Big Data and AI – A transformational shift for government: So, what next for research?
| Author | Marc Esteve,Irina Pencheva,Slava Jankin Mikhaylov |
| Date | 01 January 2020 |
| Published date | 01 January 2020 |
| DOI | 10.1177/0952076718780537 |
| Subject Matter | Articles |
Public Policy and Administration
2020, Vol. 35(1) 24–44
Big Data and AI – A
! The Author(s) 2018
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transformational shift
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DOI: 10.1177/0952076718780537
for government: So, what
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next for research?
Irina Pencheva
Government Digital Service, Cabinet Office, UK
Marc Esteve
International Public Management, University College London, UK;
ESADE Business School, Spain
Slava Jankin Mikhaylov
Public Policy and Data Science, University of Essex, UK;
Essex County Council, UK
Abstract
Big Data and artificial intelligence will have a profound transformational impact on
governments around the world. Thus, it is important for scholars to provide a useful
analysis on the topic to public managers and policymakers. This study offers an in-depth
review of the Policy and Administration literature on the role of Big Data and advanced
analytics in the public sector. It provides an overview of the key themes in the research
field, namely the application and benefits of Big Data throughout the policy process, and
challenges to its adoption and the resulting implications for the public sector. It is argued
that research on the subject is still nascent and more should be done to ensure that the
theory adds real value to practitioners. A critical assessment of the strengths and
limitations of the existing literature is developed, and a future research agenda to
address these gaps and enrich our understanding of the topic is proposed.
Keywords
Big Data, literature review, policy process
Corresponding author:
Marc Esteve, University College London, 29 Tavistock Square, London WC1H 9QU, UK.
Email: marc.esteve@ucl.ac.uk
Pencheva et al.
25
Introduction
Big Data is thought to have a global reach and exert a fundamental structural
impact throughout society (McNeely and Hahm, 2014). While the use of data in the
public sector is not new, the potential and actual use of Big Data applications
affects aspects of the theoretical and practical considerations of decision-making
in the public sector (Giest, 2017). This is driven not only by the data revolution but
also the accompanying development of advanced analytics. From the practitioner
perspective, this is summarised in a speech by John Manzoni, Chief Executive of
the UK Civil Service and Permanent Secretary of the Cabinet Office on Civil
Service Transformation. Civil service is undergoing a transformation with robotics
and automation changing the provision of public services. There is a requirement
to embrace big data and technology that is reshaping the workforce. The challenge
currently is to better use citizen data for improvement of public services, target who
needs services more specifically and ‘tailor those services more accurately’
(Manzoni, 2018).
At the most basic level, Big Data is about the volume of information, a variety
of the different data sources and types (structured and unstructured), and the vel-
ocity – namely the speed of creation, storage and dissemination of data, often in
real-time (Einav and Levin, 2014). The three ‘V’s is a term coined to distinguish Big
Data from conventional data (Eaton et al., 2012). However, different stakeholders
attribute different meanings to the concept (Stough and McBride, 2014). Some see
it as ‘a cultural, technological, and scholarly phenomenon’ (Boyd and Crawford,
2012: 663); others as ‘a multidimensional concept embracing technology, decision
making, and public policy’ (McNeely and Hahm, 2014: 304). The difficulty of
defining such a broad concept has led to several attempts of clarifying its real
meaning. As a result, some authors have proposed to include concepts such as
veracity, validity, value, and viability – 4Vs (Kimble and Milolidakis, 2015).
Although the use of these new concepts has created some controversy, as they
do not refer to proportional dimensions of big data, but instead they can refer
to all types of data. The very definition of the concept is not the only challenge
faced by governments and policymakers; the difficulties ranging from governance
and ethical concerns, to structural and organisational resource limitations when
dealing with Big Data need to be considered as well (Mergel, 2016; Phillips, 2017;
Youtie et al., 2017).
Advanced analytics is related to a more general concept of artificial intelligence
(AI) and its underlying technologies. Over the last decade, there have been dra-
matic advances in core AI technologies like machine learning, natural language
processing, virtual agents, and computer vision (Russell and Norvig, 2009).
