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Politics and Governance (ISSN: 2183–2463)
2018, Volume 6, Issue 4, Pages 29–38
DOI: 10.17645/pag.v6i4.1565
Article
Big Data under Obama and Trump: The Data-Fueled U.S. Presidency
Barbara Trish
Political Science Department, Grinnell College, 50112 Grinnell, USA; E-Mail: trish@grinnell.edu
Submitted: 2 May 2018 | Accepted: 28 September 2018 | Published: 21 November 2018
Abstract
The much-heralded use of data, analytics, and evidence-based decisions marks the U.S. presidency, wherein many pro-
cesses and decisions are structured by the analysis of data. An approach with historical precedent, reliance on data was
prominent under Obama, and is even under Trump, despite signals to the contrary. This article examines three cases from
the Obama era: microtargeting in electoral campaigns, performance management in government, and signature drone
strikes employed by the national security apparatus. It also reflects on the early Trump administration. The processes de-
scribed are highly dependent on data, technically big data in two instances. The article examines the cases both on their
own terms and in the context of a critical lens that directs attention to the political economy of the data. The analysis helps
unpack the allure of data and analytics as well as the challenges in structuring an environment with a measured approach
to data and big data, which would examine both their potential and drawbacks.
Keywords
analytics; big data; data; drone strikes; evidence-based; microtargeting; Obama; performance management; president;
Trump
Issue
This article is part of the issue “Big Data Applications in Governance and Policy”, edited by Sarah Giest (Leiden University,
The Netherlands) and Reuben Ng (National University of Singapore, Singapore).
© 2018 by the author; licensee Cogitatio (Lisbon, Portugal). This article is licensed under a Creative Commons Attribu-
tion 4.0 International License (CC BY).
1. Introduction
“Evidence-based” is the 21st century coin of the realm,
with broad, seemingly unbounded applicability. Prac-
tices relying on evidence, their counterpart “data-based
decisions”, along with the tracking, data, and analytics
that fuel all have infused the public realm, private lives
and all areas between. Presidential politics in the U.S. is
no exception.
Reliance on data, in some cases technically “big data”,
marks the contemporary presidency to the extent that it
has become the default approach, part of its institutional
DNA. In some measure, this mirrors the broad progres-
sion of thought and practice that extends beyond the nar-
row scope of the U.S. presidency, from politics and gov-
erning generally, to commerce, sports, and all variety of
enterprises that value efficiency, either as means of de-
ploying resources or as an ultimate goal. But for U.S. pres-
idents and their administrations, reliance on data has at-
tendant advantages, quite apart from the operations and
decisions that unfold—and these serve to deflect atten-
tion from problems, including difficult ethical challenges,
that accompany the data-driven presidency. Because of
this, the presidency needs the rare combination of exper-
tise and detachment to yield effective decision making,
in order to weigh the traps and biases associated with
this world of data against the advantages.
This article focuses primarily on the role of data
in the Obama administration, in structuring processes
and decision-making, with cases drawn from three dif-
ferent domains—presidential selection, internal gover-
nance, and tactical national security decision-making.
Early indications for the Trump presidency suggest con-
sistency in the role of data, despite some signals to the
contrary. Taken together, these cases demonstrate how
reliance on data is buttressed by recurring calculations
that emphasize efficiency, and in some cases a private
sector that provides the data, at an extreme even extract-
ing data from individuals without compensation. In other
words, the political economy of data in the presidency
helps explain its modern allure—and recognizing it can
also inform prudent action in the future.
Politics and Governance, 2018, Volume 6, Issue 4, Pages 29–38 29
2. Epistemological and Political Roots
The turn to data in the U.S. federal executive is consistent
with early twentieth century ideas, both epistemological
and ideological. Emphasizing data has roots in logical pos-
itivism, which envisioned knowledge as the result of em-
pirical evidence. In the U.S., the ideological counterpart
to positivism was turn-of-the-century Progressivism, valu-
ing information and expertise as tools to disrupt the stran-
glehold of moneyed interests and patronage-fueled po-
litical parties on political power (Greider, 1992; Orren &
Skowronek, 2017). But as the century advanced, informa-
tion and expertise in the domain of the executive became
vehicles not for disruption, but for leadership by the
president in the U.S. separated system, with a massively-
expanded state apparatus. In other words, data and ex-
pertise became instrumentally valuable in politics.
Like the political world, the academy pivoted to em-
brace empirical evidence. About when Progressive po-
litical sentiment took hold, reformers in the American
academy moved to establish a distinctive approach to
the study of politics and government, shedding “the
legalist and theoretical way in which political life was
studied in the European academy” (Susser, 1992, p. 4).
The result was a new discipline of political science, at
times with some academics inserting themselves into the
rough and tumble of politics. But the more pervasive
quality of this new approach—which was fully formed by
the 1960s—was its social-scientific orientation, emulat-
ing the scientific model and placing a premium on empir-
ical, especially quantitative, evidence.
Against this backdrop, contemporary applications of
data in the U.S. presidency—that is, reliance on evidence-
driven practices and data—are not fundamentally new.
