect pre-existing stereotypes may yield 

 51See Ashcroft v. Iqbal, 556 U.S. 662 (2009).
Page 56 GAO-16-659SP Data and Analytics Innovation

results that reinforce or exaggerate societal biases (see Kay et al. 

2015; Sweeney 2013). Algorithmic discrimination may extend to hiring 

and other important decisions, such as lending, housing, and 

university admissions (Executive Office of the President 2016, citing 

Flatow 2015, among others), and Microsoft’s CEO was recently 

quoted as calling for “algorithmic accountability [to] . . . undo 

unintended harm” (Simonite 2016, quoting Satya Nadella).

One participant emphasized that lack of consumer awareness extends to 

cybersecurity risks. He said that most users likely would be unaware that 

some devices contain sensors that (1) are hidden and not activated, but 

(2) are designed to record—if activated—information apparently unrelated 

to the ostensible purpose of the device. Consequently, a hacker could 

activate such sensors and pick up more information about users than the 

normal operation of the device would generate. This participant described 

an instance where white hat hackers52 accessed a currently popular incar navigation system—and were able to listen in on personal (nonphone, non-navigation) conversations between drivers and passengers.53

In this instance, the car’s built-in microphones had the capability of 

picking up in-car conversations, although manufacturers had not activated 

the system to use the microphones in that way. The participant’s point 

was that consumers might have no knowledge of the potential risks 

associated with a particular IoT device.

Lack of consumer awareness may extend to security risks involving 

physical harm and, in some cases, death, for example, unauthorized 

interference with medical devices such as connected pacemakers. 

Logically, the issue of awareness of such risks could become more 

important with increasing adoption of connected sensors, ranging from 

personal devices to national or international systems.

Although consumers may accept exchanging their privacy for free Internet 

services, the transparency and awareness issues discussed above 

logically rule out the possibility of fully informed “risk-managed decisions 

about usage.” As summed up at the forum, there is a growing disparity 

between:

 52White-hat hackers are ethical experts who notify organizations of vulnerabilities, allowing 

them to make improvements (Hoffman 2013). 

53The drivers and passengers in this study were volunteer research subjects.
Page 57 GAO-16-659SP Data and Analytics Innovation

• the large amounts of knowledge or information about consumers that 

private-sector data collectors/controllers now possess, and

• the limited knowledge that consumers (or data subjects) have on (1) 

what information is being collected about them, (2) which companies 

have received that information or will receive it in the future through 

company-to-company sharing, and (3) what kinds of uses or analyses 

currently involve consumers’ data, or might in the future.

Participants discussed situations in which consumers now have—or in 

future would likely have—little or no ability to make choices about data 

privacy.

One issue is that some apps, devices, or services may require consumer 

acceptance of stated privacy policies as a condition of use. A participant 

pointed out (1) that certain infrastructure or service items are needed by 

people or families to carry out their activities of daily life and (2) that these 

necessary items currently involve Internet connectivity or likely will in 

future. Examples include telephone or cell phone service and Internet 

connected utilities in the home (such as electricity, heat, and water). This 

participant said that unless people can opt out of tracking or data 

collection when accessing these kinds of services, they would logically 

have no choice regarding the collection of data about themselves.54

Other limitations on choice pertain to data collected in public places. 

Participants said that:

• Public-place data collection by companies—such as marketers’ videorecording consumers near shops or malls, Wi-Fi detection of a deviceuser’s location, and so forth—is increasing and may not be easy for 

individuals to avoid.

• An individual’s privacy might be infringed upon by other consumers’

use of data-collecting devices. For example, a person might snap a 

photograph of friends—and other diners— in a restaurant and post it 

on the web. Privacy interference of this sort could increase in the 

future if, for example, many individuals routinely video-record others 

 54This participant distinguished the lack of choice involving essential devices and services 

from non-essential devices and services such as social media.

Area of Concern: 

Situations with Little or No 

Consumer Choice about 

Privacy
Page 58 GAO-16-659SP Data and Analytics Innovation

while wearing cameras. As one participant said: “I can imagine a 

future world . . . where my choices affect your privacy.”55

In some cases, these kinds of data collection (which raise issues about 

data subjects’ lack of choice) might occur without data subjects being 

aware that companies or other individuals are collecting data about them.

These limitations may become more important, going forward, as both 

companies and consumers increasingly adopt new IoT devices (1) that 

have a variety of sensors (cameras, microphones, temperature sensors, 

motion sensors, and so forth) that collect data to be transmitted over the 

Internet, and (2) that are designed for use in multiple locations—public 

places, cars, and homes.

Forum participants discussed personal autonomy and constitutional rights 

as fundamental concepts that can inform consideration of data privacy 

issues today.

Participants considered the concept of personal autonomy as a 

component of personhood. Logically, the collection of information about 

persons without their knowledge or against their will has the effect of 

overruling or undermining personal autonomy. One participant 

emphasized two points:

• Autonomy is undermined if the individual is prevented from making 

decisions for himself or herself about, for example, (1) whether to 

share certain information about himself or herself or not share it or (2) 

whether to make statements to others about certain matters or refrain 

from doing so.

• Systemic invasions of privacy systematically undermine autonomy of 

decision or the ability to make a choice.

Other participants emphasized constitutional rights, saying for example, 

that:

 55Another way that “my choices affect your privacy” is that an individual’s decision to learn 

and make public information about his or her DNA can affect family members and others.

Area of Concern: Personal 

Autonomy and 

Constitutional Rights
Page 59 GAO-16-659SP Data and Analytics Innovation

• Citizens of the United States have certain rights, including a 

reasonable expectation of privacy, based on the Constitution and the 

Bill of Rights, as interpreted by the Supreme Court. (As one 

participant said, going forward, as technology evolves, the Supreme 

Court “is there to make sure that a principles document about who we 

are as a people is observed.”56)

• The Bill of Rights is interpreted as including an individual’s right to 

privacy vis-à-vis private-sector entities (particularly, an individual’s 

right to choose or control what information is gathered or released 

about him or her), based on the landmark Harvard Law Review article 

entitled “The Right to Privacy” (Warren and Brandeis 1890).

The 1890 article was written at a time when (1) cameras were a new 

technology and (2) newspapers had begun to print photographs that were 

surreptitiously taken. In arguing against photographing people without 

their knowledge, Warren and Brandeis, in effect, argued for the 

autonomy—or the ability to choose—of those who might (absent legal 

protections) be photographed against their will.

Reductions in the cost of tracking or surveilling individuals (which were 

noted in the pre-forum Reading Package) are illustrated in figure 21. 

Various levels of government have surveilled individuals, including 

activists considered to be potential law violators. In recent years, civil 

libertarians have raised issues concerning possible surveillance of 

individuals at both ends of the political spectrum—in a manner not unlike 

the historical surveillance of leaders of minority movements and related 

activists during the Civil Rights era.

57 If current reductions in the cost of 

surveillance were to result in increased levels of surveillance, some (for 

example, Bankston and Soltani 2014) would see this sort of development 

 56Judicial interpretations have a role in shaping how the right to privacy is defined as 

technology evolves. In Riley v. California (2014), the Supreme Court ruled that once a 

person is arrested by the police, a warrant is required to search that person’s cell phone, 

based on the Fourth Amendment, which broadly mandates that the people “be secure in 

their persons, houses, papers, and effects” 134 S. Ct. 2473 (2014). On May 31, 2016, the 

4th Circuit ruled that the Government is not required to obtain a search warrant to obtain 

historical cell-site location information from a cell phone provider; see United States v. 

Graham, 2016 824 F.3d 421.

57A forum participant noted that government surveillance targeted certain racial and ethnic 

minorities and leaders of these groups, such as Martin Luther King, Jr., and Cesar Chavez 

during the Civil Rights era. See also M. Smith et al. (2016).
Page 60 GAO-16-659SP Data and Analytics Innovation

as impinging on a reasonable expectation of privacy with respect to 

government surveillance.

Figure 21: Cost of Location Tracking by Method

Notes: Used by permission of Yale Law Journal. Bankston and Soltani 2014, 

http://www.yalelawjournal.org/forum/tiny-constables-and-the-cost-of-surveillance-making-cents-out-ofunited-states-v-jones.

a

The most expensive per-hour cost for each method is shown. A cell phone signal can be detected 

not only by a cell tower but also by an International Mobile Subscriber Identity (IMSI) Catcher or 

“Stingray” that simulates a cell tower in order to collect information about the location of nearby cell 

phones independently of the cell service provider.

Although privacy can be violated whenever a security breach occurs, a 

further concern is that some connected devices and systems now include 

action-taking machines, such as

• automated brakes on a vehicle, or

• automatic medical devices (for example, an insulin pump).

Relevant systems pertain to areas such as medical devices, 

transportation systems, and many other instances—for example, a smart 

grid.58 The expanding connectedness of devices and systems means that 

security increasingly involves much more than information privacy issues: 

In some cases, cybersecurity outcomes may involve the life and death of 

patients, the preservation of critical national infrastructures, and the 

continuity of government systems.

In light of the importance of security for cyber-physical systems, the 

National Institute of Standards and Technology (NIST) is helping develop 

 58A smart grid is a computerized electricity distribution system that utilizes sensors and 

two-way communications.

Area of Concern: 

Cybersecurity and ActionTaking Machines
Page 61 GAO-16-659SP Data and Analytics Innovation

appropriate standards and frameworks in this area.59 Additionally, GAO 

(2016) has emphasized that efforts to assure security for cyber-physical 

systems should consider the concept of domain separation. Domain 

separation efforts would include:

• separating non-physical systems (such as automobile infotainment 

services) from physical systems (such as automobile safety controls 

with cyber aspects), and

• providing joint systems with special protections, such as “firewalls” or 

secure gateways at key connection points, to keep physical systems 

safe from cyberattacks.

Looking ahead, cybersecurity experts have highlighted the continuing—

and potentially increasing—challenge of keeping abreast of ongoing 

developments and investment in new connected technologies.

Several forum participants viewed privacy issues as complex and 

involving key distinctions. They said:

• Different levels of sensitivity are associated with different kinds of 

data. Very sensitive data were described as including, for example: 

(1) location data;60 (2) the content of e-mail messages (especially 

those between intimates or family members); (3) patient 

communications with medical professionals (especially those that 

concern socially sensitive diseases or conditions); and (4) data that 

may be collected by home appliances or wearables connected to the 

IoT, including data from microphones, cameras, or other sensors—

which may become more common in future.

 59Two key goals involve working toward (1) “trustworthy secure” systems that focus on 

multiple considerations—for example, privacy, safety, and security—across 16 areas of 

critical infrastructure, such as health care, manufacturing, transportation, and the electric 

grid among many others, and (2) “layered security architecture” to protect the central 

system from attacks originating in one or more linked components, such as smart meters 

in an electric grid (Cyber Physical Systems Public Working Group 2016).

60One participant cited a map of neighborhoods rated on the likelihood of residents’

having engaged in “one night stands.” This was based on location data—specifically, data 

collected from a ride-sharing company concerning rides to and from specific locations at 

certain hours.

The Complexity of Privacy 

Issues
Page 62 GAO-16-659SP Data and Analytics Innovation

• There are instances in which society’s claim to knowledge based on 

personal data would outweigh an individual’s privacy interests. For 

example: (1) numerous states have established drug prescriptionmonitoring programs to facilitate the identification of persons addicted 

to prescription drugs, so that they can be treated (U.S. Department of 

Justice 2011) and (2) vehicle-to-vehicle communication technologies 

(known as V2V) are being designed to maximize highway safety and 

eventually could save thousands of lives on U.S. roads every year.

61

One participant emphasized the distinction between such instances 

and others in which personal privacy would be surrendered solely for 

the benefit of commercial applications that bring few public benefits; 

he quoted Ohm (2013), saying: “We should distinguish between 

research that benefits the public and that which serves only narrow 

and private gain.” Another participant said that government may need 

to know certain information based on its responsibility to provide 

societal protections from, for example, unsafe vehicle operation.

• Privacy erosion or loss occurs at different levels. For example, one 

level of privacy loss would pertain to situations where an individual’s 

information (1) had previously been somewhat difficult to access but 

was publicly available and (2) has now become easily accessible 

online. A different level would pertain to information that had not been 

publicly available previously but has now been made public.

• Privacy issues raised by the advent of new technology may subside 

with time. One participant (citing Castro and McQuinn 2015) said that 

new technology can bring an overemphasis on privacy concerns, 

which typically subside in time, at least to some degree.

An additional key point raised is that it is important to preserve a free flow 

of information.

An area of disagreement occurred with respect to what might be termed 

an “outcomes only” or “outcomes mainly” approach to addressing privacy 

concerns. One participant said that the focus of efforts to enhance privacy 

should be on addressing harmful outcomes—a view that has been stated 

elsewhere, together with suggestions for little or no limitation on data 

collection (President’s Council of Advisors on Science and Technology 

 61However, in the case of V2V, designers of the system are attempting to both improve 

safety benefits and preserve privacy. 
Page 63 GAO-16-659SP Data and Analytics Innovation

2014).62 Other participants argued that putting the primary focus on 

outcomes would be

• inconsistent with the fundamental privacy concepts discussed above 

(including personal autonomy and the rights granted by the U.S. 