The early promise of AI was largely viewed in terms of providing decision support
for public managers (e.g. Hadden, 1986; Hurley and Wallace, 1986; Jahoda, 1986;
Masuch and LaPotin, 1989). Latest advances in AI allow computers to learn
from past experiences and understand the world through a hierarchy of concepts
(Goodfellow et al., 2016) that can lead to automation of tasks (Bailey et al., 2016;
26
Public Policy and Administration 35(1)
Barth and Arnold, 1999). While many early promises of AI went unfulfilled, recent
successful applications represent the third wave of AI that started from around
2006 (Goodfellow et al., 2016). A key contributing factor to increasing maturity of
AI technologies and the viability of AI application to public policy and adminis-
tration is the availability of data that can be used in the computer learning process.
At the same time, without the underlying analytical technology, the data revolution
can be viewed simply as a shift in the scale of the available data rather than a
transformational change. Hence, this study refers to Big Data as a phenomenon
where the scale of available data (data revolution, 4Vs, etc.) is integral to machine
learning, natural language processing, and other AI technologies. This view is not
far removed from the definition of Big Data through the concepts data analytics
and data science in Mergel et al. (2016).
Increasing prominence makes it imperative to understand the role of Big Data in
the public sector. Despite its importance, research on the topic from a Policy and
Administration perspective is still nascent. Maciejewski (2017) provides a valuable
summary of the applications and lessons of Big Data in public policy, although this
draws largely on practical examples and makes limited mention of the academic
understanding of the subject. A notable exception is offered by Mergel et al. (2016),
which provide an operational definition of Big Data for public affairs and discuss
major challenges for the Public Administration field emanating from Big Data,
especially in the field of education. However, there has been little effort to date
to consolidate findings, distil the key effects and recommendations for public
organisations and, crucially, provide a coherent approach for the future direction
of the Public Policy and Administration field. This study aims to address this gap at
an opportune moment, given the growing importance of the topic.
Research design
This study is based on a comprehensive literature review of the role and implica-
tions of Big Data for the public sector. It explores the coverage of the subject in
leading Policy and Administration journals but also considers the wider field of
work, including sources covering social science, management, and information and
technology management. This methodological approach was selected as it allows
the researcher to (a) identify the central topics and strands of theory; (b) consoli-
date and critically review our existing knowledge; and (c) provide direction for
future research. The impact of Big Data on the public sector is beginning to
unfold in the Policy and Administration literature. As a result, an informed,
broad and detailed comprehension of the subject is important and timely as it
can help to move the discussion forward as to how Big Data can benefit the
public sector.
The literature review aims to offer an exhaustive coverage of all articles pub-
lished in the top 20 journals of the 2018 Google Scholar rankings under the ‘‘Public
Policy and Administration’’ subcategory (see Appendix 1 for a list of journals and
the tabulation of relevant articles in each journal). These sources were selected
Pencheva et al.
27
as they provide a credible and, importantly, replicable set of key journals in the
discipline. Four additional journals were included to the list for comparability with
the Google Scholar rankings from 2017 and 2016 when some of the early research
was undertaken (Journal of Policy Analysis and Management, Public Policy and
Administration, Administrative Science Quarterly, and International Journal of
Public Sector Management). The coverage included all articles up to April 2018
that mention any of the following search terms: ‘Big Data’, ‘Data Analytics’, ‘Data
Science’,
‘Advanced
Analytics’,
‘Machine
Learning’,
‘Natural
Language
Processing’, and ‘Artificial Intelligence’. These terms, in our opinion, capture the
essence of the Big Data phenomenon as discussed above and relate to the key
descriptors that appear in academic work and, importantly, discussions within
the industry and government. An illustrative example here is the reference to AI
and data revolution as the first Grand Challenge set out in the UK Government
Industrial Strategy (HM Government, 2017). Abstracts (and full articles as needed)
were examined to identify studies for this review. In total, 196 articles in primary
Public Policy and Administration (henceforth PPA) journals that...
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