Indeed, well before “analytics” and “big data” emerged,
there was a strong element of data-based politics in
the U.S., extending over the nation’s entire history and
with remarkable scope. From census data informing
allocation of congressional representation starting at
the founding, to data-fueled economic projections man-
dated by the New Deal, and even to Ronald Reagan-as-
president using polling data to refine his rhetoric, the
historical examples are abundant. However, politics and
governing in the U.S. has reached a critical juncture, with
reliance on data so pervasive that it’s difficult to imagine
anything but; it has become the default choice, the go-to
solution for decisions, management and administration.
With this in mind, the following section describes how
data are employed in three different domains of the con-
temporary U.S. presidency, beginning with campaign pol-
itics, marked by what can justifiably be called big data.
3. Data and Big Data under Obama
3.1. The Data Science of Campaigns
While the road to the White House had been paved with
data and analytics for some time, the near obsession
of Barack Obama’s two campaigns with evidence-based
practices represents a difference in kind. What’s more,
the campaigns’ successes impelled the wide diffusion of
the data-centric campaign model. Even Donald Trump,
who conveyed skepticism about data—at times eschew-
ing it—subscribed to fundamentals of a data-driven cam-
paign model.
The predominant narrative of both Obama’s 2008
nomination and general election wins emphasized that
the campaign’s data-driven operation successfully mobi-
lized voters, especially new ones, to the polls. In 2008,
a ground game flush with money fueled a sophisticated
data-rich field operation, enhanced by online capacity
which included new platforms to engage voters. The
2012 addition to the narrative emphasized that the re-
election campaign was metric-driven and informed by
the insights of social scientific research. Both campaigns
fundamentally ran on data, not unprecedented in ap-
proach, but certainly in scope.
The data at the heart of mobilization efforts are voter
lists, used by campaigns to identify potential supporters
and mobilize them—through direct contact—to the polls.
These basic lists are longstanding, in fact the byproduct
of the early Progressive Era introduction of voter regis-
tration. Ironically, the information collected by this turn-
of-the-nineteenth-century reform, meant to weaken po-
litical parties, became the raw material for the mobiliza-
tion efforts of the parties and their candidates. Before
the advent of polling, these lists provided a rare portrait
of relative party strength among the electorate as well
as measures—like party affiliation and demographics col-
lected by the state—that could inform mobilization ef-
forts (Hersh, 2015, p. 49).
Obama’s voter contact data were simply an advanced
version of those early lists, but digitally enhanced and
readily operational through a user interface. The cam-
paign used “VoteBuilder”, the Democratic Party’s propri-
ety data, accessed through a user interface purchased
from a left-leaning for-profit, NGP-VAN. VoteBuilder lists
offer—at their core—the same type of information that
in a prior era a party agent might have retrieved from
official voting records, namely voting history and demo-
graphics of the registered vote. Now these lists are aug-
mented to include additional individual-level informa-
tion about the voter, drawn from a number of sources,
including commercial firms, as well as parties and cam-
paigns themselves which glean information from field
staff and volunteer interactions with voters.
By 2008, the use of data like these was common,
not just for presidential campaigns. So too were for-
ays in microtargeting, procedures to further augment
data by means of statistical analyses, a process that
had been evolving over the prior decade. Microtarget-
ing techniques produce synthetic measures of voter
characteristics—“model scores”—by means of predic-
tive analytics, integrating the results of large-n survey
data with the augmented voter file. The model scores
serve as criteria for a particular voter contact effort, tar-
Politics and Governance, 2018, Volume 6, Issue 4, Pages 29–38 30
geted to specific individuals. Indeed, the “micro” aspect
of this enterprise involves data analysis at the individual
level, with targeted contact efforts similarly aimed. It is,
in effect, an algorithmic decision-making process, though
the practice began well before the term was applied to it.
Obama in 2008 engaged in a virtually uninterrupted
process of modeling and refining the data, and then
modeling again (Issenberg, 2012a). Progressive data gi-
ant Catalist, another for-profit firm partnering with the
Obama effort, extended the data beyond the traditional
cache of registered voters to include unregistered vot-
ers, not represented in traditional voter files. The Catalist
data offering, according to the firm’s own accounts, num-
bered 265 million cases, reflecting approximately the uni-
verse of voting-age adults in the U.S.
Conventional wisdom holds that the prowess of
Obama’s resource-flush 2008 campaign contributed
heavily to his win. But by 2012, the reelection efforts
would be enhanced significantly by an evidence-based
understanding of the effectiveness of voter mobilization
techniques, drawing heavily from experimental research
with ties to the social-scientific community. The 2012
campaign, gripped by a culture of experimentation, em-
ployed evidence-informed programs (IEPs) not only in di-
rect voter contact protocols used in the field, but also in
digital and fundraising campaigns (Issenberg, 2012b).
