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GOVERNMENT BIG DATA GOVERNANCE MODEL TO IMPROVE THE EFFECTIVENESS OF INTEGRATED PUBLIC SERVICES

Authors

  • Muhammad Kautsar Institut Pemerintahan Dalam Negeri
  • Mujahidin Institut Pemerintahan Dalam Negeri

DOI:

https://doi.org/10.69916/jkbti.v4i3.470

Keywords:

big data governance, data integration, integrated public services, interoperability, local government

Abstract

This study aims to analyze a government big data governance model for improving the effectiveness of integrated public services. The research focuses on inter-agency data integration, system interoperability, data security, and the use of public data in integrated service delivery. This study employs a qualitative method with an exploratory-descriptive approach and conceptual governance model development. Data were collected from secondary and documentary sources, including recent peer-reviewed journal articles, policy documents, institutional reports, digital government guidelines, regulatory materials, and scholarly works related to big data governance, data-driven government, interoperability, data security, and public-service innovation. The data were analyzed using thematic analysis by classifying findings into inter-agency data integration, system interoperability, data quality, data security, institutional coordination, collaborative governance, public-service effectiveness, and accountability. The findings show that integrated public services require more than digital applications or one-stop service portals. Effective integration depends on shared data standards, interoperable systems, secure data exchange, reliable data quality, and coordinated institutional responsibility. The study contributes by proposing a cross-sector government big data governance model consisting of institutional coordination, data integration, system interoperability, data quality assurance, data security, and collaborative service use. This model emphasizes that big data must be governed as a strategic public asset to improve service speed, accuracy, accessibility, transparency, and accountability.

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References

S. I. H. Shah, V. Peristeras, and I. Magnisalis, “DaLiF: A data lifecycle framework for data-driven governments,” Journal of Big Data, vol. 8, Art. no. 89, 2021, doi: 10.1186/s40537-021-00481-3.

M. A. Hossin, J. Du, L. Mu, and I. O. Asante, “Big data-driven public policy decisions: Transformation toward smart governance,” SAGE Open, vol. 13, no. 4, 2023, doi: 10.1177/21582440231215123.

D. S. Sayogo, S. B. C. Yuli, and F. A. Amalia, “Data-driven decision-making challenges of local government in Indonesia,” Transforming Government: People, Process and Policy, vol. 18, no. 1, pp. 145–156, 2024, doi: 10.1108/TG-05-2023-0058.

K. Ariansyah, A. B. Setiawan, A. Hikmaturokhman, A. Ardison, and D. Walujo, “Big data readiness in the public sector: An assessment model and insights from Indonesian local governments,” Journal of Science and Technology Policy Management, vol. 16, no. 2, pp. 252–275, 2025, doi: 10.1108/JSTPM-01-2023-0010.

T. Sangkachan and N. Powintara, “Data-driven government: Essential mechanisms to unleash the power of data,” International Journal of Electronic Government Research, vol. 18, no. 1, pp. 1–19, 2022, doi: 10.4018/IJEGR.288070.

B. W. Wirtz, J. C. Weyerer, M. Becker, and W. M. Müller, “Open government data: A systematic literature review of empirical research,” Electronic Markets, vol. 32, pp. 2381–2404, 2022, doi: 10.1007/s12525-022-00582-8.

G. M. Begany and J. R. Gil-Garcia, “Open government data initiatives as agents of digital transformation in the public sector: Exploring the extent of use among early adopters,” Government Information Quarterly, vol. 41, no. 3, Art. no. 101955, 2024, doi: 10.1016/j.giq.2024.101955.

K. T. Tai, “Open government research over a decade: A systematic review,” Government Information Quarterly, vol. 38, no. 2, Art. no. 101566, 2021, doi: 10.1016/j.giq.2021.101566.

A. Francey and T. Mettler, “The effects of open government data: Some stylised facts,” Information Polity, vol. 26, no. 1, pp. 1–16, 2021, doi: 10.3233/IP-200281.

M. Wook, N. A. Hasbullah, N. M. Zainudin, Z. Z. A. Jabar, S. Ramli, N. A. M. Razali, and N. M. M. Yusop, “Exploring big data traits and data quality dimensions for big data analytics application using partial least squares structural equation modelling,” Journal of Big Data, vol. 8, Art. no. 33, 2021, doi: 10.1186/s40537-021-00439-5.

J. I. Criado, A. Guevara-Gómez, and J. Villodre, “Digital public services: A study of innovation in public administrations,” Government Information Quarterly, vol. 38, no. 3, Art. no. 101583, 2021, doi: 10.1016/j.giq.2021.101583.