Constitution);

• not sufficient to deal with privacy issues concerning emerging IoT 

devices for the home and the car, because such devices may collect 

more sensitive and intrusive data than are being collected by today’s 

technology and because, in some cases, IoT devices may include 

hidden sensors (of which consumers would likely be unaware); and

• not sufficient given that cybersecurity threats apply to all areas in 

which data are collected.

A further aspect of complexity concerns potential responses to privacy 

issues: no single federal agency is responsible for the full range of 

potential consumer protections in the area of Internet privacy and 

security. Rather different agencies are contributing to the ongoing 

evolution of efforts to better understand and grapple with privacy issues 

(see app. VII).

Overall, the forum discussions outlined above appeared to enrich 

understanding of the scope and importance of privacy concerns and 

issues in a way that might contribute to varied ongoing efforts to address 

the complex range of data privacy and security issues facing the nation 

now and in the coming years. Although forum discussions did not result in 

clear, agreed-upon pathways forward in terms of addressing privacy 

concerns, participants did offer some suggestions, which are included in 

the following section on Considerations Going Forward.

 62See also Castro (2014).
Page 64 GAO-16-659SP Data and Analytics Innovation

As detailed above, participants in the January 2016 forum said that data 

and analytics innovation (DAI) will bring

• far-reaching economic opportunities, although key challenges or 

hurdles must be addressed;

• changes in types of jobs and required skills and potentially, job 

losses—suggesting the need for more advanced education and/or retraining;

• effective pathways for achieving numerous societal goals, ranging 

from more efficient and effective health care to safer and less 

congested highway transportation; and

• increasing concerns about privacy that—unless addressed—could 

intensify with the more intrusive data collections that are anticipated.

Participants also said that DAI benefits would ideally apply to all sectors 

of society.

As identified by participants, near-term activities aimed at laying the 

groundwork for realizing economic opportunities, achieving societal 

benefits, and addressing unintended consequences would work to

• increase awareness about and understanding of DAI developments 

across a range of groups, including policymakers, government 

personnel, the general public, and others;

• foster the DAI process by developing supportive mechanisms and 

resources;

• proactively prepare the workforce of the future; and

• explore socially responsible approaches to privacy concerns and 

challenges.

Specifically, participants highlighted:

• Raising and enhancing awareness of DAI among (1) policymakers 

who could articulate and disseminate a vision for data and analytics, 

and create new national initiatives to help realize new economic 

opportunities and use new pathways to benefit society; (2) 

government officials who determine specific policies and set priorities 

within agencies—and staff who carry out policies; (3) the general 

public who make decisions about what levels and types of education 

or training they or their children will pursue; and (4) businesses—from 

small and medium-sized enterprises to large legacy firms that in some 

cases have yet to realize the potential and value of these new 

Considerations Going 

Forward
Page 65 GAO-16-659SP Data and Analytics Innovation

methods. Efforts to raise or enhance awareness might be directed to, 

for example, the extent and nature of economic and societal 

opportunities; the lack of transparency in data policies and algorithmic 

decision making; the issue of equity (in terms of all groups of 

individuals and businesses being able to participate in and/or reap the 

benefits of data and analytics); cybersecurity vulnerabilities (including 

vulnerabilities that may occur in cyber-physical systems); and the 

need for quality data. Because of current and anticipated rates of 

change in these areas, such efforts should be monitored and possibly 

re-envisioned on a regular basis.

• Developing mechanisms and resources to support DAI, which 

would involve bringing together the hardware, software, management 

mechanisms, and other resources necessary to build the 

infrastructure for collecting, integrating, and analyzing data. Open 

data efforts represent a key resource in such frameworks or 

ecosystems, and encouraging the development of relevant 

ecosystems that include data released by the government will help 

maximize data- and analytics-driven innovation. Increased access to 

varied private-sector data might also be key to a dynamic ecosystem. 

Although participants pointed to the current domination of some 

private-sector data flows by a few large entities (refer back to fig. 14), 

some suggested that a new marketplace for businesses’ sharing or 

trading data might provide greater market equity and increase DAI 

opportunities for start-ups and small and medium-sized enterprises. 

Other possible activities to enhance private-sector ecosystems might 

include developing online or other community networks to bring 

together different kinds of firms or individuals for sharing or trading 

ideas and resources. Additionally, governments purchasing goods 

and services (in accordance with existing needs) might consider 

including early-adopter choices, thus encouraging consumer and 

business adoption of relevant technologies.

• Minimizing negative impacts on jobs through (1) re-designing 

school curriculums, training and retraining courses, and university 

course offerings to focus on future job needs and opportunities—by 

emphasizing STEM and fostering the creativity needed for innovation 

through adding the arts to STEM training (and thus creating what has 

been termed STEAM63); (2) prioritizing an increase in U.S. high 

 63See GAO (2015b).
Page 66 GAO-16-659SP Data and Analytics Innovation

school and college graduation rates—to be on a par with other leading 

OECD countries; (3) encouraging diversity with the goal of eliciting the 

full range of talent and potential contributors; and (4) increasing U.S. 

GDP growth (and potentially job opportunities) by taking advantage of 

current U.S. global leadership in Internet services and technology 

areas that support data activities (for example, facilities for hosting, 

storing, and processing data).

• Exploring risk-managed approaches to privacy issues through 

encouraging (1) research to develop privacy-enhancing technologies; 

(2) some private-sector companies’ efforts to compete based on 

offering consumers better privacy deals; and (3) approaches to 

government regulation that provide a better privacy deal for 

consumers—but avoid “throwing out the baby with the bath water.”

Another idea suggested by forum participants involved creation of a 

data marketplace that would establish property rights and enable 

consumers to sell their data to varied service providers (including, for 

example, device systems, apps, and websites), although this might 

advantage or disadvantage different groups of consumers based on 

their economic situations. Logically, each of these areas of possible 

exploration and development—and possibly other approaches to 

enhancing consumer privacy—could recognize the complexity of data 

privacy issues, distinguishing between, for example, privacy 

approaches or requirements for the products and services that each 

person or family in today’s society needs (such as a usable phone or 

in-home utilities) versus other, more optional products and services. 

These varied approaches could also be seen as representing 

complementary pieces of a larger effort to work toward a broader, 

overall privacy strategy appropriate to the twenty-first century.
Appendix I: Forum Agenda

Page 67 GAO-16-659SP Data and Analytics Innovation

Day One: Wednesday, January 27, 2016

ARRIVALS, CHECK-IN

OPENING SESSION 

Welcome

The Honorable Gene L. Dodaro, Comptroller General of the United States

(10 minutes)

Overview

Timothy M. Persons, Chief Scientist, GAO (15 minutes)

Significance to the Nation

Andrew Wyckoff, Director for Science, Technology and Innovation, OECD 

(10 minutes)

REVIEW OF IoT PRIMER AND THREE PROFILES

Timothy M. Persons (5 minutes)

• The Internet of Things—Discussant: Ashkan Soltani, Sokwoo Rhee (7 

minutes each/15 minutes total)

• Financial Markets —Discussants: Linda Powell, Pete Kyle (7 minutes 

each/15 minutes total)

• Health Care—Discussants: Martin Sepulveda, Philip Bourne (7 

minutes each/15 minutes total)

• Intelligent Transportation Systems—Discussants: Nat Beuse, Steve 

Shladover (7 minutes each/15 minutes total)

FORUM DISCUSSION (ALL)

BREAK

Appendix I: Forum Agenda

12:30 p.m.

1:00 p.m.

1:45 p.m.

2:45 p.m.

3:30 p.m.
Appendix I: Forum Agenda

Page 68 GAO-16-659SP Data and Analytics Innovation

FUTURE PROMISE, BENEFITS

How and why does Big Data promise to bring benefits, such as economic 

prosperity and positive social impacts, across many sectors?

Piyushimita (Vonu) Thakuriah (10 minutes)

Discussants: Eric Andrejko, Dan Gordon (7 minutes each, 15 minutes 

total)

FORUM DISCUSSION (ALL)

RECEPTION 

Day Two: Thursday, January 28, 2016

Continental breakfast: 

WELCOME BACK 

(Tim Persons 5 to 15 minutes)

CHALLENGES: HURDLES

What potential hurdles must be overcome to maximize benefits?

Shyam Sunder (10 minutes)

Discussants: Dan Castro, Erik Andrejko (7 minutes each, 15 minutes 

total)

FORUM DISCUSSION (ALL) 

BREAK 

3:45 p.m.

4:10 p.m.

5:15 p.m.

7:45 a.m.

8:15 a.m. 

8:30 a.m.

8:55 a.m.

9:55 a.m.
Appendix I: Forum Agenda

Page 69 GAO-16-659SP Data and Analytics Innovation

CHALLENGES: DRAWBACKS 

What is the full scope of potential drawbacks that should be recognized?

Alvaro Bedoya (10 minutes)

Discussants: Ashkan Soltani, Michael Lynch, Yoshi Kohno, Oliver Richard 

(7 minutes each, 30 minutes, total)

FORUM DISCUSSION (ALL) 

BOX LUNCH

REPORTS FROM THE FIELD: Riley Newman, Andrew Cromarty, Matt 

Gardner

MAXIMIZING BENEFITS, ADDRESSING CHALLENGES 

What are options for the way forward—addressing both hurdles and 

drawbacks?

The federal role—what options seem best? John Verdi (8 minutes)

Public opinion—why does this matter? Solomon Messing (8 minutes)

The private sector—can it address both hurdles and drawbacks? Dan 

Castro (8 minutes)

Technology, vision, and ideas—how might new approaches help? Ashkan 

Soltani (8 minutes)

Overall strategy—how might this be defined or developed? Andrew 

Wyckoff (8 minutes)

FORUM DISCUSSION (ALL) 

BREAK 

SUMMING UP THE BIG DATA PHENOMENON FOR POLICY MAKERS 

How might we best convey the nature, import, and challenges of the Big 

Data phenomenon to policy makers?

Simon Whitfield, Andrew Cromarty, Philip Bourne (7-8 minutes each, total 

30 minutes)

10:10 a.m.

10:50 a.m.

11:50 a.m.

12:50 p.m.

1:30 p.m.

2:30 p.m.

2:45 p.m.
Appendix I: Forum Agenda

Page 70 GAO-16-659SP Data and Analytics Innovation

FORUM DISCUSSION (ALL) 

WRAP-UP 

3:15 p.m.

4:15 PM
Appendix II: List of Forum Participants

Page 71 GAO-16-659SP Data and Analytics Innovation

Gene L. Dodaro, Comptroller General of the United States

Erik Andrejko, Vice President, Science, Head of Data Science, Climate 

Corporation, San Francisco, CA.

Alvaro Bedoya, Executive Director, Georgetown Law Center on Privacy & 

Technology, Washington, D.C.

Nathaniel Beuse, Associate Administrator for Vehicle Safety Research, 

National Highway Traffic Safety Administration (NHTSA), Washington, 

DC.

Philip Bourne, Associate Director for Data Science, National Institutes of 

Health, Bethesda, MD.

Daniel Castro, Director of the Center for Data Innovation, Vice President 

of the Information Technology and Innovation Foundation, 

Washington, DC.

Andrew Cromarty, President and Chief Executive Officer, Distributed 

Systems Technology LLC, San Francisco, CA.

Matthew Gardner, Head of Experimentation Analytics, eBay, San Jose, 

CA.

Dan Gordon, Technology Partner, Valhalla Partners, Vienna, VA.

Tadayoshi Kohno, Short-Dooley Professor, Department of Computer 

Science & Engineering; Adjunct Associate Professor, Information 

School, University of Washington, Seattle, WA.

Albert (Pete) Kyle, Charles E. Smith Professor, Department of Finance, 

Robert H. Smith School of Business, University of Maryland, College 

Park, MD.

Michael Lynch, Professor, Department of Philosophy, University of 

Connecticut, Storrs, CT.

Solomon Messing, Director of Data Labs, Pew Research Center, 

Washington, DC.

Riley Newman, Director, Data Science, Airbnb, San Francisco, CA.

Appendix II: List of Forum Participants

Host

Forum Participants
Appendix II: List of Forum Participants

Page 72 GAO-16-659SP Data and Analytics Innovation

Timothy M. Persons, Chief Scientist, U.S. Government Accountability 

Office, Washington, DC.

Linda Powell, Chief Data Officer, Consumer Financial Protection Bureau, 

Washington, DC.

Sokwoo Rhee, Associate Director, Cyber-Physical Systems Program, 

National Institute of Standards and Technology, Gaithersburg, MD.

Oliver Richard, Director, Center for Economics, U.S. Government

Accountability Office, Washington, DC.