The 2012 campaign was structured to give data and
analytics a strong voice. At the Chicago Obama headquar-
ters, a team of fifty analytics professionals worked out of
the “the cave”, with a direct line of reporting to the Chief
Innovation and Integration Officer, who reported directly
to the campaign manager. Data and technology depart-
ments constituted an estimated 30–40% of headquar-
ter staff, and “[a]nalytics was the breakout star of 2012”
(Engage, 2012). The data-focused structures and prac-
tices were complemented by the attitudes and norms of
personnel, including senior staff with a willingness “to lis-
ten to numbers people rather than consultants acting on
old-fashioned political intuition” (TechPresident, as cited
in Engage, 2012.)
These 2008 and 2012 campaigns represented state-
of-the-art data operations—in fact “big data”, at least
in the sense that they integrated data from a variety
of sources, augmenting them repeatedly with newly-
acquired information. The data even approached an
N=all (Mayer-Schöenberger & Cukier, 2013) quality, in
Catalist’s case approximating the universe of the voting
age population. But not all data that fuel the presidency
are big in this sense. In fact, data collected under man-
agement protocols are rather conventional, despite be-
ing part of a monumental data-collection enterprise.
3.2. Tracking the Executive with Performance
Management
Performance management in the abstract focuses on
how efficiently and effectively the executive branch ad-
ministers the programs of the federal government. It’s
data-driven management, which—much like the use of
data in campaign politics—has become systematic and
elaborate over time, especially since the 1990s. The data
at the heart of performance management as practiced
in the Obama years were collected by agencies, permit-
ting judgment of the extent to which the outcomes of
their activities met the goals of the programs and of
the administration.
Most observers trace the development of perfor-
mance management to the Bill Clinton era, though the
impetus to employ management to consolidate the pres-
ident’s leadership of the executive branch came ear-
lier, during the Richard Nixon administration. Nixon, in
an effort to harness the discretion of the federal agen-
cies and to ensure agreement across the administration
with his policy priorities, layered on management re-
sponsibilities to the existing budget office, creating the
Office of Management and Budget (OMB) in 1971. While
OMB offered an institutional arm to the president for
management, it wasn’t until later, in 1993, that perfor-
mance management as a systematic approach was cod-
ified by Congress in the 1993 Government Performance
and Results Act (GPRA), requiring federal agencies to en-
gage in strategic planning every five years and undertake
annual performance reviews. And then in 2010, Congress
passed the GPRA Modernization Act (GPRAMA), which
revised specific expectations for performance manage-
ment, including movement from annual to quarterly re-
views/reporting, making performance management an
ongoing process. This world is data-heavy, requiring
that agencies establish goals and track progress toward
achieving them. The initial statute instructed agencies
to develop “quantifiable and measurable program tar-
gets” as well as “outcome measures”, metrics by which
the real-world success of the programs would be judged
(Harris, 2015, pp. 105–106).
In its rhetoric, the Obama administration embraced
performance management. It built onto the efforts of
the Bush administration (Jochum, 2009), which had it-
self prioritized performance management and had de-
vised and touted its Performance Assessment Rating
Tool (PART), a quantitative assessment of goals and
performance used by over 200 federal programs, esti-
mated to account for 20% of the federal budget. Jeff
Zeints, the acting director of OMB at the start of the
Obama administration, described GPRA and Bush’s PART
as important starting points for the new administra-
tion. Ratcheting up the hype, Obama’s performance
management was spearheaded by “performance guru”
Shelley Metzenbaum, who was responsible for develop-
ing www.performance.gov, a tool to both articulate the
administration’s approach and to provide access to copi-
ous reports and reviews filed under the program.
A wide variety of data is collected under performance
management protocols, with each agency establishing
annual performance goals for its mission areas, identify-
ing metrics for assessing the goals, and then reporting
the actual performance. Much of the data is straightfor-
Politics and Governance, 2018, Volume 6, Issue 4, Pages 29–38 31
ward, the product of counting and tracking. For example,
the U.S. Department of Agriculture (USDA) reports the
number of wetland acres (in millions) restored by the
Conservation Reserve Program (CRP), a measure com-
piled from CRP contracts. But other measures entail es-
timation procedures, requiring technical expertise and
skill, with results that may be marked by irregularities.
Consider USDA’s metric for enabling access to healthcare
facilities for rural areas. Measured as the percentage of
people who are provided access to new and/or improved
essential community facilities, this metric involves esti-
mation of a geographic service area for each facility and
the population within it, both difficult to assess (USDA,
2013). Messiness aside, this is not “big data” by any con-
ventional definition, though monumental in scope, since
every agency collects measures to assess a large number
of goals. To get a sense of the magnitude, consider the
2013 performance management reports for Commerce
and USDA alone, each listing over forty metrics, while
Housing and Urban Development (HUD) reported more
than fifty. Twenty-four agencies are subject to this data-
driven management process.
Data collected by the General Accountability Office
(GAO) offer a valuable window to the experiences of
agency managers in performance management, and the
2013 Federal Managers Survey reveals a mixed picture
about the implementation of performance management.
Administered in late 2012 and early 2013, the survey
sampled a population of some 148,000 managers and su-
pervisors in agencies that undertake performance man-
agement as specified by the two statutes, producing ap-
proximately 3,000 usable responses. The survey found
that a full 48.8% had never even heard of the GPRAMA.
