Lephoto N. 1, Segooa M. A. 1, Motjolopane I. 2, Seaba T. R. 3
| 1 Tshwane University of Technology, South Africa 2 University of Witwatersrand, South Africa 3 Nelson Mandela University, South Africa |
Abstract
Background and Aim of Study: Occupational health and risk management (OHRM) in the South African mining sector remains a critical national priority, where the life or death outcomes can be impacted by poor quality-data usage. Big data analytics (BDA) is increasingly used for hazards predictions and timely decision-making.
The aim of the study: to explore critical data quality factors that influence the reliability and effectiveness of BDA for decision-making to guide occupational health practitioners and risk managers within South African mining sector.
Material and Methods: The study employed a quantitative survey methodology, informed by the literature review, to identify key data quality factors of BDA impacting OHRM in the South African mining sector. Underpinned by Technological, Organizational and Environmental (TOE) theory and contextual factors within big data quality dimensions and big data sources. Data was collected from 103 OHRM experts determined by the population size of 140.
Results: The results reveal the following factors to have influence on data quality for BDA within SA mining OHRM; Environmental factors with a predictive power of 25.0% (β=0.250) at p=0.014; followed by big data quality dimensions with 24.1% (β=0.241) at p=0.008; then, technological factors with 15.9% (β=0.159) at p=0.027; big data sources with 13.2% (β=0.132) at p=0.026; lastly organisational factors was less significant at p=0.228 with 10.0% (β=0.100).
Conclusions: This study identifies the factors of data quality, highlighting its role in BDA for decision-making within OHRM. These factors can further be used to provide guidance for SA mining OHRM decision makers to target critical data quality improvement areas for enhanced decision making in the sector.
Keywords
big data analytics, data quality, mining safety, occupational health, risk management, South Africa
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Lephoto Nyakallo (Corresponding Author) – https://orcid.org/0009-0000-7899-6950;
Segooa Mmatshuene Anna – https://orcid.org/0000-0002-4190-8256; Doctor of Computing, Senior Lecturer, Department of Informatics, Tshwane University of Technology, Pretoria, South Africa.
Motjolopane Ignitia – https://orcid.org/0000-0001-9047-6720; PhD in Information Systems, Associate Professor, Digital Business Wits Business School, University of Witwatersrand, Johannesburg, South Africa.
Seaba Tshinakaho Relebogile – https://orcid.org/0000-0002-5773-887X; Doctor of Computing in Informatics, Senior Lecturer, Department of IT Management and Governance, Nelson Mandela University, Gqeberha, South Africa
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APA
Lephoto, N., Segooa, M. A., Motjolopane, I., & Seaba, T. R. (2025). Data quality factors for big data analytics in occupational health and risk management. International Journal of Science Annals, 8(2), 1–11. https://doi.org/10.26697/ijsa.2025.2.5
Harvard
Lephoto, N., Segooa, M. A., Motjolopane, I., & Seaba, T. R. "Data quality factors for big data analytics in occupational health and risk management." International Journal of Science Annals, [online] 8(2), pp. 1–11. viewed 30 June 2025, https://culturehealth.org/ijsa_archive/ijsa.2025.2.5.pdfVancouver
Lephoto N., Segooa M. A., Motjolopane I., & Seaba T. R. Data quality factors for big data analytics in occupational health and risk management. International Journal of Science Annals [Internet]. 2025 [cited 30 June 2025]; 8(2): 1–11. Available from: https://culturehealth.org/ijsa_archive/ijsa.2025.2.5.pdf https://doi.org/10.26697/ijsa.2025.2.5
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