Martín J. Sepúlveda, IBM Fellow, Member of the National Academy of 

Medicine, Washington, DC.

Steve Shladover, California PATH Program Manager, University of 

California, Berkeley, Richmond Field Station, Richmond, CA.

Ashkan Soltani, Independent Researcher and Former Chief Technologist, 

Federal Trade Commission, Washington, DC.

S. Shyam Sunder, Senior Science Advisor to the Deputy Secretary, 

Department of Commerce, Washington, DC.

Piyushimita (Vonu) Thakuriah, Halcrow Chair of Transportation, Director, 

Urban Big Data Centre, University of Glasgow, Glasgow, United 

Kingdom.

John Verdi, Director of Privacy Initiatives, National Telecommunications 

and Information Administration, Department of Commerce, 

Washington, DC.

Liam (Simon) Whitfield, Deputy Director, Data Analytics, Horizon 

Scanning and Project Development, UK Government Office for 

Science, London, United Kingdom.

Andrew Wyckoff, Director, Directorate for Science, Technology and 

Innovation, Organisation for Economic Co-operation and Development, 

Paris, France.
Appendix III: List of Other Experts Consulted

Page 73 GAO-16-659SP Data and Analytics Innovation

This list includes experts, interviewed in preparation for, or following, the 

forum, as well as experts who reviewed the draft report. For the list of 

experts who participated in the CG Forum, see appendix II.

Claire Barrett, Chief Privacy Officer, U.S. Department of Transportation, 

Washington, DC.

Tom Batz, Deputy Executive Director, TRANSCOM, Jersey City, NJ.

Marjory Blumenthal, Executive Director, President’s Council of Advisors 

on Science and Technology (PCAST), White House Office of Science 

and Technology Policy, Washington, DC.

Jamie Boone, Director, Government Affairs, Consumer Electronics 

Association, Arlington, VA.

Alan Butler, Appellate Advocacy Counsel, Electronic Privacy Information 

Center, Washington, DC.

David Bray, Assistant Chief Financial Officer, Federal Communications 

Commission, Washington, DC.

Milton Brown, Deputy Chief Counsel, National Telecommunications and 

Information Administration, U.S. Department of Commerce, 

Washington, DC.

Wo Chang, Information Access Division, Information Technology 

Laboratory, National Institute of Standards and Technology, 

Gaithersburg, MD.

Kathryn Dall’Asta, Associate Counsel, General Counsel, CTIA-The 

Wireless Association, Washington, DC.

Karen DeSalvo, Acting Assistant Secretary for Health, U.S. Department of 

Health and Human Services, Washington, DC.

Nicholas G. Evans, Postdoctoral Fellow in Advanced Biomedical Ethics, 

University of Pennsylvania, Philadelphia, PA.

Paul Feenstra, Senior Vice President, Government and External Affairs, 

Intelligent Transportation Society of America (ITS America), 

Washington, DC.

Appendix III: List of Other Experts Consulted
Appendix III: List of Other Experts Consulted

Page 74 GAO-16-659SP Data and Analytics Innovation

Cristian Jara Figueroa, Research Assistant, Micro Connections Lab, MIT 

Media Lab, Massachusetts Institute of Technology, Cambridge, MA.

Mark Flood, Senior Policy Advisor, Office of Financial Research, U.S. 

Department of Treasury, Washington, DC.

Robert Groves, Provost, Georgetown University, Washington, DC.

Natalie Evans Harris, Senior Policy Advisor, Data-Driven Government,

Office of Science and Technology Policy (OSTP), Executive Office of 

the President, The White House, Washington, DC.

Lisa M. Harrison, Assistant General Counsel for Legal Counsel, U.S. 

Federal Trade Commission, Washington, D.C.

Randall Iwasaki, Executive Director, Contra Costa Transportation 

Authority/GoMentum Station, Walnut Creek, CA.

Ian Kalin, Chief Data Officer, U.S. Department of Commerce, 

Washington, DC.

Jeffrey D. Keesing, Strategic Initiative Manager, Memorial Sloan-Kettering 

Cancer Center, New York, NY. 

Richard Kowalksi, Manager, Industry & Intelligence, Consumer 

Electronics Association, Arlington, VA.

Maciej Kranz, Vice President of Corporate Technology Group, Cisco, San 

Jose, CA.

Naomi Lefkovitz, Chief Cybersecurity Advisor, Information Technology 

Laboratory, National Institute of Standards and Technology, 

Gaithersburg, MD.

Donald Light, Director, North American Property/Casualty Practice, 

Celent, Inc., New York, NY.

Michael Marsico, Assistant Commissioner, New York City Department of 

Transportation, New York, NY.

Debbie Matties, Vice President, Privacy, CTIA-The Wireless Association, 

Washington, DC.
Appendix III: List of Other Experts Consulted

Page 75 GAO-16-659SP Data and Analytics Innovation

Jackie McCarthy, Director of Wireless Internet Development, CTIA-The 

Wireless Association, Washington, DC.

Daniel Morgan, Chief Data Officer, U.S. Department of Transportation, 

Washington, DC.

John Morris, Jr., Associate Administrator, National Telecommunications 

and Information Administration, Office of Policy Analysis and 

Development, U.S. Department of Commerce, Washington, DC.

Jim O’Neill, Senior Analyst, Banking, Celent, Inc., New York, NY.

Munnesh Patel, Manager, Transportation Data, New York Metropolitan 

Transportation Council, New York, NY.

D.J. Patil, Chief Data Scientist, Office of Science and Technology 

(OSTP), Executive Office of the President, The White House, 

Washington, DC.

Alexander “Sandy” Pentland, Faculty Director, Connections and Social 

Engineering, MIT Media Lab, Massachusetts Institute of Technology, 

Cambridge, MA.

Miguel Perez, Director, Center for Data Reduction and Analysis Support, 

Virginia Tech Transportation Institute, Virginia Tech University, 

Blacksburg, VA.

Maureen Pirog, Rudy Professor of Policy Analysis, School of Public and 

Environmental Affairs, Indiana University, Bloomington, IN.

Randi Polanich, Director of Global Communications, GE Digital, San 

Ramon, CA.

Alan Porter, Professor Emeritus, School of Industrial & Systems 

Engineering, and Public Policy, and Co-Director of the Technology 

Policy and Assessment Center, Georgia Institute of Technology, 

Atlanta, GA.

George Poste, Chief Scientist, Complex Adaptive Systems, Regents’

Professor and Del E. Webb Chair in Health Innovation, Arizona State 

University, Scottsdale, AZ.
Appendix III: List of Other Experts Consulted

Page 76 GAO-16-659SP Data and Analytics Innovation

Ronald Repasi, Deputy Chief, Office of Engineering and Technology, 

Federal Communications Commission, Washington, DC.

Jessica Rich, Director, Bureau of Consumer Protection, Federal Trade 

Commission, Washington, DC.

Maria Roat, Chief Technology Officer, U.S. Department of Transportation, 

Washington, DC.

Marc Rotenberg, President and Executive Director, Electronic Privacy 

Information Center, Washington, DC.

Deb Roy, Social Machines Lab, MIT Media Lab, Massachusetts Institute 

of Technology, Cambridge, MA.

William Ruh, Chief Executive Officer of GE Digital and Chief Digital 

Officer of General Electric, San Ramon, CA.

Matt Scholl, Chief, Computer Security Division, Information Technology 

Laboratory, National Institute of Standards and Technology, 

Gaithersburg, MA.

Robert Schoshinski, Assistant Director, Division of Privacy and Identity 

Protection, Federal Trade Commission, Washington, DC.

Robert Schukai, Head, Advanced Product Innovation, Thomson Reuters, 

New York, NY.

Monali Shah, Director, Global Intelligent Transportation Solutions, HERE 

Connected Driving, Nokia-HERE, Chicago, IL.1

David Shrier, Managing Director, Connection Science & Engineering, MIT 

Media Lab, Massachusetts Institute of Technology, Cambridge, MA.

Christopher Soghian, Principal Technologist and Senior Policy Analyst, 

American Civil Liberties Union, Washington, DC.

 1 HERE is no longer part of Nokia, as Nokia sold it to a consortium of automotive 

companies in 2015.
Appendix III: List of Other Experts Consulted

Page 77 GAO-16-659SP Data and Analytics Innovation

Jim St. Pierre, Deputy Director, Information Technology Laboratory, 

National Institute of Standards and Technology, Gaithersburg, 

Maryland.

Latanya Sweeney, Professor of Government and Technology in 

Residence, Harvard University, Cambridge, MA.

Jason Thomas, Chief, Innovation, Thomson Reuters Special Services, 

Thomson Reuters, New York, NY.

David Wollman, Deputy Director, Cyber-Physical Systems, National 

Institute of Standards and Technology, Gaithersburg, MD.

Jan Youtie, Principal Research Associate, Enterprise Innovation Institute, 

Georgia Institute of Technology, Atlanta, GA.
Appendix IV: A Brief Primer on the Internet of 

Things

Page 78 GAO-16-659SP Data and Analytics Innovation

This appendix reproduces, verbatim, “A Brief Primer on the Internet of 

Things,” which is excerpted from the Reading Package that we e-mailed 

to participants in advance of the forum, which was held January 27-28, 

2016, at GAO Headquarters, Washington, D.C. References cited in this 

excerpt are included in the List of References (app. VIII).

Appendix IV: A Brief Primer on the Internet of 

Things
A Brief Primer on the Internet of Things (IoT)

The definition of the Internet of Things (IoT) varies but generally can be defined as the universe of objects that can 

gather or transmit data, across networks such as the Internet.1 IoT devices are increasingly common, as items such 

as security cameras, home appliances, and medical devices are adapted to transmit information for monitoring or 

data logging. Other items, such as fitness trackers or cell-phone-controlled lightbulbs, can be readily integrated into 

the IoT universe by using Internet programs such as IFTTT (If This Then That). For example, fitness trackers may 

send data on heart rate or GPS location via cellphone and computer connections to the Internet for long-term 

storage (see fig. 22). Given the low costs associated with integrating devices with computers, potentially any device 

that measures characteristics or controllable performance (motion, light, etc.), process, or data can eventually be 

part of the networked universe, resulting in what some reports refer to as the Internet of Everything.2

 

Figure 22: A Fitness Tracker as an IoT Device 

A key benefit of IoT devices is their ability to generate many 

different kinds of data that can be used in many ways. To 

illustrate this with a consumer example, users of IoT devices 

might: 

(1) retain data to monitor usage of resources over time (e.g., 

to assess levels of power consumption, to help manage utility 

bills); 

(2) report data that can be used to extract patterns and inform 

decisions, such as identifying how much more to exercise, 

based on historical data gathered from fitness trackers; and 

(3) actively use data in real time, monitoring homes with 

Internet-enabled cameras—such as during a vacation. 

Focusing on situations where the primary user is the consumer, three potential IoT device categories are:

• Personal devices and wearables, which include activity or fitness trackers, as illustrated in figure 22: In addition 

to tracking levels of exercise, these devices may monitor basic physiological functions such as heart rate and 

breathing rate. When a user receives feedback on his/her own number of steps—combined with comparisons to 

others—this comparison may result in the users’ decisions to increase their own exercise efforts. Although 

commonly worn on the wrist, fitness-tracking devices may take other forms, such as connected clothing. Less 

related to health are devices to enable data access, such as watches and glasses that link to a cellphone to 

permit notifications of incoming texts or to display videos or other data for convenience.

• Connected vehicle devices or systems, which may involve Internet access and may transmit vehicle operational 

data—such as speed and braking status: Various aspects and implications of connected vehicles are discussed 

in the Profile 3: Intelligent Transportation Systems and Connected Driving in the 21st Century: Areas of 

Development, included later in this Reading Package. Additionally, consumer drones are considered IoT 

devices. Drones—some of which may have cameras and microphones—can fly or drive into areas that would 

 

1

The Internet consists of a network of computers that typically exchange data, such as text and pictures found on websites. According 

to experts, whether computers and smartphones are considered part of the IoT depends on factors such as whether these are used in 

isolation for word processing or taking pictures (not considered part of the IoT) or used to gather and transmit data, e.g., used to collect 

satellite data to control a smart vehicle (considered part of the IoT).

2

Theoretically, any object can be integrated into the IoT with inexpensive electronic components, such as sensors, and network access. 

Several do-it-yourself guides detail how to convert common items into IoT-capable objects.

Appendix IV: A Brief Primer on the Internet of Things Page 79 GAO-16-659SP Data and Analytics Innovation
otherwise be inaccessible (e.g., by flying over fences)—and transmit information back to the drone pilot and 

others.

• Home-based devices, such as the Amazon Echo. The Echo is a multifunctional device that can (1) serve as a 

streaming music player; (2) answer questions and transmit reports on weather and news; (3) check calendar 

appointments; and (4) provide information, for example, from Wikipedia (which describes itself as a web-based, 

openly editable encyclopedia).3 Other examples of connected home applications, according to a magazine 

report, include connected lights and kitchen appliances, and “automated home” components—such as 

thermostats that adjust themselves based on impending arrivals. Such devices offer increased convenience or 

increased control, or both, over one’s residence, even remotely. Such devices may also help families manage 

home systems and related costs (e.g., energy management).