Just under one-fifth (18.8%) of managers suggested that
performance data were not easily accessible to them,
while close to one-third (31.2%) felt the data were
not easily accessible to their employees. When asked
whether data were formatted in a manner easy to use,
27.7% reported they were not, and 30.2% expressed that
they did not have sufficient analytic tools to collect, ana-
lyze and use the data (GAO, 2013). These indicators can
be taken as glass half-full or half-empty, but regardless
suggest that the reality of data-based performance man-
agement under Obama might not have met the promise
of the accompanying build-up.
That said, performance management is transparent,
with not only the data publicly available, but also the
perception of the practitioners revealed systematically.
Furthermore, the data are quite traditional, metrics with
which to judge performance and, presumably, instruct
decision makers. The following example offers a dramatic
contrast, drawn from the realm of national security, in-
volving the specification of targets for military attack.
3.3. High-Stakes, Covert Signature Strikes
To base decisions about whom to attack on evidence is
unsurprising. But the Obama administration employed a
controversial technique to target individuals or groups
of individuals for drone strikes when they bore the
characteristics—the “signature”—of those likely to be
engaged in terrorist activity. In contrast to “personality
strikes”, in which the U.S. targets known terrorists (Zenko,
2012a), signature strikes are based on patterns of behav-
ior indicative of terrorists, even if the individual target is
not known to be a terrorist. President George W. Bush
was the first to authorize such signature strikes (Zenko,
2013, p. 12), though this came at the end of his tenure.
These strikes are shrouded in secrecy, extreme even
compared to the typical opacity of national security.
Micah Zenko notes that signature strikes have not been
acknowledged officially. “[N]o U.S. Government official
has ever acknowledged the practice of signature strikes”.
Nor has any official “described the practice, justified it,
or explained how it is consistent with the…laws of war”
(M. Zenko, personal communication, 26 April 2018). Even
more so, information about precisely what data and an-
alytical tools inform the targeting is sketchy, with what
is known owing largely to Edward Snowden’s June 2013
leaks of National Security Agency (NSA) data.
Snowden revealed that the NSA mines metadata, es-
sentially the trail that follows digital and cell-phone com-
munication (Hu, 2017, p. 235). Even absent the content
of the communications, these metadata allow the an-
alyst, most likely relying on a combination of machine
learning and network analytical techniques, to identify
potential terrorists by their patterns of connections to
others, including to known terrorists. Journalist Glenn
Greenwald emphasizes that the validity of the data is
not confirmed by traditional techniques, like engaging
“operatives or informants on the ground” (Scahill &
Greenwald, 2014). This threat of imperfection is cap-
tured in the oft-repeated comment, attributed to an
unnamed State Department official: “[T]he C.I.A. sees
‘three guys doing jumping jacks’…[and] thinks it is a ter-
rorist training camp, [adding that those] loading a truck
with fertilizer could be bombmakers—but they might
also be farmers” (Becker & Shane, 2012).
Making decisions based on incomplete evidence is
not new to the world of military tactics. Zenko (2012b)
recounts an anecdote conveyed by General Colin Powell
about his early history in Vietnam:
I recall a phrase we used in the field, MAM, for
military-age male. If a [helicopter] spotted a peasant
in black pajamas who looked remotely suspicious, a
possible MAM, the pilot would circle and fire in front
of him. If he moved, his movement was judged evi-
dence of hostile intent, and the next burst was not in
front but at him.
The pilot in Powell’s account makes a judgment based
on the available evidence, flawed as it might be. The
drone strike likely reflects similar judgment—but with
split-second processing, the application of algorithms
to data, perhaps even real-time geo-location data. No-
Politics and Governance, 2018, Volume 6, Issue 4, Pages 29–38 32
tably missing in this endgame is direct human judgment.
Hu (2017, p. 231) asserts that the absence of human
judgment distinguishes this big-data approach from the
“small-data” methods of the past, which relied on human
perception and human decision making. The other sig-
nificant element is that the algorithmic process yields
a quantitative measure of likelihood that a person is a
terrorist—or that a targeted geographic space would en-
compass terrorists.
Critics find especially concerning that the decision
to kill is based on a likelihood generated by an algo-
rithm. But perhaps more problematic is the high civil-
ian death toll associated with drone strikes. Data from
New America Foundation (NAF), Long War Journal (LWJ),
and The Bureau of Investigative Journalism (TBIJ), mostly
from 2004–2012, estimate over 400 drone strikes in
Pakistan, Yemen and Somalia, with approximately 12%
(401) citizens among the 3,430 killed (Zenko, 2013, p. 13.)
Advances in technology—drones, the widespread
use of mobile technology, as well as the ability of the NSA
and CIA to track and analyze the exhaust—have opened
the door for signature strikes. And while the details of
the data and analytics that undergird them escape public
scrutiny, it’s clear that this is an executive branch big-data
enterprise, not just in terms of volume, but also in the
substitution of machine judgment for human judgment.
4. Plus Ça Change…in Early Trump?
Donald Trump, despite being an unconventional candi-
date and president, over the two years of his administra-
tion has signaled that in many respsects he follows in the
footsteps of his predecessor regarding the use of data.