S. Verma, “Sentiment analysis of public services for smart society: Literature review and future research directions,” Government Information Quarterly, vol. 39, no. 3, Art. no. 101708, 2022, doi: 10.1016/j.giq.2022.101708.

A. Zuiderwijk, Y.-C. Chen, and F. Salem, “Implications of the use of artificial intelligence in public governance: A systematic literature review and a research agenda,” Government Information Quarterly, vol. 38, no. 3, Art. no. 101577, 2021, doi: 10.1016/j.giq.2021.101577.

C. van Noordt and G. Misuraca, “Artificial intelligence for the public sector: Results of landscaping the use of AI in government across the European Union,” Government Information Quarterly, vol. 39, no. 3, Art. no. 101714, 2022, doi: 10.1016/j.giq.2022.101714.

R. Madan and M. Ashok, “AI adoption and diffusion in public administration: A systematic literature review and future research agenda,” Government Information Quarterly, vol. 40, no. 1, Art. no. 101774, 2023, doi: 10.1016/j.giq.2022.101774.

G. Maragno, L. Tangi, L. Gastaldi, and M. Benedetti, “Exploring the factors, affordances and constraints outlining the implementation of artificial intelligence in public sector organizations,” International Journal of Information Management, vol. 73, Art. no. 102686, 2023, doi: 10.1016/j.ijinfomgt.2023.102686.

M. J. Ahn and Y.-C. Chen, “Digital transformation toward AI-augmented public administration: The perception of government employees and the willingness to use AI in government,” Government Information Quarterly, vol. 39, no. 2, Art. no. 101664, 2022, doi: 10.1016/j.giq.2021.101664.

A. David, T. Yigitcanlar, R. Y. M. Li, J. M. Corchado, P. H. Cheong, K. Mossberger, and R. Mehmood, “Understanding local government digital technology adoption strategies: A PRISMA review,” Sustainability, vol. 15, no. 12, Art. no. 9645, 2023, doi: 10.3390/su15129645.

N. Haug, S. Dan, and I. Mergel, “Digitally induced change in the public sector: A systematic review and research agenda,” Public Management Review, vol. 26, no. 7, pp. 1963–1987, 2024, doi: 10.1080/14719037.2023.2234917.

A. Scupola and I. Mergel, “Co-production in digital transformation of public administration and public value creation: The case of Denmark,” Government Information Quarterly, vol. 39, no. 1, Art. no. 101650, 2022, doi: 10.1016/j.giq.2021.101650.

K. Chao, M. N. I. Sarker, I. Ali, R. B. R. Firdaus, A. Azman, and M. M. Shaed, “Big data-driven public health policy making: Potential for the healthcare industry,” Heliyon, vol. 9, no. 9, Art. no. e19681, 2023, doi: 10.1016/j.heliyon.2023.e19681.

D. Mills, S. Pudney, P. Pevcin, and J. Dvorak, “Evidence-based public policy decision-making in smart cities: Does extant theory support achievement of city sustainability objectives?” Sustainability, vol. 14, no. 1, Art. no. 3, 2022, doi: 10.3390/su14010003.

B. Attard-Frost, A. Brandusescu, and K. Lyons, “The governance of artificial intelligence in Canada: Findings and opportunities from a review of 84 AI governance initiatives,” Government Information Quarterly, vol. 41, no. 2, Art. no. 101929, 2024, doi: 10.1016/j.giq.2024.101929.

P. G. R. de Almeida and C. D. dos Santos Jr., “Artificial intelligence governance: Understanding how public organizations implement it,” Government Information Quarterly, vol. 42, no. 1, Art. no. 102003, 2025, doi: 10.1016/j.giq.2024.102003.

J. R. Saura, D. Ribeiro-Soriano, and D. Palacios-Marqués, “Assessing behavioral data science privacy issues in government artificial intelligence deployment,” Government Information Quarterly, vol. 39, no. 4, Art. no. 101679, 2022, doi: 10.1016/j.giq.2022.101679.

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Published

2025-09-29

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How to Cite

[1]
Muhammad Kautsar and Mujahidin, “GOVERNMENT BIG DATA GOVERNANCE MODEL TO IMPROVE THE EFFECTIVENESS OF INTEGRATED PUBLIC SERVICES”, JKBTI, vol. 4, no. 3, pp. 334–340, Sep. 2025.

Issue

Vol. 4 No. 3 (2025): September 2025

Section

Articles

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Copyright (c) 2025 Muhammad Kautsar, Mujahidin

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This work is licensed under a Creative Commons Attribution 4.0 International License.

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