Other connected devices are designed for primary use by other overlapping major sectors, such as enterprise or 

public sector organizations.4

 Within each of these sectors (consumer, enterprise, and public, see fig. 23 below) 

different categories of devices can be distinguished. For example, three categories of consumer IoT devices were 

discussed above. Similarly, devices whose primary use occurs in enterprise or industry may be used in 

manufacturing, agriculture, or areas such as research, management, or sales (see second row of fig. 23). And 

devices for which the public sector is the primary user may apply specifically to the provision of resources, such as 

water or power (e.g., the electric grid), or transportation infrastructure (third row of fig. 23)—or potentially, research 

in the public interest.

Figure 23: IoT World by Primary Use Case: Subcategories

Some IoT devices are not associated 

with a single type of primary user. For 

example, drones may be operated by 

consumers, as previously mentioned, but 

also by businesses or government 

agencies. Additionally, when operated by 

enterprise/industry or public-sector 

organizations, drones may gather 

information on consumers or members of 

the general public, thus blurring the lines 

suggested by figure 23. 

There is interaction among the three 

major sectors described above. A key 

example is that service providers (whether public or private sector) collect IoT data from large numbers of consumer 

users; such data collectors may use Big Data methods to extract meaningful patterns from these data and/or share 

the data with others. Thus, data from devices for which the consumer is the primary user can inform decisions via 

what some experts call “data flows”—which include collection and analysis of gathered data—on the part of not only 

the consumer, but also enterprise/industry or public-sector organizations (see fig. 24). 

 

3

A streaming player is one that plays media as it is being downloaded. This is in contrast to playing media locally from physical devices, 

such as DVDs.

4

Such a categorization would be based on a device’s use cases. (According to experts, a “use case” is a series of interactions to 

accomplish a particular result. A user case can be thought of as “a type of usage.”) Categorizations may be non-comprehensive or 

share overlapping use cases.

Appendix IV: A Brief Primer on the Internet of Things Page 80 GAO-16-659SP Data and Analytics Innovation
Figure 24: Data Flows from Consumer Devices 

Outlook and Future Promise: Looking ahead, 

sizable growth seems to be anticipated for the 

global IoT industry. One projection for 2020 puts 

the future number of internet-connected things 

(excluding computers, mobile phones, and 

tablets) at over 10 billion, which implies a near 

quadrupling in the next 5 years (see fig. 25), with 

nearly 30-billion total connected devices, and an 

expert we spoke with projected even higher 

numbers of connected devices. 

Varied projections of global IoT industry 

revenues during the next 5 to 10 years range 

from hundreds of billions to trillions of dollars.

5

According to a research study, different levels of 

revenue growth are reported for different 

sectors.6 Similarly, there are different estimates 

of the overall impact of the IoT industry on the 

global economy as a whole, ranging between $2 

trillion and $6 trillion by 2025, according to one 

research study.7

Notes: (1) Devices described in this figure are those for which the consumer is the primary user. 

(2) The “home” category includes consumer-based appliances. 

(3) The “sunburst” S’s represent sensors which can be placed in the consumer devices.

 

5

Different research groups described different ranges of possibilities—predicting anywhere from $300 billion to $7 trillion in global 

revenue by 2020. 6

Identifying consistent trends in revenue growth is challenging because different sources use different metrics, definitions, and market 

cross-sections, among other things, to establish their estimates. For example, a research group (Tata Consultancy Services 2015) 

found, by survey, that consumer packaged goods industries had about a 13 percent increase in revenue from 2013-2014, with $41-

million projected mean expenditures, per company, on IoT initiatives in 2015. Another study (Mazhelis et al. 2013) reported that 

machine-to-machine consumer electronic revenue growth was expected to reach nearly 40 percent annually from 2011 through 2015 

based on 2011 projections made by Machina Research (2015), using a “compound annual growth rate” model over the 5-year period. 7

There were other estimates of overall economic impact among the studies that we reviewed, also in the trillion-dollar order of 

magnitude. These studies do not necessarily employ the same definition of economic impact or the same estimation methodologies.

Appendix IV: A Brief Primer on the Internet of Things Page 81 GAO-16-659SP Data and Analytics Innovation
Figure 25: Projected Growth of Connected Devices 

The fact that significant growth is consistently 

anticipated for the IoT as a whole by industry 

observers covered by the scope of our review 

(despite varied estimates of the amount of 

growth) may result from (1) the decreasing costs 

of connecting nearly any object and (2) 

perceptions of the desirability/utility of harvesting 

information from various devices—and remotely 

controlling systems—for convenience, safety, 

and entertainment. 

With respect to the likely extent of consumer 

adoption of IoT devices, experts varied in their 

views. For example, one expert told us that 

currently “the money is in business-to-business” 

applications, and that there are currently no 

strong use cases for home consumer adoption.8

 

Note: Bars depict growth in the number of connected devices, including projections for 

future years. Fading indicates that future projections may be (1) less accurate than

current figures and (2) characterized by greater uncertainty, as they extend farther 

into the future.

Another suggested that there has to be a “killer app” associated with devices to increase consumer adoption. As an 

example, this expert noted that activity trackers have been “hugely popular.” Some others said that—as far as some 

IoT consumer goods, such as wearables, are concerned—there is currently more hype than development.

9 Another 

view was expressed by a report that stated that by 2020, cars will dominate the number of cellular IoT connections, 

followed by devices used for “living and working,” and health, with cities and industry being far behind and 

combined, not even equal to the number of “living and working” devices.

10 

In terms of public impact, a research group (Anderson and Rainie 2014) reports that 83 percent of experts it 

surveyed anticipate “widespread and beneficial effects on the everyday lives of the public by 2025.” These benefits 

include people being able to monitor themselves as well as family members (for example, children), having greater 

control over their home, and being able to receive services, such as medical care, remotely. The data gathered from 

IoT devices can also be analyzed and used to make decisions, such as where to direct resources. Experts we spoke 

to described IoT as providing a rich source of data that can allow users to obtain data from new sources, and the 

data are provided at a larger scale and in real time. With such information, it may be possible to make better 

informed and more efficient decisions. For example, managing vehicle traffic on roads may lead to reduced 

congestion and fewer accidents; experts told us knowing when to perform equipment maintenance or tracking a 

supply chain can lead to substantial savings in manufacturing costs, and knowing what the pollution level is in a 

particular location can guide, for example, cleanup efforts.

Challenges: Potential Hurdles and Drawbacks: According to experts, the IoT universe is still developing, without 

a clear consensus on how to approach regulations on what data can be gathered, stored, used, or shared via IoT 

devices. There are several potential hurdles and drawbacks to the IoT, some of which may impede widespread 

usage of IoT devices, but many of which anticipate possible future areas of concern—such as a loss of privacy or a 

lessened ability to control the devices.11

 

8

In addition to the B2B applications, there is ongoing research for developing connected cars (vehicles), such as the Department of 

Transportation work on developing communications technology as well as related cybersecurity issues as described elsewhere in this 

report.

9

One research group (Gartner 2014) reported that IoT overall is at the peak of the “hype cycle,” suggesting that there is large demand 

and interest in IoT from vendors, but smaller investment in the IoT. 10It is important to note limitations to such studies. For example, cars may dominate cellular IoT connections, but there are other 

connections—Bluetooth or wired Internet, for example—that are not counted in such studies. Thus, each of these numbers should not 

be broadly generalized to the IoT at large.

11Due to space limitations, not all factors that may limit IoT adoption are discussed. For example, Internet address availability and 

similar resource requirements are intertwined with existing discussion or beyond the scope of this work.

Appendix IV: A Brief Primer on the Internet of Things Page 82 GAO-16-659SP Data and Analytics Innovation
IoT Standards: Harmonization of standards for data from IoT devices is limited. Lack of standards harmonization 

can lead to more limited usage by interested parties due to concerns about conflicting standards and limits to 

function if a device cannot work with other devices. Experts told us that currently, there is no universal standard or a 

comprehensive set of harmonized standards for data formats or communication protocols. To illustrate this, areas 

that may be affected include (1) “interoperability,” or the capacity for different devices to share data or receive 

commands, and (2) spectrum allocation—the assignment of specific frequencies used by devices to transmit and 

receive data. The result is that generated data may not be readily analyzed without preprocessing and that some 

devices cannot work with each other. Some experts were more cautious, indicating that while there is a lack of a 

predominant standard, different standards do not necessarily have to work together. 

Security: Vulnerabilities in many IoT devices can arise for several reasons, including (1) a lack of security standards 

addressing unique IoT needs, (2) lack of better incentives for developing secure devices, and (3) the everdecreasing size of such devices—which limits the currently available computational power. Experts we spoke to 

state that IoT security is an active area of research. Many IoT breaches have been identified prior to any physical 

damage being done, but such information reveals both the pervasiveness of IoT devices in daily life as well as the 

extent of vulnerabilities of such devices. For example, according to news and magazine articles and reports:

• Wireless medical devices have been taken over and controlled. While these devices are not always part of the 

IoT—some may function based on Bluetooth—the lack of security remains an area of concern for devices that 

permit wireless communication.12 

• Zigbee—a widespread wireless standard for IoT devices such as home automation, retail and smart energy—

was shown to be taken over by a nefarious actor despite the strong and robust encryption standards 

incorporated by the standard, according to a security company. 

• Some gas stations’ tank-monitoring system had no passwords, and simulated monitoring systems suffered 

electronic attacks, which could potentially lead to shutting down pumps or incorrect information regarding tank 

levels.

• A wireless-enabled rifle’s targeting system was shown to be open to reprogramming, allowing a nefarious actor 

to alter the gun’s target.

Privacy: According to a Congressional Research Service report (Fischer 2015) and experts, privacy is a major area 

of concern for the IoT. Experts noted, however, that IoT devices, given their increasing prevalence, may make it 

difficult or impossible for individuals to avoid being sensed and tracked (which logically might lead to others’ 

analyzing or using the information, unless appropriate technological and/or policy measures are implemented to 

balance the potential intrusiveness of these devices). Further, a report noted the types of data being gathered are 

increasingly from the IoT and that “born digital” data—data that is created specifically for use by a computer or dataprocessing system, such as mouse clicks —can be fused with much more detailed and personal data, such as “born 

analog” data gathered from microphones, cameras, and health-monitoring devices. 

 

12This is not limited to medical devices—a report stated that recently, various car technologies, including brakes, have been shown to 

be open to digitally based attacks by nefarious attackers. In contrast to devices intended for convenience or entertainment, one’s wellbeing and survival may depend directly on medical devices.

Appendix IV: A Brief Primer on the Internet of Things Page 83 GAO-16-659SP Data and Analytics Innovation
Appendix V: Profiles of DAI Developments in 

Three Areas

Page 84 GAO-16-659SP Data and Analytics Innovation

• Profile 1: Health Care and Ongoing Data Developments

• Profile 2: Intelligent Transportations Systems and Connected-Vehicle 

Driving in the 21st Century: Areas of Developments

• Profile 3: Financial Markets and Ongoing Data Developments

This appendix reproduces, verbatim, an excerpt that includes three 

profiles from the Reading Package that we e-mailed to participants in 

advance of the forum, which was held January 27–28, 2016, at GAO 

Headquarters, Washington, D.C. References cited in the profiles are 

included in the List of References (app. VIII).

Appendix V: Profiles of DAI Developments in 

Three Areas
Profile 1: Health Care and Ongoing Data Developments

Twenty-first century health care—an increasingly complex enterprise—is being transformed by new opportunities 

created by the expansion of detailed digital information and increasing analytical capabilities. 

 

The ongoing expansion of digital health-related data includes some major increases in:

• adoption of electronic health records (EHR) by hospitals and physicians (for example, U.S. physicians’ adoption 

is predicted to reach near-ubiquitous levels—95 percent of physicians—by 2020, but was estimated at just 18 

percent in 2001);1

 

• the volume and variety of connected or connectable health-related devices—also described as the Internet of 

Things—as depicted in figure 26, including (1) implanted medical health devices such as pacemakers that also 

track patients’ heart activity, and insulin monitors that communicate with an insulin pump; (2) digital patientmonitoring equipment in hospitals and from remote locations; and (3) wearable fitness trackers that consumers 

use, for example, to measure steps taken and heart rate;2 and

• the availability of diverse information on the Internet (or from other private-sector or government sources), which 

may be directly or indirectly related to health.