This orthodoxy, however, is especially notable given that
the president has at times vocally eschewed evidence-
based practices. Plenty of time remains for the Trump
approach to data to take shape, but at this juncture it
looks like rhetoric does not always mesh with actions.
Candidate Trump expressed disdain for campaign
data, calling it “overrated” (Vogel & Samuelsohn, 2016b),
but then assembled a rather conventional voter data
and mobilization effort, admittedly smaller and flying un-
der the radar more so than his predecessor’s (Vogel &
Samuelsohn, 2016a). By early 2016, the operation was
staffed by two former Republican National Committee
(RNC) operatives, low key in orientation, but with experi-
ence working with the RNC’s Voter Vault, the counterpart
to the Democratic National Committee’s VoteBuilder,
which had fueled Obama’s and—eventually—Hillary
Clinton’s campaigns. Largely undetected, the Trump cam-
paign assembled “Project Alamo”, an ambitious digital
database that aided in online and offline targeting, strate-
gic decisions and voter mobilization—as well as a dose
of voter demobilization, attempting to limit the Hillary
Clinton vote (Green & Issenberg, 2016).
And then there was Cambridge Analytica. Trump
turned to the U.K.-based data and analytics firm, which
was later revealed to have misappropriated Facebook
data for the purpose of its “psychographic modeling”
activities. The New York Times, working with London’s
Observer, reported that Cambridge Analytica, with close
ties to central figures in the Trump orbit like Steve
Bannon and the Mercers, acquired personal informa-
tion on Facebook users by means of an academic, who
claimed the data were for the purpose of academic re-
search (Rosenberg, Confessore, & Cadwalladr, 2018).
While the tangled web of the Facebook data breach
and possible connection to Russian collusion remained
unresolved by late-2018, finance reports confirm that
Cambridge Analytica was a player in the Trump data
operation. Federal Election Commission (FEC) records
from 2016 compiled by the Center for Responsive Politics
(CRP) show disbursements of $5.9 million from the
Trump campaign to the data firm. Notably, these dis-
bursements were dwarfed by the $87.8 million paid to
Giles-Parscale, the San Antonio digital marketing firm
that was responsible for Project Alamo.1
Trump’s embrace of data in the campaign phase is
replicated in management of the administration, and
there’s even sign of the same Obama-era promotional
voice. However, the ends to which Trump’s performance
management are directed are distinctively-Trump: to
limit the reach of the federal government. Of course,
this same goal is advanced by the record-number of
key appointed positions in the executive branch un-
filled well into the term (Kruzel, 2018) and the marginal
shrinking—through attrition—of the size of the civilian
work force (Jacobson, 2018). Not surprisingly, President
Trump’s approach to performance management, while
similar in its practices to Obama’s, aspires to a business
model, envisioning the citizens as customers and hold-
ing federal employees accountable. Margaret Weichert,
Deputy Director for Management at OMB, sees a cen-
tral role for data in this enterprise, with “drivers” of the
agenda being information technology, data accountabil-
ity/transparency, as well as a modern workforce (Clark,
2018, p. 16). But if not for prototypically Trump-like
messages signaling disdain for career bureaucrats, advo-
cating streamlined processes to remove poor perform-
ers, and pushing back at unions (Katz, 2018, p. 7), this
technology-driven emphasis might well have come from
Trump’s predecessor.
Similarities between Obama and Trump regarding
use of data extend to drone strikes as well. The day af-
ter inauguration, Trump authorized the use of strikes
in Syria though departing from Obama national secu-
rity processes, which reserved the strike capacity for the
Pentagon. Under Trump, the CIA both collects the intelli-
gence that fuel the targeting and carries out the strikes.
The turf maneuvering between the Pentagon and CIA
could have real ramifications, since they reportedly em-
ploy different standards of algorithmic certainty, with
the CIA’s “near certainty” decision-rule more demand-
ing than the Pentagon’s “reasonable certainty” (Lubold
1For more information see www.opensecrets.org/pres16/expenditures?id=n00023864
Politics and Governance, 2018, Volume 6, Issue 4, Pages 29–38 33
& Harris, 2017). At the same time, empowering the CIA
to conduct the strikes removes the process even more
so from the scrutiny of congress and the courts, with
CIA activities, relative to the Pentagon’s, shielded more
from view. Put differently, the data and analytics may
remain the same, but process differences could have a
real impact.
Still, in other areas that move beyond the cases ex-
plored in this article, Trump has taken aim at data. He
famously banned the Center for Disease Control (CDC)
from using terms like “evidence-based” and “science-
based” (Sun & Eilperin, 2017), removed data from the
website of the Environmental Protection Agency, and dis-
banded advisory councils that might challenge his own
beliefs about the climate and the economy. Furthermore,
Trump purged from the web the White House Visitor Log,
citing national security risks; the machine-searchable list
of visitors to the White House and the Eisenhower Exec-
utive Office Building (EEOB) had been made readily avail-
able under the Obama administration in the interest of
transparency and probably not much of a national secu-
rity threat. Not surprisingly, given signals on both sides
of the question, something of a debate still wages about
whether Trump carries on a “war on data”.
