THE UTILIZATION OF BIG DATA IN REGIONAL DEVELOPMENT PLANNING: A STUDY ON STRENGTHENING EVIDENCE-BASED POLICY IN LOCAL GOVERNMENT

Authors

  • Mujahidin Institut Pemerintahan Dalam Negeri

DOI:

https://doi.org/10.69916/jkbti.v4i3.467

Keywords:

basic services, big data, evidence-based policy, local government, regional development planning

Abstract

This study aims to analyze the utilization of big data in regional development planning as a strategy to strengthen evidence-based policy in local government. The research focuses on how big data can support development program planning, poverty reduction, social assistance targeting, and basic-service improvement. This study uses a qualitative method with an exploratory-descriptive approach and conceptual framework development. Data were collected from secondary and documentary sources, including recent peer-reviewed journal articles, policy documents, institutional reports, regional planning materials, and regulatory documents related to big data, digital governance, evidence-based policy, local development planning, poverty alleviation, and public services. The data were analyzed using thematic analysis by classifying the findings into several themes: data integration, evidence-based program formulation, poverty and vulnerability mapping, social assistance targeting, basic-service improvement, institutional readiness, data governance, and public accountability. The findings show that big data can improve regional planning by integrating population records, poverty databases, social assistance data, geospatial information, public-service indicators, village-level data, citizen complaints, and digital feedback. The study contributes by proposing an evidence-based local development planning framework consisting of five dimensions: data integration, analytical interpretation, program prioritization, accountable implementation, and continuous evaluation. This framework emphasizes that big data must be supported by institutional coordination, analytical capacity, ethical safeguards, public participation, and accountable governance to produce more accurate, inclusive, and responsive local development policies.

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References

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.

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. 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.

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. 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.

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. 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.

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.

E. Aiken, G. Bedoya, A. Coville, and J. E. Blumenstock, “Machine learning and phone data can improve targeting of humanitarian aid,” Nature, vol. 603, pp. 864–870, 2022, doi: 10.1038/s41586-022-04484-9.

I. S. Smythe and J. E. Blumenstock, “Geographic microtargeting of social assistance with high-resolution poverty maps,” Proceedings of the National Academy of Sciences, vol. 119, no. 32, Art. no. e2120025119, 2022, doi: 10.1073/pnas.2120025119.

D. W. Beuermann, B. Hoffmann, M. Stampini, D. L. Vargas, and D. Vera-Cossio, “Shooting a moving target: Evaluating targeting tools for social programs when income fluctuates,” Journal of Development Economics, vol. 172, Art. no. 103395, 2025, doi: 10.1016/j.jdeveco.2024.103395.

P. Schnitzer and Q. Stoeffler, “Targeting social safety nets: Evidence from nine programs in the Sahel,” The Journal of Development Studies, vol. 60, no. 4, pp. 574–595, 2024, doi: 10.1080/00220388.2023.2291325.

T. K. 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. A. 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.

T. Yigitcanlar, R. Y. M. Li, P. B. Beeramoole, and A. Paz, “Artificial intelligence in local government services: Public perceptions from Australia and Hong Kong,” Government Information Quarterly, vol. 40, no. 3, Art. no. 101833, 2023, doi: 10.1016/j.giq.2023.101833.

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.

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Published

2025-09-29

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How to Cite

[1]
Mujahidin, “THE UTILIZATION OF BIG DATA IN REGIONAL DEVELOPMENT PLANNING: A STUDY ON STRENGTHENING EVIDENCE-BASED POLICY IN LOCAL GOVERNMENT”, JKBTI, vol. 4, no. 3, pp. 327–333, Sep. 2025.