To help make sense of the larger volumes of the new, more complex, and varied data described above, a number of 

new analytical capabilities are now available. These capabilities include, for example:

• increases in computing power achieved through distributed processing;

• advances in genome sequencing, which have dramatically reduced the cost and increased the throughput of 

sequencing a human-sized genome; 

• new computer systems based on advanced machine learning and probabilistic reasoning (for example, IBM’s 

“Watson”);3 and

• new ways to visualize data (such as the BioMosaic mapping program being developed by the Centers for 

Disease Control).4

 

The nature of organizational relationships and interconnections in the health care arena is also changing. The 

number of entities generating health data and the flow of data among those entities have both increased 

dramatically in recent years, creating an overall pattern of increasing complexity, as illustrated in figure 27. This 

complexity may now be continuing to increase—going beyond what is shown in figure 27 because of technological

and data-related developments.

 

1

In addition to physicians, as of 2013, about 6 in 10 (59 percent) of U.S. non-federal acute care hospitals had adopted a basic EHR 

system, nearly quadrupling the 2010 figure. However, GAO has reported slower progress in the meaningful use of EHRs. For example, 

analysis of data through October 23, 2013, found that only about one-third of eligible professionals (such as physicians) received 

incentive payments in 2012 for meeting Medicare’s meaningful use requirements (GAO 2014b). 2

Currently, wearables and implants account for about 5 percent of all data generated in the digital health care universe, but connected 

devices are projected to proliferate and contribute much more in the future. Fitness trackers are now used by 10 percent to 20 percent

of U.S. adults. Advanced trackers can sync with smart phones or computers to provide fitness summaries.

3

“Watson” is the advanced computer program that famously won at Jeopardy. Its cognitive computing method was built to mirror human 

learning: observation, interpretation, and evaluation leading to a decision.

4

BioMosaic is designed to map data from various sources—including health data but also Census data, international travel, and 

migration patterns—to help public health practitioners to anticipate and track emerging epidemics.

Appendix V: Profiles of DAI Developments in Three Areas Page 85 GAO-16-659SP Data and Analytis Innovation
Figure 26: Connected or Connectable Devices Used by Consumers and Patients

 

Note: “Consumers” includes healthy individuals or individuals not receiving medical treatment. “Patients” are individuals receiving medical treatment.

More generally, medical and health-related consumer technologies are now being designed to facilitate networking 

and interconnections and to feed information into the patient care system. For example, the emerging Internet of 

Things could provide:

• new glucose monitors that allow sharing a patient’s glucose levels with chosen others such as physicians and 

family members;

• new GlowCap pill containers that are designed to (1) remind patients to take their medications, (2) collect 

information on patients’ compliance, and (3) send that information to their doctors (and if desired, to family 

members) to help to avoid under-medication problems; and

• fitness trackers that similarly allow a process of syncing/sharing with friends, family members, or other 

individuals.

Another development consists of efforts to increase the involvement of patients and their families in medical studies 

through technology that sends their data directly to researchers—notably, the network founded by the PatientCentered Outcomes Research Institute (PCORI).5

 

 

5

PCORI was established to close the gaps in evidence needed to improve key health outcomes by identifying critical research 

questions, funding patient-centered comparative clinical effectiveness research, and disseminating the results. The hope is that 

research will be conducted more efficiently and cost-effectively and results disseminated quickly back to patients, clinicians, and 

delivery systems to improve patient health (Fleurence et al. 2014).

Appendix V: Profiles of DAI Developments in Three Areas Page 86 GAO-16-659SP Data and Analytis Innovation
Figure 27: Increases in Sources of Personal Health Data and Data Flows, from 1997 to 2013

Potential Benefits: Advances in twenty-first-century data and analytics seem poised to generate major benefits in 

health care, spanning diverse areas such as research; delivery of care including diagnosis, treatment, and follow-up; 

and preventative care and lifestyle changes that—as a result—may contribute to an overall reduction in health care 

costs. 

To illustrate this future promise, significant advances are anticipated in personalized health care. Developments in 

genomics are providing the keys for targeting specific treatments to individual patients, based on their genetic makeup. Additionally, developments in advanced computer systems are facilitating the review and analysis of vast 

amounts of information to assist in identifying optimal treatments for individual patients—by conducting this work 

with greater comprehensiveness and speed than would be possible for a single physician. As an example, doctors 

at the Memorial Sloan Kettering Cancer Center are training IBM’s Watson with anonymized patient cases to teach it 

how to identify the most salient clinical information when making a medical decision and then to select the best 

course of treatment. Additionally, applying data analytics to data on filled prescriptions may increase treatment 

compliance for some patients and decrease the costs and health risks associated with noncompliance, although one 

expert cautioned that the link between Big Data analytics and increased compliance with any healthy behavior may 

be difficult to verify at present. Advances in data collection and analytics can also help hospitals and accountable 

care organizations6 manage risk and could lead to more efficiently delivered care. More efficiently delivered care 

 

6

Accountable care organizations (ACO) are groups of health care providers that are rewarded for improving health outcomes and 

controlling costs. They take responsibility for a defined population of patients. Since 2010, the number of ACO-covered lives has 

Appendix V: Profiles of DAI Developments in Three Areas Page 87 GAO-16-659SP Data and Analytis Innovation
might include accurately identifying high risk patients, such as those with multiple comorbidities, and monitoring 

admission and discharge decisions for those patients.

More efficient and effective research and development, delivery of care, and preventative care and lifestyle changes 

have the potential to reduce overall costs of health care by $300 billion to $450 billion annually, according to a report 

by McKinsey and Company (Groves et al. 2013). Figure 28, adapted from that report, provides a range of cost 

savings estimates in various categories of health care. 

Figure 28: Potential Annual Health Care Savings Generated by Advances in Data and Analytics 

 

Note: These estimates are based on a detailed analysis of cost savings associated with a large number of individual big data innovations already in use 

to varying degrees in different areas of health care in the base year of 2011. Some of these savings may be in the form of lower costs to patients, health 

care providers, or associated businesses such as insurance companies. We did not fully assess the reliability of these estimates, particularly the extent 

to which they might include overlaps in savings reported among the categories or subcategories listed in the figure. However, McKinsey asserts that its

estimates are conservative because there are likely to be new innovations in subsequent years that result in additional savings.

Challenges: There are two key types of challenges related to big data in health care: (1) challenges related to 

realizing the future promise just described, such as the challenge of developing technical and interoperability 

standards for EHR systems and other health data forms, and the challenge of achieving valid results in big-data 

analyses; and (2) challenges that could possibly arise as a result of successful implementation of big data in health 

care, such as those related to privacy and security.

Challenges to achieving interoperability: This issue may be most apparent for EHRs. There are many ways to store 

EHR data, and multiple EHR management systems are available on the market. They may not be technically 

compatible in structure or format. For interoperability, not only must structured data—such as demographic 

information—be shared across records, but unstructured data—such as some clinical notes, where doctors may use 

different abbreviations and shorthand to take notes—must be accessible and make sense to another doctor in 

another system as well. Without standards across the field, these differences can make exchanging or aggregating 

EHRs very difficult, preventing the nation from realizing a truly interoperable health system.7

Hurdles to achieving valid and reliable results: Analyses using new data and analytics may be subject to a number 

of biases including those arising (1) from data (even voluminous data) that are not sufficiently representative; (2) 

from comparisons made across nonequivalent groups (even when causal conclusions are being considered); and 

(3) from the use of indicators that do not measure the issue of interest. An example of this last point is the significant 

overestimation of peak flu levels provided by Google Flu Trends in 2013. Although statisticians and researchers 

 

increased annually. Current estimates of the size of the population in ACOs ranges from 23 million to more than 50 million covered 

lives. ACOs seek to provide more integrated care and have the potential to merge clinical and claims or administrative data. However, 

many ACOs are still in the early stages of gathering and using Big Data to fulfill their mission. 7

Standards are one of several challenges to achieving health information interoperability, as GAO (2015d) has previously reported. 

Other challenges include variation in state privacy rules, costs associated with interoperability, and the need for governance and trust 

among entities, such as agreements to facilitate the sharing of information among all participants in an initiative.

Appendix V: Profiles of DAI Developments in Three Areas Page 88 GAO-16-659SP Data and Analytis Innovation
have long studied problems such as these, it may be important to transfer and expand relevant knowledge to help 

achieve valid and reliable results in the new era.

Increasing privacy and security issues: Americans are concerned about the privacy and security of their health data. 

In one recent survey by the Pew Research Center, respondents included health data in the top three most sensitive 

and personal types of data (Madden et al. 2014);8 in a second survey, respondents were far more likely to support 

EHRs if they were confident that adequate privacy and security protections are in place (Hughes et al. 2014), and in 

a third, they also indicated that they were concerned about privacy for connected medical devices (TRUSTe 2014). 

In one expert’s view, the issue of a person’s autonomy regarding the use and ownership of his or her own data 

underlies such concerns.

Experts and published research highlight two other current concerns about health care data:

• The complexity of the health care system, which can have both privacy and security implications: In the United 

States, health data privacy and security are not regulated or enforced by a single agency under a single 

authority. Rather, multiple interacting agencies and authorities oversee different, and sometimes overlapping, 

categories of entities. For example, HHS enforces the Health Insurance Portability and Accountability Act

(HIPAA), but the Federal Trade Commission (FTC) also has authority to address some privacy concerns by 

taking actions against deceptive or unfair practices. This patchwork of regulation creates uncertainty about the 

vulnerabilities health data face as they are used and shared. Furthermore, both the number of entities 

generating health data and the flow of data among those entities has increased dramatically in recent years (fig. 

27) and is increasing. The expansion of the number of entities interacting with health care data means that 

privacy assurance is far more uncertain. More generally, the increasing number of entities with access to 

sensitive data has increased the risk of a privacy breach and makes the already challenging task of providing 

adequate data security even more difficult.9

 

• Cyber-physical or “smart” system security, which is a concern with respect to connected or connectable medical 

devices: The cybersecurity of connected medical devices involves patient-safety issues. For example, a cyberbreach of a Bluetooth-enabled pacemaker could have life-threatening implications, but currently many such 

devices may not be well-protected. A further problem is that when security flaws are identified, it is important to 

correct them quickly with “patches.” Medical devices are regulated by the Food and Drug Administration (FDA), 

and the FDA would have to review changes. While FDA guidance attempts to avoid a potentially lengthy 

approval process when patches would not result in unintended (negative) safety or health consequences,10

patches might introduce new, even less well understood problems, and responsibilities for determining the 

impacts may be unclear. One expert told us that current FDA rules or guidance may not be fully understood by 

all vendors. In addition, the expert told us that there are growing uncertainties about what needs to be regulated 

in the first place and what constitutes medical software.

These issues may take on an added significance if the emerging Internet of Things is increasingly adopted by 

consumers and patients across the nation. Finally, even supposedly “de-identified” health data may allow for an 

individual to be re-identified if combined with certain data elements— such as date of birth, gender, and residential 

zip code obtainable from voter registration records. While studies of “re-identification attacks” on health care data 

have generally presented low success rates, there may be exceptions. 

 

8

While people are concerned about breaches, big data also presents issues of privacy even when there is no breach such as the 

sensitivity about data related to mental health, substance abuse, or HIV/AIDS. 9

These issues can also lead to uncertainty about whether or not transferring data is even permissible, a problem that could have a 

chilling effect on businesses’ decision to share data, especially if combined with the general lack of competitive advantage in sharing 

data.

10See, for example, U.S. Department of Health and Human Services (2005). 

Appendix V: Profiles of DAI Developments in Three Areas Page 89 GAO-16-659SP Data and Analytis Innovation
Profile 2: Intelligent Transportation Systems and Connected-Vehicle Driving in the 21st Century: Areas of 

Development

The Big Data phenomenon has begun to affect the efficiency and safety of surface transportation through the 

research, development, and deployment of intelligent transportation systems (ITS)—including emerging 

technologies, the Global Positioning System (GPS), and the Internet of Things (IoT). These platforms enable 

vehicles to “communicate” with each other or to roadway infrastructure (such as traffic signals), to identify locations, 

and to provide local data. Analyses of these data help make driving private vehicles safer and more convenient.11

Existing Intelligent Transportation Systems: ITS currently encompasses a broad range of wireless and wire-line 

communications-based information and electronic technologies, including technologies for collecting, processing, 

disseminating, or acting on data to help manage congestion and improve the transportation system’s operation and 

safety.12 See figure 29 for examples of how ITS technologies help manage congestion. 

Figure 29: Select Uses of ITS Technologies to Manage Congestion

As the figure above shows, transportation officials can monitor traffic conditions through multiple cameras and 

sensors that develop volume and incident data. Such information allows faster response to incidents that impede 

traffic flow, as well as easing congestion. For example, many regional transportation agencies, such as in

metropolitan New York City, are using tools to help better manage traffic, including providing travel time information 

to motorists on area roads. Moreover, the U.S. Department of Transportation (DOT) has funded several research 

 

11In this profile, we are principally focusing on private vehicle users. 12Aspects of ITS that are already incorporated into the American transportation system include traffic signal controls, closed circuit

cameras, ramp meters, radar detectors, dynamic message signs, and vehicle probes. For more information, see GAO (2012a). 