5. Theoretical Insight Regarding Data and the
Presidency
This article’s description of data and evidence-based ap-
proaches used in the U.S. presidency is necessarily in-
complete, dependent on a handful of cases, focusing on
one presidency with some insight into another. Indeed,
the portrait of the prominence of data in the presidency
is if anything modulated by these cases. Performance
management is a bit of a sleeper, and the data-driven
campaign model, since showcased by Obama, has been
diffused widely, across parties, down the ballot and even
to campaigns in different international settings, so much
so that even it is a little passé.
But Obama did deploy data in far more contexts than
described in this article. Technology reporter Nancy Scola
dubbed him the “big data president”, with some eighty-
five big data projects ongoing in his time in the execu-
tive (Scola, 2013). Under Obama’s watch, the National
Institutes of Health (NIH), worked to facilitate delivery
of healthcare targeted to a patient’s unique genetic
makeup, with the goal of collecting data from one million
volunteers (NIH, n.d.). The Justice Department (2016),
in conjunction with the Whitehouse and law enforce-
ment agencies, released data on police actions, “to in-
crease transparency and accountability and build trust
with...communities”. And as documented—along with
other projects—by the Executive Office of the President
(2012), “Mission-oriented Resilient Clouds” would detect
and respond to security threats in cloud computing.
The allure of these programs—as well as that of the
cases described in this article—is readily apparent, given
their stated ambitions. And precisely because of natural
allure we should proceed warily, to remember that a crit-
ical lens would caution against ignoring the underlying
assumptions and power relationships that undergird the
processes related to data. With this in mind, this article
returns to the three cases, introducing a focus on the po-
litical economy of the data.
5.1. A Presidential Data “Revolution”
At first blush, the data-heavy model of campaign poli-
tics, performance management and drone strikes under
Obama all entail a conceit that the practices are democ-
ratizing. After all, a microtargeting process resulting in
direct voter contact—campaign personnel reaching out
on the phone and at the door—is a decided departure
from the mass media model of campaigns that had be-
come prominent over the final decades of the twentieth
century. The democratic nature of performance manage-
ment in the executive is a little different, but it entails
the ability of the electorate, as mediated by represen-
tatives in Congress, to hold the vast unelected bureau-
cratic state accountable. Even signature drone strikes, ob-
scured from the view of the public and most elected offi-
cials, arguably have an attendant democratic sense. Pro-
tecting the U.S. military from ground combat, as uncer-
tain as that is up against non-state opponents in the fight
against terrorism, is democratically significant in that mil-
itary personnel are disproportionately drawn from lower
economic classes.
In each of these cases, the data and analytics op-
erations exploited new technologies. Granted microtar-
geting was around long before Obama (Malchow, 2003),
and even the basic architecture of the data employed by
Obama was already in place (Hersh, 2015; Kreiss, 2016).
But the extent of processing power and the servers, espe-
cially in the Catalist world of data mining and modeling
with essentially a universe of cases, were fundamentally
new and characteristics of big data. In contrast, the tech-
nology of data collection in performance management
was not cutting edge, but the dissemination of data was.
The Obama Administration prioritized the distribution of
data, with its performance.gov portal, along with the her-
alded data.gov portal, which in late October 2015 offered
some 189,000 data sets and as of late 2018 over 300,000
data sets to the public. As for signature drone strikes un-
der Obama, it wasn’t so much the new tool of drones,
because unmanned aerial vehicles have a long history. It
was the ability to equip the operation with digital and
mobile data, abundant on the ground, then mined, inte-
grated and analyzed to inform algorithmic decisions.
These three data applications, beyond holding demo-
cratic allure and the draw of new technology, have the
added appeal of secondary instrumental benefits. Re-
garding drones strikes, a variety of polling data shows
that the American public is not particularly critical of
them, and presumably successful strikes, not putting
American personnel directly at risk, secures stronger
public support. A similar byproduct accompanies perfor-
Politics and Governance, 2018, Volume 6, Issue 4, Pages 29–38 34
mance management. Early applications of the practice
did result in an upswing of trust of government (Kamarck,
2013). And political campaigns realize multi-faceted in-
strumental benefits from data and metric-driven efforts.
The metrics are used to motivate volunteers and staff
and to hold them accountable. Even more so, they serve
as concrete and persuasive evidence for donors of the
impact and promise of the campaign in the absence of
more definitive measures like election outcomes.
It may only be a slight exaggeration to suggest these
uses of data in the presidency hold some apparent revo-
lutionary potential. Not only do the data contribute to a
desired outcome, but they purport to even change usual
power dynamics—offering voice to those not typically
heard, as well as a new role for or protection of the av-
erage American. But this same revolutionary potential
makes it easy to glance away, ignoring inherent biases
and threats associated with data in general and more
specifically in the presidency.
5.2. The Political Economy of Presidential Data
Three common threads are woven through the cases ex-
amined, and they expose concerns that deserve to be ad-
dressed in both decisions to turn to data and evaluation
of success. The first of these is the uncritical acceptance
of efficiency as a goal, most directly borne out in perfor-
mance management, with its emphasis on outcomes and
the use of metrics to judge success.
