Big Data in Public Affairs

Abstract

This article offers an overview of the conceptual, substantive, and practical issues surrounding “big data” to provide one perspective on how the field of public affairs can successfully cope with the big data revolution. Big data in public affairs refers to a combination of administrative data collected through traditional means and large‐scale data sets created by sensors, computer networks, or individuals as they use the Internet. In public affairs, new opportunities for real‐time insights into behavioral patterns are emerging but are bound by safeguards limiting government reach through the restriction of the collection and analysis of these data. To address both the opportunities and challenges of this emerging phenomenon, the authors first review the evolving canon of big data articles across related fields. Second, they derive a working definition of big data in public affairs. Third, they review the methodological and analytic challenges of using big data in public affairs scholarship and practice. The article concludes with implications for public affairs.

Big Data and AI – A transformational shift for government: So, what next for research?

“…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.…”

Big Data and Evidence-Driven Decision-Making: Analyzing the Practices of Large and Mid-Sized U.S. Cities

“…For example, instead of highlighting the efficiency gains of Big Data analytics, some researchers have cautioned about the potential threat of privacy violations or institutionalized discrimination through computer algorithms [22,37]. Others question the supremacy and insightfulness of computing analytics and argue that Big Data may only generate a lot of "digital exhaust" [4,25,26]. There are also others who suggest that the public sector has its own unique organizational context, such as concerns about national security, its mission to protect individual liberty and privacy, and the legacy of departmental structure.…”

Evolving Government Information Processes for Service Delivery: Identifying Types & Impact

“…Firstly, new sorts of datasets (e.g., social media data, mobile phone data, GPS signals, website clickstream data, sensor data etc.) offer possibilities to combine these data with traditional government information, mainly survey data and administrative data (Giest 2017;Mergel et al 2016). Secondly, advanced analyzing techniques (e.g., complex algorithms, machine learning and statistic correlations) enable governments to build prediction models, to discover hidden patterns and anomalies, to assess sentiments and to customize service delivery (Deloitte 2016;Mergel et al 2016;Technopolis Group et al 2015;Van der Sloot and Schendel 2016).…”

“…offer possibilities to combine these data with traditional government information, mainly survey data and administrative data (Giest 2017;Mergel et al 2016). Secondly, advanced analyzing techniques (e.g., complex algorithms, machine learning and statistic correlations) enable governments to build prediction models, to discover hidden patterns and anomalies, to assess sentiments and to customize service delivery (Deloitte 2016;Mergel et al 2016;Technopolis Group et al 2015;Van der Sloot and Schendel 2016). These opportunities might alter the traditional government information process as initial data can be enriched, combined with other datasets and analyzed to find hidden correlations or other useful knowledge potentially leading to improved output information to achieve objectives.…”