Appendix V: Profiles of DAI Developments in Three Areas Page 90 GAO-16-659SP Data and Analytis Innovation
programs, such as dynamic mobility applications and road weather management, for improving individual mobility, 

mitigating safety risks, as well as for better understanding driver behavior, such as how people are distracted and 

respond while driving. 

Connected-vehicle technologies: Transportation technologies—such as advanced wireless communications, onboard computer processing, advanced vehicle-sensors, GPS navigation, and others—are now being networked into 

what is known as the “connected vehicle,” which could potentially transform the way people travel. 

Vehicle-to-vehicle (V2V) technologies: The primary goal of V2V technologies is to prevent crashes, thereby reducing 

injuries, deaths, and instances of crash-related congestion. V2V technologies, now in real-world testing, transmit 

data between vehicles to warn drivers about potential collisions.13 Specifically, a V2V-equipped vehicle emits data 

10 times per second—including data on the vehicle’s speed, position, heading, and acceleration, as well as its size, 

braking status, and other factors. The transmissions are received by on-board V2V equipment in surrounding V2Vequipped vehicles; the on-board equipment in a receiving vehicle would interpret the data and provide its driver with 

warnings as needed. V2V uses dedicated short-range communication (DSRC), a Wi-Fi-like technology that 

provides a link through which vehicles and infrastructure can transmit messages over a range of about 1,000 to 

1,600 feet—which is greater than that of existing sensor-based vehicle technologies.14 V2V technologies are 

capable of alerting drivers to a wide range of potential collisions, including some that could not be anticipated by 

existing sensor-based technologies—such as a stopped vehicle blocked from view or a moving vehicle approaching 

a blind intersection (see fig. 30). Based on current research, DOT’s National Highway Traffic Safety Administration 

(NHTSA) believes that V2V devices are able to transmit and receive messages securely with few problems, and 

would provide safety benefits. NHTSA initiated an Advanced Notice of Proposed Rulemaking in August 2014 to aid 

the agency to develop rules.15 The proposed rule would require vehicle manufacturers to install V2V technologies in 

new cars and light trucks. NHTSA plans to develop a draft V2V rule during 2016, with the expectation that V2V 

technologies may be available in certain car models as soon as 2017. 

 

Figure 30: Vehicle-to-Vehicle Communications and a Warning Scenario

Vehicle-to-infrastructure (V2I) technologies: V2I relies 

on information exchanged between vehicles and 

infrastructure—meaning that the infrastructure requires 

built-in technologies to communicate with nearby 

vehicles (see fig. 31). Like V2V technologies, V2I 

technologies will use DSRC technology to transmit data 

between vehicles and the road infrastructure, resulting 

in alerts and advice to drivers. 

 

V2I applications are designed to avoid or mitigate 

vehicle crashes not addressed by V2V as well as to 

provide warnings of upcoming rail crossings and road 

curves that require lower speeds, among other things. 

Further, V2I applications are expected to provide 

safety, mobility, and environmental benefits. For

Note: In this scenario, the truck and sports utility vehicle (SUV) are at risk of colliding because 

the drivers are unable to see one another approaching the intersection and the flattened stop sign Is

not visible to the truck’s driver and maybe not to the SUV’s. With V2V technology enabled, both 

drivers would receive warnings of a potential collision, allowing them to take actions to avoid it.

example, the Spot Weather Impact Warning application is designed to detect unsafe weather conditions, such as ice 

or fog, and notify drivers if reduced speed or an alternative route is recommended. Likewise, a DOT research 

partnership with auto manufacturers is focusing on developing and implementing crash avoidance 

countermeasures.16 Deployment of V2I systems requires the collaboration of many stakeholders, particularly state 

and local agencies, as well as auto manufacturers. Domestically, DOT, state and local agencies, and universities 

 

13See GAO (2013b). 14Sensor-based crash avoidance technologies are capable of detecting potential collisions with other vehicles within a range of about 

500 feet.

15See National Highway Traffic Safety Administration (2014). 16This partnership is known as the Crash Avoidance Metrics Partnership.

Appendix V: Profiles of DAI Developments in Three Areas Page 91 GAO-16-659SP Data and Analytis Innovation
have established connected-vehicle test beds for testing and refining V2I technologies and applications.17 

Internationally, Japan has fully implemented V2I across its highway system, while several European countries are 

also working to develop an international smart corridor to provide drivers information on, for example, road work and 

upcoming traffic.18 

Figure 31: Examples of Vehicle-to-Infrastructure Applications

DOT’s Federal 

Highway 

Administration 

(FHWA) plans to 

release V2I 

guidance to 

assist 

transportation 

managers and 

operators in 

adapting traffic 

signals and other 

roadside devices 

so they are 

compatible with 

the new 

connectedvehicle 

technologies. In 

September 2015, 

DOT announced 

three recipients 

of its national 

connectedvehicle pilot deployment program grants: New York City, Tampa, Florida, and the State of Wyoming.19 

Emerging and anticipated developments: Researchers, car manufacturers, DOT, and others are working on the 

following:

• vehicle-to-device (V2X) technologies that would allow vehicles to communicate with any device equipped 

with enabling sensors, thus allowing vehicle/driver communication, for example, with pedestrians and 

bicyclists; and

• autonomous (or self-driving) cars, which are now being developed by some corporations—and which are 

• currently being road-tested in some locations.20 These corporations are already introducing (or planning to 

introduce in next year or so) semi-autonomous features, including automatic braking and hands-free driving, 

and some companies anticipate that the technology for fully autonomous vehicles capable of navigating the 

roadways could be ready sometime during the next decade.

 

Other new developments include collecting data from drivers. For example, StreetBump is an application that 

allows drivers to easily report potholes or other problem conditions and help transportation agencies set 

maintenance priorities. 

Future benefits of connected-vehicle technologies: According to transportation experts and DOT, ITS and—in 

particular, connected-vehicle technologies—hold great potential to make surface transportation safer, smarter, and 

greener. 

 

17Some test beds are also being used to test and refine V2V technologies. 18See GAO (2015c). 19These state and city governments will receive up to $42 million to pilot next-generation connected-vehicle technology in infrastructure 

and vehicles.

20In 2013 DOT released guidance to states permitting testing of fully automated vehicles.

Appendix V: Profiles of DAI Developments in Three Areas Page 92 GAO-16-659SP Data and Analytis Innovation
• Safety: Connected-vehicle technologies would have a significant impact on safety. According to DOT, 

connected-vehicle technologies could potentially address about 80 percent of unimpaired (i.e., non-alcohol 

involved) crashes involving two or more vehicles. Once fully implemented, the various communications 

technologies could also prevent accidents involving infrastructure, pedestrians, and cyclists, thereby further 

decreasing injuries and fatalities. The technologies (V2I, in particular) could also be used to warn drivers of 

potentially dangerous conditions such as icy roads or impending collisions. Furthermore, connected-vehicle 

technology research could lead to the design of future systems to provide safety benefits to certain categories of 

drivers, such as teen drivers or aging drivers. 

• Mobility: According to the Texas A&M Transportation Institute’s (TTI) Urban Mobility Scorecard, Americans 

spent 6.9-billion hours in traffic congestion in 2014.21 DOT estimates that connected technologies could reduce 

time Americans spend in traffic by 4.8-billion hours annually. These technologies could help local governments:

1. Provide commuters and travelers with information such as detailed and accurate information on realtime traffic (including on bottlenecks), transit information, and parking data; and

2. Manage transportation systems better, for example, to reduce congestion. 

Additionally, connected technologies are being used by new types of transportation applications, such as Uber 

or RideScout, which provide more mobility choices.22 

• Traffic operations and the environment: Connected-vehicle technologies may also provide operational and 

environmental benefits. According to TTI, Americans wasted 3.1 billion gallons of fuel sitting in congestion in 

2014, compared to 0.5 billion in 1982. Capacity building and information sharing can help mitigate traffic’s 

impact by giving drivers key information on travel options in order to make more environmentally friendly 

transportation choices such as potentially switching to public transit or taking routes that are less congested 

(which would result in less idling). For example, in the metropolitan New York area, transportation officials 

analyze travel-time data to help better plan travel corridors, assess the cost of delays, and provide travelers with 

real-time travel information.

Challenges: Several factors pose challenges to realizing the potential benefits of connected vehicles discussed 

above. While some challenges specifically affect the deployment of connected vehicles (such challenges as 

developing technical standards to ensure interoperability of connected-vehicle systems, along with providing 

sufficient funding to develop and maintain such systems), other challenges can affect the users of these systems. 

Among the challenges identified by experts we consulted for this profile include: 

• Spectrum sharing: Both V2V and V2I technologies depend on radiofrequency spectrum, which is a limited 

resource that is in high demand, in part, due to the increase in mobile broadband use. Currently, FCC licenses 

DSRC-based ITS use of radio frequencies in the 5.9 GHz band on a co-primary basis with federal government 

radar and commercial satellites. FCC has proposed a rulemaking pertaining to the sharing of this spectrum.23 

DSRC systems support safety applications that require the immediate transfer of data between entities (such as 

vehicle or infrastructure); sharing this spectrum—some transportation experts believe—would result in delays or 

interference from unlicensed devices that may jeopardize crash avoidance capabilities. 

• Quality, analysis, storage, and ownership of transportation data (data management): Some of the experts we 

spoke with identified the management of these data as a challenge, especially for public transportation agencies 

and transportation researchers. Quality is a major concern, as more data collected do not necessarily equate to 

better data. Steps must be taken to “clean” the data, such as identifying and eliminating “bad” data (e.g., 

duplicates). Further, data must be formatted in a way that permits analysis: at present, companies and public 

agencies may format data differently. For example, states that collect road data based on location mile markers 

cannot easily share and compare their data with states and local agencies that use a different location-based 

system without additional formatting. Data storage may also be difficult, especially for transportation agencies 

which, according to one expert we spoke with, may not have cloud storage capability or the funds to buy cloud 

 

21 See Schrank et al. (2015). 22 Uber is a ride sharing company. RideScout is a mobile navigation app that provides real time transportation options (including transit, 

taxi, and walking) for an area. 23 See Federal Communications Commission (2013). 

Appendix V: Profiles of DAI Developments in Three Areas Page 93 GAO-16-659SP Data and Analytis Innovation
storage for large data sets. Other issues involve policies on data retention and data ownership: according to 

one expert, ownership has implications regarding responsibility and accountability. 

• Data privacy: According to experts, the collection and sharing of location data, whether from smartphones or 

other electronic devices, pose privacy risks. The huge number of data elements created and collected daily 

from smart phones, connected vehicles, and other devices could be used to track travelers’ movements. While 

these data elements generally are used in ways that are helpful to travelers, such as providing directions or area 

information, such personally identifiable information (PII) can pose a privacy risk if, for example, the data are not 

securely protected. Data from vehicles include not only GPS location data but also other data on the operation 

of the vehicle, for example, its speed and braking data. Some of the experts we spoke with recognize that the 

public may perceive that PII could be exposed or tracked. Still, such risks might affect different people 

differently: one expert told us that focus groups his agency held found that younger participants were fine with 

giving up some privacy for convenience while older participants were not. While DOT officials, car 

manufacturers, and suppliers plan to incorporate privacy by design24 into connected-vehicle technologies, 

consumer acceptance might require greater public education about how third parties might use PII data, 

according to some experts we spoke with.25 

• Data security: According to the experts we consulted, securing and transmitting transportation data is a 

challenge, partly given the many sources from which transportation data are derived and transmitted. According 

to DOT, for connected-vehicle technologies to function safely, security and communications infrastructure need 

to help assure users that messages between vehicles and infrastructure are trustworthy. DOT and industry have 

taken steps to develop a security framework for all connected-vehicle technologies. They are testing and 

developing the Security Credential Management System (SCMS) to ensure that messages are secure and 

coming from an authorized device. In addition to these security issues, concerns have been raised about the 

potential for future breaches of cybersecurity systems designed to protect connected vehicles and other 

computer-connected entities. Cybersecurity, within the context of road vehicles, is the protection of automotive 

electronic systems from malicious attacks, damage, unauthorized access, or manipulation. 

 

24Privacy by design is an approach to protecting privacy by embedding it into the design specifications of technologies, business 

practices, and physical infrastructures. According to DOT, for V2I, this would create a security system technically designed so that it 

would be very difficult to use the system to track vehicles or individuals, or otherwise discover any personally identifiable information.

25The automotive industry announced in November 2014 efforts to develop industry privacy principles to protect the consumers by 

ensuring driver data privacy, including geolocation, driver behavior, and biometric data.

Appendix V: Profiles of DAI Developments in Three Areas Page 94 GAO-16-659SP Data and Analytis Innovation
Profile 3: Financial Markets and Ongoing Data Developments

Growing bodies of data and advanced computing power are affecting key aspects of financial institutions and 

markets. For example, they are

• changing the way that banks and insurers conduct financial business with individuals and firms, and 

• improving regulators’ and others’ ability to provide information on and exercise oversight of financial markets 

and institutions in those markets.