The approach to performance management manifest
in government used by of Obama—Trump too—and pre-
decessors was adopted initially from the private sector
in the 1970s. The “New Public Management” aspired to
“create market like conditions within the government…to
run them ‘more like a business’” (Muller, 2018, p. 51).
But despite numerous shortfalls of this market applica-
tion in governing—like metrics distorting incentives and
representing overly simplistic conceptions of what mo-
tivates personnel—the practice was well-entrenched by
the late 1990s (Muller, 2018, p. 55) and it continues to-
day. Muller (2018) finds that metrics that drive manage-
ment often operate perversely, drawing attention to only
things that can be measured and even stifling innova-
tion. There is, however, even a more fundamental con-
cern with this business model, in that it poses efficiency
as a preeminent goal.
Efficiency is a common metric employed in the pri-
vate sector, but its adoption in politics and governing
may be at the expense of other things valued. Microtar-
geting in campaigns, for example, is premised on the effi-
cient deployment of resources to mobilize and persuade
enough voters to win an election. But the flip side of tar-
geting voters is that some are ignored, deemed either
lost causes or even certain supporters, neither warrant-
ing attention by the campaign. And while this may effec-
tively carry a candidate across the line in a given election,
it represents a narrow, short-term focus that may not
contribute to building an electorate that will support the
party in the future. Sociologist Robert Merton called this
“the imperious immediacy of interests” (Merton, as cited
in Muller, 2018, p. 170, emphasis added), wherein indi-
viduals look only as far as the short-term consequences
of their action.
Zeynap Tufekci’s (2012) problem with the efficiency
in campaigns is a little different, namely that they will
succeed in efficiently engineering the electorate. Tufekci
is first concerned that the “scalpel” of microtargeting is
deployed in private, not subject to public scrutiny. But
the bigger problem is that it just may be effective, es-
pecially for well-financed campaigns with the resources
to devote to data and persuasive techniques. Even if not
effective—even if the data which guide the appeals are
flawed and replete with errors, as any staffer or volunteer
who has worked with these data knows—that campaigns
are treating the electorate as a target of their engineer-
ing efforts is itself a cause for worry.
Data enterprises that posit efficiency as a goal is
a first thread that runs through the cases. A second
thread is that the data-based presidency is inexorably
tied to a private sector that both supports and bene-
fits from it. The interface and sometimes the data that
the campaigns use are held in private hands. NGP-VAN
and Catalist, the left’s go-to data interface and source
of mined data respectively, along with thousands of
other paid vendors, constitute the for-hire network of
data professionals, many of whom move back and forth
between the campaigns and the party apparatus from
election to non-election seasons (Kreiss, 2016). It’s no-
table that the combination of the Democratic data and
the privately-held NGP-VAN interface, according to Kreiss
(2016), serves as a “robust piece of infrastructure that
the party’s technology ecosystem convenes around”. In
other words, the private data actually structure the party
organization. And in a related fashion, the dependence of
a political party and its campaigns on a small number of
private firms cannot help but affect where power rests
within the party organization, not necessarily with the
voter or the party elites, but with vendors.
Performance management, like the data and ana-
lytics in campaigns, is subject to a revolving door of
sorts regarding leadership. While not universal, a com-
mon pattern is that top personnel responsible for per-
formance management, and OMB directors as well, are
drawn from the private sector—or at least from those
with experience in the private sector. It’s also the case
that many of these management leaders return then
to the private sector after service. Admittedly, the ca-
reer of Shelley Metzenbaum, President Obama’s “perfor-
mance guru”, was more entangled with academia and
other governmental positions than the private sector.
But Trump performance management leader Margaret
Weichert demonstrates a clear trajectory into govern-
ment from the private sector.
The undercurrent of values and practices that inject
a market-based influence into data in the presidency ex-
tends to a third dimension as a well: the transaction
Politics and Governance, 2018, Volume 6, Issue 4, Pages 29–38 35
marking the exchange of data. This is evident in the data
used by campaigns and in signature strikes, with the first
involving an implicit transaction with the state and the
second with individuals, granted, many outside of the
boundaries of the U.S.
The campaign data originate in lists of registered vot-
ers compiled by the U.S. states. As a condition of voting,
individuals provide data to the state, but then private
firms like NGP-VAN offer user-friendly tools for working
with the data to parties and campaigns. Or in the case
of Catalist, the data are augmented through integration
with other sources, using algorithmic processes to add
synthetic measures (Hersh, 2015). In the abstract, these
data and tools, the likes of which are used by presiden-
tial candidates, represent an implicit transaction, one in
which data collected by the state and made available
at little cost is collected by businesses, then sold in a
repackaged form to political organizations. Of course,
compensating the intermediaries for the value added to
the data seems only right. Yet it introduces the question
of whether there is just compensation for the original
data provider.
For Phil Howard (2018) the answer is “no”, at least
with reference to the big social media players and politi-
cal mining firms like Cambridge Analytica, which extract
data with ease. Howard is concerned that the citizens
have no effective control over their data, which will be
used for political purposes. Among the mix of Howard’s
recommendation to put some degree of control back in
the hands of the public is that individuals should be able
to donate their data to “the civic groups, political parties,
or medical researchers they want to support” allowing
them to leverage their own data for political purposes.
