Affiliations: [a] University at Albany, State University of New York, USA | [b] Universidad de las Americas Puebla, Mexico
Correspondence:
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Corresponding author: J. Ramon Gil-Garcia, Center for Technology in Government & Rockefeller College of Public Affairs and Policy, University at Albany, SUNY, 1400 Washington Avenue, UAB 120, Albany, NY 12222, USA. E-mail: [email protected].
Abstract: Data analytics (DA) use has been frequently considered a transformational practice in the public sector, particularly in terms of its potential for data-driven decision making by local governments. Despite growing interest from academics and practitioners, empirical research on what cities are actually doing regarding DA and how is still relatively scarce, particularly in terms of a focus on specific activities and processes. Based on a socio-technical view, this paper examines a local government’s experience with DA use and how DA can be seen as a process and transformational practice. Results reveal that DA practices go beyond the use of specific technologies and involve collaborative efforts to formulate meaningful problems and prepare data for specific uses. Indeed, problem formulation is an essential step where collective discussions and assessment by technical and nontechnical DA practitioners occurs. Our findings suggest that (1) data analytics viewed as a socio-technical process involves data management and analysis, but also collaborative processes between multiple city agencies, (2) collaborative efforts go beyond data analytics and include data collection, data representation, and problem formulation; and (3) formulating the problem in a collaborative manner could be considered an important first step when using data analytics for decision-making in local governments. Furthermore, collaborative problem formulation in DA seems to be affected by specific variables related to the collaborative effort such as leadership, governance, and technology.
Keywords: Problem formulation, data analytics, collaboration, local government, data-driven decision making, data management, leadership, governance, technology, smart city, collaborative efforts
DOI: 10.3233/IP-211512
Journal: Information Polity, vol. Pre-press, no. Pre-press, pp. 1-24, 2024