Banking and insurance: Risk-based decisions about loans and insurance are increasingly being affected by data 

derived from social media or the Internet of Things (IoT).26 For example, lenders may note the number of friends on 

a loan applicant’s social media profile (which is publicly available) and his/her prior postings on social media sites, or 

even mine these sites for financial status information (e.g., an applicant’s earlier statement, perhaps in jest: 

“Bummed that my credit card maxed out!”). 

Figure 32: Lending in the Context of Big Data Phenomena

Similarly, lenders’ consideration of a small 

business loan applicant might be affected by 

the comments posted about that company. 

Such developments are part of a shift in 

lending processes, as illustrated in figure 32. 

The insurance industry is also assessing 

risk and making pricing decisions based on 

new data—including data from sensors, 

such as driving behavior-monitoring sensors 

placed in insured autos; soil sensors on 

farms insured for crop insurance; and data 

from fitness trackers or smart contact lenses 

worn by persons purchasing life or health 

insurance.

Turning to credit default swaps (CDS), these

were central to the system-wide problems 

encountered during the financial crisis of 

2007-2009, according to the Financial 

Stability Oversight Council (or FSOC), which 

was created in response to that crisis, but 

new data and analytics may bring the 

potential for addressing or avoiding such 

problems in future.

27 Prior to the crisis, such 

swaps were not well understood by 

regulators or market participants. The failure 

of any CDS seller would have eliminated the 

protection for buyers against credit risk when they most likely needed it, and CDS holders stood to bear the risk of a 

counterparty’s default. Also, neither market participants nor regulators fully understood how financial institutions 

were interconnected through CDS markets. This lack of clarity was partly because CDS transactions were not 

reported. In response, regulators have now begun to collect new data and develop analytical tools to clarify who is 

obliged to pay whom, when, and why. For the CDS market, the network of connections between buyers and sellers 

represents a series of conduits through which one company’s financial distress can spill over to other companies, in 

turn affecting other companies. Recently, analyses by the Depository Trust and Clearing Corporation (DTCC 2013) 

have illuminated the web of complex transactions through which financial institutions may be interconnected (shown 

in fig. 33).

 

26The IoT consists of networked sensors transmitting various types of data to designated recipients or data repositories typically without 

prompting by a person.

27 See FSOC (2011). CDS are bilateral contracts that are sold over-the-counter and transfer credit risks from one party to another. The 

seller, who is offering credit protection, agrees, in return for a periodic fee, to compensate the buyer, who is purchasing credit 

protection, if a specified credit event, such as default, occurs.

Appendix V: Profiles of DAI Developments in Three Areas Page 95 GAO-16-659SP Data and Analytis Innovation
Note: CDS exposures across different contracts (“underliers”) are netted against each other so that any circle represents the net protection bought or 

sold by a market participant. Orange circles represent participants that are net sellers of CDS protection; pale blue circles represent participants that are 

net buyers of CDS protection. The size of the circle indicates the amount of protection bought or sold. The sample excludes small market participants for 

simplification. See Yellen 2013. 

In addition, FSOC and its member agencies are responding to their mandate to monitor and address systemic risk—

the risk that the failure of one large institution would cause other companies to fail or that a market event could 

broadly affect the financial system rather than just one or a few companies.28 Some of these agencies are using 

analytics to improve their ability to monitor individual markets. To illustrate the use of analytics to enhance regulation 

of securities markets, the Securities and Exchange Commission (SEC) since 2013 has used the Market Information 

Data Analytics System (MIDAS) to capture and analyze over 1 billion records daily from exchanges and offexchange executed trades. According to SEC, MIDAS allows the agency to analyze thousands of stocks, exchangetraded products, equity options and futures contracts over 6 months or a year, involving 100 billion records at a time. 

SEC also uses MIDAS to reconstruct market events and better understand long-term trends and the functioning of 

securities markets. In addition, the Office of Financial Research (OFR), which supports FSOC and its member 

agencies, is developing new models of the financial system to reveal the complicated interconnections between 

financial institutions.29

Future promise—the potential for positive impacts in the coming years: Banks, insurers, investors, and other 

financial institutions may, among other benefits, offer individuals and businesses customized financial products and 

services based on this new information, possibly at lower prices. Additionally, new services might be offered to 

previously underserved individuals. For example, lenders use big data to continue improving their evaluation of 

credit risk and detection of fraud. Also, as one financial industry analyst told us, lenders may increasingly make 

lending decisions based on real-time nontraditional information gathered from social media sites, and in emerging 

markets from sources such as text messages, allowing lenders to extend credit to customers they otherwise would 

not have been able to evaluate using traditional information sources. Market participants could make more informed 

financial decisions, businesses and regulators could better understand risk to the financial system, and regulators 

could improve their oversight of the financial services markets. One market expert added that even the perception 

that market participants are being monitored in real-time could affect their behavior. It might even be possible for 

 

28FSOC’s voting members consist of the Secretary of the Treasury, who chairs the council, and the heads of the Bureau of Consumer 

Financial Protection, Commodity Futures Trading Commission, Federal Deposit Insurance Corporation, Board of Governors of the 

Federal Reserve System, Federal Housing Finance Agency, National Credit Union Administration, Office of the Comptroller of the 

Currency, Securities and Exchange Commission, and an independent member with insurance experience, and its nonvoting members 

consist of the directors of the Office of Financial Research and the Federal Insurance Office, as well as representatives from state-level 

financial regulators.

29OFR was created by the Dodd-Frank Wall Street Reform and Consumer Protection Act to collect and standardize financial data and 

perform essential long-term research, among other things. Pub. L. No. 111-203, §§ 152, 153, 124 Stat. 1376, 1413 (2010) (codified at 

12 U.S.C. §§ 5342, 5343).

Figure 33: A Credit Default Swap Market: A Network of Buyers and Sellers

Appendix V: Profiles of DAI Developments in Three Areas Page 96 GAO-16-659SP Data and Analytis Innovation
regulators to identify and respond to unstable developments in the financial system, lessening or perhaps preventing 

market panics or protracted recessions.

Challenges—hurdles and potential drawbacks: One type of hurdle that can complicate analyses occurs: (1) 

when the data that researchers need to combine in an analysis have either not been collected or organized using a 

common format or (2) when the regulators do not have access to the rules that investors, for example, use to 

organize their data. For instance, in its regulation of the commodities markets, the Commodity Futures Trading 

Commission (CFTC) receives data daily on thousands of over-the-counter (OTC) swaps.30 However, these data 

have not been submitted to CFTC in a standardized format. To help it analyze the data, CFTC has begun working 

with Treasury’s OFR staff to develop appropriate data standards and analytical models. A different kind of hurdle is 

presented by limitations on data availability. Some analyses may not be possible because the relevant information is 

proprietary. As one academic told us, analysts may have difficulty linking buyers and sellers of stock because the 

required mapping is proprietary and remains with clearinghouses that handle the trades, not SEC. Another instance 

of limitations on data availability occurs for analyses that require personally identifiable information. It is collected 

and may be used, for example, by banking institutions to determine whether an individual should receive credit or 

other financial services, but such data need to be secured to assure individual privacy and systemic and market 

integrity.31 Potential drawbacks to collecting and using the data include privacy and security issues. There are 

several privacy issues that concern new developments where lenders and insurers use social media or IoT sensor 

data in making risk decisions. To cite three examples:

• Algorithms financial institutions use to make decisions may not be transparent. Two privacy experts told us that 

whether an algorithm is transparent is relevant when organizations use data on individuals to make decisions 

that affect them. One expert said that basing decisions on “correlations” creates a potential for hidden 

discrimination. The other emphasized that organizations should be open about what data they are using and 

how they are using them, as articulated in the Fair Information Practice Principles (FIPPs).32 Public opinion 

appears to support attention to this issue: in a recent online survey of Internet users who volunteered to share 

their opinions, respondents rated their concern about “not knowing what information is being collected about 

[me] or how it may be used” higher than concern about losing their jobs or becoming a victim of crime in their 

own community.33 

• It may not be clear what will happen to personal data should a bank reorganize or merge with another or 

nonbanking business. One financial industry analyst told us that data can be mismanaged when financial 

institutions are sold, such as documents with Social Security numbers ending up in the “digital dumpster,” only 

to be retrieved by a third party. And GAO has reported that while some privacy laws apply specifically to 

financial institutions, there is no overarching federal privacy law governing the collection and sale of personal 

information among private-sector companies.34 Moreover, the current statutory framework for consumer privacy 

does not fully address new technologies—such as the tracking of online behavior or mobile devices—and the 

vastly increased marketplace for personal information, including the proliferation of information sharing among 

third parties (GAO 2013a). 

• Personal data could be used for illicit gains. The same technologies that produce opportunities for financial 

institutions and regulators also can be leveraged by illicit actors attempting to accumulate information for 

fraudulent, or otherwise illicit, activity. In an environment where these actors also have sophisticated cyber 

 

30CFTC is receiving swaps data in the wake of the Dodd-Frank Wall Street Reform and Consumer Protection Act, which required that 

standardized OTC swaps actions be cleared through a central counterparty clearing house and reported to data repositories.

31For example, the Fair Credit Reporting Act limits the circumstances under which consumer reporting agencies may provide consumer 

reports to third parties. Pub. L. No. 91-508, Tit. VI, 84 Stat. 1114, 1128 (Oct. 26, 1970) (codified as amended at 15 U.S.C. §§ 1681-

1681x).

32The FIPPs were first proposed by a U.S. government advisory committee in 1973, and the Organisation for Economic Co-operation 

and Development developed a revised version in 1980 that has been adopted by many organizations and governments. The principles 

address the collection and use of personal information, data quality and security, and transparency, among other things, and have 

served as the basis for many of the privacy recommendations federal agencies have made (Organisation for Economic Co-operation 

and Development 1980). The recent Consumers Bill of Rights draws on the FIPPs and include the principle of “openness” or 

transparency (see Appendix A of the White House 2011). 33National Cyber Security Alliance national survey online with 1,000 U.S. adults ages 18 and up between December 27, 2013, and 

January 5, 2014. (National Cyber Security Alliance 2015). 34GAO 2013a.

Appendix V: Profiles of DAI Developments in Three Areas Page 97 GAO-16-659SP Data and Analytis Innovation
capabilities, the existence of vast amounts of information on individuals and markets could result in targeted 

attempts to gain access to data that can be exploited for illicit purposes.35 

Turning to data security, the Department of Defense has declared current levels of cybersecurity “uncertain” across 

16 critical infrastructures in the United States, including the financial services sector—in part because of increasing 

dependence on the Internet. Perhaps reflecting this uncertainty, the survey of Internet users cited above reported 

that the most highly rated concerns pertain to cybersecurity. One of the critical concerns was: “having [my] financial 

information lost or stolen.” Additionally, a New York State survey described reports of financial cyber breaches, 

including reported percentages of ATM and other breaches at large financial institutions (see fig. 34).

Figure 34: Percentages of Large Institutions Reporting Three Types of Cyber Breaches

New technologies or new technological approaches to security are being developed. For example, one 

financial industry analyst told us that a bank might thwart fraud by using “dual band authentication and 

verification” that requires that a customer’s cell phone be located near the location of his/her banking transaction 

(see fig. 35). 

Figure 35: Dual-Band Authentication

Additionally, in response to cybersecurity concerns, the National Institute for Standards and Technology has 

developed a voluntary cybersecurity framework for use across 16 critical infrastructure sectors, including the 

financial services sector.

 

35FSOC noted in its 2015 annual report that recent cyber-attacks have heightened concern about operational risk to the financial sector, 

namely that a more destructive incident “could significantly disrupt the workings of the financial system.” (See FSOC 2015.)

Appendix V: Profiles of DAI Developments in Three Areas Page 98 GAO-16-659SP Data and Analytis Innovation
Appendix VI: Scope and Methodology

Page 99 GAO-16-659SP Data and Analytics Innovation

This report on data and analytics innovation (DAI) is based primarily on 

an expert forum, held at GAO on January 27–28, 2016, and also draws 

on relevant literature and consultation with experts in addition to those 

participating in the forum. Our methodology included (1) selecting and 

inviting forum participants, who had a wide range of expertise and views, 

with the assistance of the National Academies; (2) developing a preforum Reading Package, which we sent to participants in advance of the 

forum and which included a primer on the Internet of Things (IoT) and 

three profiles of areas with DAI-related developments (health care, 

transportation, and financial markets); (3) convening and recording the 

forum and preparing a Summary of Forum Discussions, which was based 

primarily on forum transcripts and which we sent to participants for their 

review; (4) drafting the report based on consideration of the transcripts, 

participants’ responses to the Summary of Forum Discussions, the 

material developed earlier for the Reading Package, and other literature 

as well as consultation with experts; and (5) obtaining internal GAO 

reviews and sending the report to two outside (i.e., non-GAO) experts 

who had not participated in the forum, as a final measure of quality 

control.1

Based on recognition of ongoing trends and issues described in literature 

(for example, Manyika, Chui, Brown, et al. 2011; Mayer-Schönberger and 

Cukier 2013; and the President’s Council of Advisors on Science and 

Technology 2014), we identified policy-relevant topics concerning new 

developments in data and analytics, in consultation with the U.S. 