This transactional sense of data emerges in Evgeny
Morozov’s (2017) analysis of artificial intelligence (AI) as
well. Morozov asserts that the compensation received
by individuals for the data that fuel AI research and
development—compensation that is nothing more than
access to a social network—is modest when considering
the price that government and individuals will pay for
products created by AI. Signature strikes invoke an ele-
ment of this same transactional logic, with a troubling ad-
dition. The data that fuel the strikes represent the digital
exhaust of users on the ground, snatched up by surveil-
lance operations. In this, access to internet and mobile
technology is the compensation for the user, which ad-
mittedly may be of substantial value. But that the data
are then deployed to target for the purpose of killing
individuals with only some stated degree of statistical
certainty, and that this practice captures innocent by-
standers as well, has an element of perversity to it. Ad-
mittedly, national security and covert operations are not
the same as AI enterprises, and it’s absurd to suggest that
those being surveilled should be better compensated for
their data. But this transactional calculus regarding sig-
natures strikes, just like those implied or described by
Kreiss (2016), Howard (2018) and Morozov (2017), at a
minimum, points to the merits of looking well beyond
the effectiveness of the data and processes as measured
by numbers of terrorist killed.
5.3. Moving Forward
This article has suggested that the world of data—
including big data—is borne out in the U.S. presidency, in
some cases accompanied by the buzz that this is funda-
mentally new, even to the point of revolutionary, poten-
tially disruptive of traditional power arrangements. But
the subtle irony is that viewed through a critical lens,
those traditional power arrangements may prevail, in
some cases enhanced by the perceived revolutionary po-
tential of the data and data-related processes used in
the presidency.
But one need not focus on the political economy
of data to identify ways in which the popular under-
standing of data and the potential they hold are entan-
gled with political and ethical concerns. Consider the
alarm generated by looking closely at algorithms, chal-
lenging the conceit that they are immune from preju-
dice. Cathy O’Neil (2016) demonstrates how algorithmic-
informed decisions can reinforce the existing biases of so-
ciety, that policing tools using predictive modeling carry
the appearance of objectivity but can be “tools of math
destruction”, perpetuating existing traditional class bi-
ases. What’s more, data journalism outlet ProPublica of-
fers a telling rejoinder to statisticians who judged as
fair the algorithms used in sentencing recommendations,
finding that when applied to actual people, the algo-
rithms systematically overestimate the threat of recidi-
vism for African American defendants and underesti-
mate it for White defendants (Caplan, Donovan, Hanson,
& Matthews, 2018).
Ethical concerns come into the picture typically
in ways subtler than the life/death calculations mark-
ing signature drone strikes, and because of this they
slip by undetected, especially when safety procedures
don’t intervene successfully. Cambridge Analytica’s mis-
use of data was facilitated by Facebook permitting aca-
demic Aleksandr Kogan to harvest its user data, despite
Kogan’s research proposal being rejected by his univer-
sity’s ethics board (Weaver, 2018). Even with a defini-
tive say by an Institutional Review Board, these bod-
ies tend to gravitate toward a legalistic review of pro-
posals, and—furthermore—are frequently ill-equipped
to tease out the ethics of big data (Metcalf, Keller, &
boyd, n.d.). It goes without saying that many of the de-
cisions regarding data escape scrutiny by experts tasked
with reviewing ethics, especially in the realm of politics
and government.
This is all to say that from a number of perspectives—
whether viewing data deliberately though a critical lens
or simply examining the current areas of concern re-
garding data and big data—it’s clear that the U.S. pres-
idency faces substantial data-related challenges. Despite
the occasional utterance of Trump that calls expertise
into question, the U.S. political system continues to value
Politics and Governance, 2018, Volume 6, Issue 4, Pages 29–38 36
information and expertise, both of which contribute to
the political capital of actors and institutions. And the
norms of science still prevail, indeed even with some
new role for social-scientific applications in the political
world. In short, there is no reason to believe that “data-
driven” is a passing phase. But it’s time to contemplate
what a measured approach to data would look like. To
be concrete, the goal should be to deploy data in effec-
tive and ethical ways, all the while alert to the biases and
shortcomings that underlay their collection and use. This
is no small task, especially in an environment that rou-
tinely prioritizes quick action over deliberation, particu-
larly on matters that may require extraordinary techni-
cal expertise, though sound and detached political judg-
ment as well.
Acknowledgments
The author thanks colleagues Michael Guenther (His-
tory), Janet Seiz (Economics) and Eliza Willis (Political Sci-
ence) for sharing their insight.
Conflict of Interests
The author declares no conflict of interests.
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About the Author
Barbara Trish is Professor of Political Science at Grinnell College (Grinnell, IA), where she also directs
the Rosenfield Program in Public Affairs, International Relations, and Human Rights. Her analyses of
U.S. politics have been published in scholarly journals, edited volumes and in the popular press.
Politics and Governance, 2018, Volume 6, Issue 4, Pages 29–38 38
