Putting Big Data to Work in Government: The Case of the United States Border Patrol

• Published date: 01 March 2022
• Author: Stephen Coulthart,Ryan Riccucci
• Date: 01 March 2022
• DOI: http://doi.org/10.1111/puar.13431

Research Article

280Public Administration Review • March | Apri l 202 2

Abstract: Investigating how the public sector adopts technologies to process and analyze very large datasets is crucial

for understanding governance in the digital age. The authors of this article examine a large government agency, the

United States Border Patrol (USBP), an organization that is in the early phases of building big data capabilities. They

argue the wide-scale adoption of big data analytics will require trial-and-error processes coordinated by organizational

leadership in partnership with front-line employees who make the technology relevant to their needs in the field.

Absent engagement from both levels, organizations like USBP that face significant barriers to adoption (e.g., limited

data science expertise) will struggle to leverage data at scale. The authors also extend the literature on big data in the

public sector and provide a rich description of how factors, such as organizational leadership and resources, impact the

innovation process.

Evidence for Practice

• Public sector organizations that seek to leverage big data face many obstacles, ranging from how to craft data

governance standards to cultural resistance, among others.

• In organizations with limited formal data science expertise leaders should tap into informal knowledge

networks of employees.

• Leaders should institutionalize “skunk works” teams—small groups of employees with the freedom to

experiment with new big data technologies.

In recent years, public administration scholars

have discussed the risks and benefits that arise

from governments using large-scale datasets for

decision-making and service provision (Kim, Trimi

and Chung2014; Lavertu2016; Mergel etal.2016).

However, scholars note that governments are only

beginning to leverage big data and are lagging behind

the private sector (Desouza and Jacob2017). Surveys

of public sector agencies support this observation

and suggest most are collecting large amounts of data

but not integrating it into their regular operations

(Columbus2018; Microstrategy2020).

Many government agencies will adopt big data

analytics in some form in the future. This raises an

important question and the focus of this article: how

will public sector agencies adopt big data technologies

now and in the coming decade? Some preliminary

answers come from the literature. The adoption

process for complex innovations like big data

technologies will be slow and incremental (Torugsa

and Arundel2016) and will have to overcome a

variety of structural barriers, ranging from human

resource limitations to data governance issues (Kim,

Trimi and Chung2014; Morabito2015). To address

these barriers, organizations will likely engage in trial-

and-error processes (Borins2000), as they determine

how to best use the technology. Innovation requires

facilitation from organizational leadership but the

literature suggests that front-line workers will likely

play an important role in testing new information

communication technologies (ICT) like big data

analytics (Borins1998, 2014). However, despite

previous research, there is little primary source

data available that describes the process by which

governments come to not just collect but actually use

big data.

This article presents a case study of big data

innovation in an organization in the early phases

of using big data, the USBP. USBP is one of the

operational components of the United States

Department of Homeland Security (DHS) and is

charged with protecting thousands of miles of land

and coastal border. Desouza and Jacob(2017) identify

the most potentially data-driven public sector agencies

as “big data” organizations. USBP is an example of

this class of big data public sector agency, which

has access to vast amounts of complex data but has

yet to leverage it widely across the organization.

Putting Big Data to Work in Government: The Case of the

United States Border Patrol

Stephen CoulthartRyan Riccucci

University at Albany (SUNY)University of Arizona

Ryan Riccucci has held leadership

positions in the U.S. Border Patrol

overseeing intelligence and border security

operations. His career includes two

headquarter assignments in Washington

DC, working with stakeholders across the

homeland security enterprise on research,

development, testing, and evaluation of

innovative technology. He is currently

Deputy Patrol Agent In Charge of a large

station on the southern border and an

Adjunct Professor of Practice at the

University of Arizona College of Applied

Science and Technology.

Email: ryan.a.riccucci@cbp.dhs.gov

Stephen Coulthart is an associate

professor at the University at Albany in

the College of Emergency Preparedness,

Homeland Security, and Cybersecurity.

His research concentrates on intelligence

analysis and technology implementation

in homeland and national security

organizations. He is the lead editor of

Researching National Security Intelligence:

Multidisciplinary Approaches

(Georgetown

University Press) and a fellow with the

Truman National Security Project.

Email: scoulthart@albany.edu

Public Administration Review,

Vol. 82, Iss. 2, pp. 280–289. © 2021 by

The American Society for Public Administration.

DOI: 10.1111/puar.13431.