Comptroller General, who decided to convene a GAO Comptroller 

General forum. The forum agenda we created (see app. I) allowed for 

considerable open discussion and flexibility. We briefed majority and 

minority staff for a number of congressional committees on our plans to 

conduct the forum under the Comptroller General’s authority. A number of 

committees and members wrote letters of support for this activity.

Following the Forum, we identified the need to explicate the DAI concept 

as key background material for the report. Additionally, we identified key 

policy-relevant topics for organizing first, the Summary sent to 

participants, and subsequently, similar topics for this report. The policy 

 1

This methodological approach has been termed an “expert-participatory technology 

assessment” (Persons et al. 2014).

Appendix VI: Scope and Methodology

Selection and Report of 

Policy Relevant Topics
Appendix VI: Scope and Methodology

Page 100 GAO-16-659SP Data and Analytics Innovation

relevant topics include (1) far-reaching DAI economic opportunities; (2) 

anticipated DAI impacts on U.S. jobs (and employment more generally) 

and other challenges to realizing economic benefits; (3) pathways to 

societal benefits that DAI can provide—and accompanying challenges in 

achieving these benefits; and (4) privacy concerns associated with data 

and analytic developments.

To develop background for the Forum participants, we identified four key 

areas to be covered. One concerns the emerging Internet of Things (or 

IoT). Three others consist of varied profile areas in which data and 

analytics are having an impact. In selecting the three areas for profiling, 

our main goals were to include areas

• that appeared individually significant in terms of their potential size, 

economic impact, or social importance and

• that as a group, covered diverse industries.

We also considered GAO experience and expertise in relevant areas and 

chose (1) health care, (2) intelligent surface transportation, and (3) 

financial markets.

For the IoT primer and each of the three profiles, GAO analysts and 

specialists (1) conducted preliminary literature reviews and semistructured interviews with experts from industry, government, and 

academia; (2) created a draft reflecting the experts’ opinions and material 

from literature; and (3) subjected each draft to review by the original 

expert-interviewees and other relevant stakeholders. Comments from 

stakeholders and the expert-interviewees were incorporated in the 

profiles as appropriate. A list of the experts whom we consulted (in 

addition to Forum participants) is included in appendix III of this report.

We shared the resulting IoT primer and three area profiles with invitees in 

advance of the Comptroller General forum and have included them in 

appendixes IV (the IoT primer) and V (the three area profiles). The preforum reading material was not designed to be comprehensive or 

definitive; instead, it was developed to provide information that could 

stimulate discussion among a broad array of experts representing varied 

perspectives. The profiles also were intended to give readers of this 

product information about the various ways in which data and analytics 

are being used and could potentially be used in future years and to help 

put forum discussions in context.

Creation of the Pre-Forum 

Reading Package
Appendix VI: Scope and Methodology

Page 101 GAO-16-659SP Data and Analytics Innovation

Finally, we and NAS staff members talked briefly with other experts, not 

included in the listing of experts in appendix III.

To prepare for the Comptroller General forum, GAO contracted with the 

National Academies to assist GAO as it undertook participant selections.

We met with the National Academies staff to help ensure balance and to 

help us assess potential conflicts of interest for forum participants.

In our initial discussions with the National Academies we agreed that 

forum participants should:

• as a group represent a range of backgrounds, experience, and 

knowledge in terms of (1) affiliations with academia, business, 

government, and non-profit organizations (such as think-tanks); (2) 

experience with data and analytics development across a range of 

areas; and (3) diverse professional backgrounds that would include, 

but not be limited to expertise in the emerging IoT (or cyber-physical 

systems or the Industrial Internet) and in data and analytics 

developments in health, transportation, and financial markets;

• from an individual perspective, be able to address topics such as (1) 

economic opportunities, (2) potential impacts on jobs, or (3) concerns 

about data privacy and cybersecurity; and

• further, include individuals who as a group include diverse 

perspectives on issues such as innovation and data privacy.

Our criteria for defining an expert included one or more of the following: 

(1) a significant position in organization(s) relevant to DAI issues; (2) 

authorship of papers in professional journals or other substantial 

publications relevant to DAI issues; and (3) selection to appear on an 

expert panel or make public presentations relevant to DAI issues.

To implement final selections for Comptroller General forum participants, 

first the National Academies identified potential participants based on the 

criteria listed above. GAO and the National Academies then met again, to 

discuss the National Academies’ list of potential participants along with 

other participants whom GAO felt met the requisite qualifications. This 

staged strategy allowed the National Academies an opportunity to 

independently identify and internally discuss potential invitees before 

GAO shared suggestions for potential invitees. This staged strategy was 

employed to bring increased independence to the selection process, 

although GAO made final determinations regarding participant selections.

The Selection of Forum 

Participants
Appendix VI: Scope and Methodology

Page 102 GAO-16-659SP Data and Analytics Innovation

To exercise due diligence and to understand forum participants’ potential 

conflicts of interest, we asked all forum participants to sign a form, which 

asked participants about their perspectives and circumstances. 

Specifically, we asked participants (1) whether their immediate family had 

any investments or assets that could be affected, in a direct and 

predictable way, by a decision or action based on the information or 

opinions they would provide to GAO; (2) whether they or their spouses 

receive any income or hold any organizational positions that could be 

affected, in a direct and predictable way, by information or opinions they 

would provide GAO; (3) whether there were any other circumstances, not 

addressed in the two previous questions, that could be reasonably viewed 

by others as affecting participants’ point of view on the topics to be 

discussed. GAO received signed responses from all forum participants to 

these queries.

The Comptroller General forum was held on January 27–28, 2016, at the 

GAO headquarters building in Washington, DC. The forum was recorded 

and the discussion was transcribed. After the forum was held, a written 

summary of the discussion was prepared by GAO and sent to 

participants. Participants were asked (1) if they concurred overall with our 

(GAO’s) summarization of the discussions and (2) if they had specific 

comments that could help convey specific points. Twenty-two of the 

twenty-three external forum participants responded to these queries, and 

twenty-one based their response on an overall review. Twenty of these 

twenty-one concurred that overall, the draft summary represented 

participants’ remarks at the forum. Finally, GAO staff followed up with 

some forum participants to (1) develop first-hand examples of some key 

points raised by participants in the meeting and/or (2) identify references 

to relevant literature, and so forth.

Forum participants were not polled and votes were not taken on positions 

at the forum. GAO reviewed meeting transcripts and interacted with 

participants after the meeting to summarize and prepare our summary 

findings. In our written summary, the use of the term “forum participants”

means that more than one participant contributed to the point being 

made.

For some of the quantitative data included in the report, we assessed the 

reliability of figures by noting the general methods used and the 

reputation of the publishing organization—or, if deemed necessary, by 

communicating with authors about data and methods. Additionally, we 

Independence of Forum 

Participants

Comptroller General 

Forum and Participant 

Follow-up

Data Reliability
Appendix VI: Scope and Methodology

Page 103 GAO-16-659SP Data and Analytics Innovation

include caveats where deemed appropriate. For example, we include four 

key interpretation points to consider when viewing projected economic 

impacts.

This “non-audit” engagement was designed to represent primarily the 

viewpoints of experts who were selected to participate in the Comptroller 

General forum. The experts were selected by GAO with assistance from 

the National Academies to help ensure balance and independence, and 

the forum was designed to help ensure that all significant viewpoints were 

represented. While we present a summary of the forum and relevant 

issues, the testimonial evidence of experts in this engagement is not 

being used to develop GAO recommendations for executive branch 

actions or to present a matter for congressional consideration.

Forum attendees and other experts were informed that GAO would not 

directly identify individuals or their affiliations in association with specific 

comments (without their permission) and this report does not do so.

Findings and 

Recommendations

Disclosure
Appendix VII: A Brief Note on Federal 

Responses to Privacy Concerns

Page 104 GAO-16-659SP Data and Analytics Innovation

Varied federal concerns about current data privacy issues, including 

cybersecurity, are reflected in a range of reports, including White House 

(2012; 2015) reports on consumer privacy; the National Institute of 

Standards and Technology (2014; 2016) reports on a framework for 

improving cybersecurity; and the Federal Trade Commission’s 2015 

report focusing on privacy and the Internet of Things (FTC 2015)—among 

many others.1

No single federal agency is responsible for the full range of potential 

consumer protections in the area of Internet privacy and security. Rather, 

multiple agencies are concerned with various aspects of data privacy and 

cybersecurity (as illustrated for four selected agencies in fig. 36 below).

 1

Additionally, a report from the President’s Council of Advisors on Science and 

Technology (2014) presents a technological perspective on data privacy issues.

Appendix VII: A Brief Note on Federal 

Responses to Privacy Concerns
Appendix VII: A Brief Note on Federal 

Responses to Privacy Concerns

Page 105 GAO-16-659SP Data and Analytics Innovation

Figure 36: Four Selected Federal Agencies: Relevance to Internet Privacy and Security

In the months following our January 2016 forum, federal agencies have 

participated in numerous efforts to better understand and grapple with 

data privacy and security issues. We illustrate these efforts with some 

examples from the four federal agencies highlighted in the figure above:

• In April 2016, the Federal Communications Commission (FCC) 

published a Notice of Proposed Rulemaking that seeks public 

comment on potential future privacy regulations that would apply to 

broadband Internet access service (BIAS) and Internet service 

providers (ISP); see FCC (2016). The Notice raises many questions 

about ISPs’ and carriers’ collection and sharing of consumer data, 

including (1) whether consumer consent should be required, and if so, 

the conditions under which that consent should be opt in or opt out, 

and (2) the need for transparency in communications to consumers 

about privacy-related practices. The FCC asked for comments by the 

end of June 2016. Additionally, current FCC efforts to advance the 
Appendix VII: A Brief Note on Federal 

Responses to Privacy Concerns

Page 106 GAO-16-659SP Data and Analytics Innovation

development of “Spectrum Frontiers” emphasize the need to build in 

security by design (Wheeler 2016).

• In May 2016, the National Telecommunications and Information 

Administration (NTIA) reported that 45 percent of online households 

said that privacy or security concerns “stopped them from conducting 

financial transactions, buying goods or services, posting on social 

networks, or expressing opinions on controversial or political issues 

via the Internet, and 30 percent refrained from at least two of these 

activities” (Goldberg 2016).2

• Also in May 2016, the Federal Trade Commission (FTC) and the FCC 

began examining how smartphones’ security updates are issued and 

handled by “major mobile carriers and device makers” because of 

concerns that the devices may not be protected with the latest 

security fixes (Williams 2016; Peterson 2016).

• In April 2016, NIST convened a workshop to discuss responses to its 

proposed Cybersecurity Framework—a voluntary framework that 

“focuses on using business drivers to guide cybersecurity activities 

and considering cybersecurity risks as part of [each] organization’s 

risk management processes.” Specifically, workshop participants 

discussed results from NIST’s earlier request for information on that 

framework. In June 2016, NIST published an interim report on 

feedback received, stating that, for example, workshop participants 

expressed a need for “more actionable guidance in mitigating the risk 

of privacy and civil liberties inherent in cybersecurity.”

 2

These data are based on NTIA’s analysis of responses to the Computer and Internet Use 

Supplement to the Current Population Survey, which included nearly 53,000 households 

and was conducted by the U.S. Census Bureau in July 2015.
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Appendix IX: GAO Contacts and Staff 

Acknowledgments

Page 125 GAO-16-659SP Data and Analytics Innovation

Timothy M. Persons, Chief Scientist, at 202 512-6412 or 

personst@gao.gov

Other leadership for this project was provided by Judith A. Droitcour, 

Assistant Director, and Virginia A. Chanley, Analyst-in-Charge, Applied 

Research and Methods (ARM).

Martha Chow, Senior Analyst, Physical Infrastructure (PI)

Philip G. Farah, Assistant Director, ARM

John Forrester, Assistant Director, Financial Markets and Community 

Investment

Ashley M. Grant, Senior Analyst, ARM

Hayden Huang, Senior Analyst, ARM

Eric M. Larson, Senior Analyst, ARM

Maria McMullen, Visual Communication Analyst, Forensic Audits and 

Investigative Service

Courtney LaFountain, Senior Economist, ARM

Harold Podell, Assistant Director, ARM

Carl Ramirez, Assistant Director, ARM

Kiera T. Reifschneider, Senior Analyst, ARM

Edward Staples, Communication Analyst, PI

Walter Vance, Assistant Director, ARM

Susan Zimmerman, Assistant Director, PI

Key engagement support, including arrangements for the forum, was 

provided by Juanita A. Aiken.

Appendix IX: GAO Contacts and Staff 

Acknowledgments

GAO Contacts

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