Original Research
Structuration theory perspective of Big Data in a typical South African municipality
Submitted: 01 October 2023 | Published: 22 May 2024
About the author(s)
Modjadji P. Kgoale, Department of Information Systems, College of Science, Engineering and Technology, University of South Africa, Pretoria, South AfricaMampilo Phahlane, Department of Information Systems, College of Science, Engineering and Technology, University of South Africa, Pretoria, South Africa
Abstract
Background: Big Data, sourced from various digital sources, offers valuable insights for better decision-making, and organisations are implementing Big Data technologies to improve their services. This article is about how the City of Tshwane (CoT), a South African metropolitan municipality is using data collected from its various information and communication technology (ICT) projects to improve decision-making.
Objectives: The study aims to identify the type of Big Data generated by South African municipalities and to establish ways in which knowledge, expertise and suitable management techniques impact the efficient application of Big Data in these municipalities.
Method: A qualitative approach, a case study method and Semi-structured interviews were deemed fit to collect data while thematic analysis was employed to identify patterns and themes associated with participants’ experiences. Structuration Theory was used to analyse existing social structures that govern how Big Data is used in the CoT.
Results: There is a lack of data integration and a Big Data management system in the CoT. However, the CoT is ready to embrace Big Data and it is currently establishing a unit to effectively analyse collected data.
Conclusion: Big Data’s potential for developing effective data management systems has garnered significant attention in the public sector and the CoT is also ready to fully adopt and implement these technologies.
Contribution: South African municipalities can effectively utilise Big data by establishing skilled staff, good infrastructure and suitable policies for efficient data generation and interpretation.
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