Big Data and Artificial Intelligence in Policy Making: A Mini-Review Approach
Keywords:big data, artificial intelligence, policy, decision-making
The digitization era in public affairs is increasingly disrupting Indonesia. Governance in Indonesia is forced to implement aspects of digitalization in its public services. Big data and artificial intelligence have been used in other scientific activities that provide colour to governance with various scientific disciplines. This study discusses the use of big data and artificial intelligence in policymaking in Indonesia by focusing on the ability of policymakers in policymaking. The method used in this paper is qualitative research with a literature study approach. We reviewed articles with related themes as many as 25 articles published in the last five years from ScienceDirect. The result of this research is that the dynamics that exist in the implementation of public services require appropriate and fast decision-making, considering that this is a community demand. Therefore, public leaders need to disrupt themselves in public services so that these services can be served quickly by enriching skills in big data and artificial intelligence. In conclusion, the critical aspect of the public policy decision-making process is similar to that of any other stage in the policymaking process. Like the preceding steps in the public policy process, the decision-making stage differs depending on the nature of the policy subsystems involved and the degree of consensus faced by decision-makers. The presence of big data in the public sector cannot be disputed as an intriguing approach, particularly during the policy formation cycle. Also, big data and artificial intelligence can help public leaders make decisions to deliver the best policies.
Agustino, L. (2020). Outbreak Handling Policy : The Experience Of Indonesia. Junal Borneo Administrator, 16(2), 253–270.
Awan, U., Shamim, S., Khan, Z., Zia, N. U., Shariq, S. M., & Khan, M. N. (2021). Big data analytics capability and decision-making: The role of data-driven insight on circular economy performance. Technological Forecasting and Social Change, 168(December 2020), 120766. https://doi.org/10.1016/j.techfore.2021.120766
Corredor, C. (2020). Deliberative speech acts: An interactional approach. Language and Communication, 71, 136–148. https://doi.org/10.1016/j.langcom.2020.01.005
Courtney Mustaphi, C. J., Capitani, C., Boles, O., Kariuki, R., Newman, R., Munishi, L., Marchant, R., & Lane, P. (2019). Integrating evidence of land use and land cover change for land management policy formulation along the Kenya-Tanzania borderlands. Anthropocene, 28, 100228. https://doi.org/10.1016/j.ancene.2019.100228
Drosou, M., Jagadish, H. V., Pitoura, E., & Stoyanovich, J. (2017). Diversity in Big Data: A Review. Big Data, 5(2), 73–84. https://doi.org/10.1089/big.2016.0054
Falcone, P. M., Lopolito, A., & Sica, E. (2019). Instrument mix for energy transition: A method for policy formulation. Technological Forecasting and Social Change, 148(August), 119706. https://doi.org/10.1016/j.techfore.2019.07.012
Grewenig, E., Lergetporer, P., Werner, K., & Woessmann, L. (2020). Do party positions affect the public’s policy preferences? Experimental evidence on support for family policies. Journal of Economic Behavior and Organization, 179, 523–543. https://doi.org/10.1016/j.jebo.2020.09.006
Hasan, A., Putri, E. R. M., Susanto, H., & Nuraini, N. (2021). Data-driven modeling and forecasting of COVID-19 outbreak for public policy making. ISA Transactions, xxxx, 7–15. https://doi.org/10.1016/j.isatra.2021.01.028
Höchtl, J., Parycek, P., & Schöllhammer, R. (2016). Big data in the policy cycle: Policy decision making in the digital era. Journal of Organizational Computing and Electronic Commerce, 26(1–2), 147–169. https://doi.org/10.1080/10919392.2015.1125187
Kandt, J., & Batty, M. (2021). Smart cities, big data and urban policy: Towards urban analytics for the long run. Cities, 109(October 2019), 102992. https://doi.org/10.1016/j.cities.2020.102992
Karaivanov, A., Lu, S. E., Shigeoka, H., Chen, C., & Pamplona, S. (2021). Face masks, public policies and slowing the spread of COVID-19: Evidence from Canada. Journal of Health Economics, 78(December 2020), 102475. https://doi.org/10.1016/j.jhealeco.2021.102475
Kim, K. (2021). Indonesia’s Restrained State Capitalism: Development and Policy Challenges. Journal of Contemporary Asia, 51(3), 419–446. https://doi.org/10.1080/00472336.2019.1675084
Kurniawan, D., Sutan, A., Mufandi, I., Supriyanto, E., & Rachmawati, M. (2021). Social Media Used to Spread Vaccination Program: Case of Indonesia Vaccination Covid-19 Policy. Proceedings of the 1st International Conference on Law, Social Science, Economics, and Education. https://doi.org/10.4108/eai.6-3-2021.2306469
Lacam, J. S., & Salvetat, D. (2021). Big data and Smart data: two interdependent and synergistic digital policies within a virtuous data exploitation loop. Journal of High Technology Management Research, 32(1), 1–15. https://doi.org/10.1016/j.hitech.2021.100406
Li, V. O. K., Lam, J. C. K., & Cui, J. (2021). AI for Social Good: AI and Big Data Approaches for Environmental Decision-Making. Environmental Science and Policy, 125, 241–246. https://doi.org/10.1016/j.envsci.2021.09.001
Madsen, A. K. (2018). Data in the smart city: How incongruent frames challenge the transition from ideal to practice. Big Data & Society, 5(2), 205395171880232. https://doi.org/10.1177/2053951718802321
Merhi, M. I., & Bregu, K. (2020). Effective and efficient usage of big data analytics in public sector. Transforming Government: People, Process and Policy, 14(4), 605–622. https://doi.org/10.1108/TG-08-2019-0083
Papakyriakopoulos, O., Hegelich, S., Shahrezaye, M., & Serrano, J. C. M. (2018). Social media and microtargeting: Political data processing and the consequences for Germany. Big Data & Society, 5(2), 205395171881184. https://doi.org/10.1177/2053951718811844
Purbokusumo, Y., & Katangga, B. (2021). Electronic Government (e-Gov), Artificial Intelligence (AI), dan Kesenjangan Digital. In G. Lele & W. Kumorotomo (Eds.), Tinjauan Studi Manajemen dan Kebijakan Publik di Indonesia (Vol. 1, pp. 148–162). Gadjah Mada University Press.
Simonofski, A., Fink, J., & Burnay, C. (2021). Supporting policy-making with social media and e-participation platforms data: A policy analytics framework. Government Information Quarterly, 38(3), 1–13. https://doi.org/10.1016/j.giq.2021.101590
Supriyanto, E. E., Bakti, I. S., & Furqon, M. (2021). The Role Of Big Data In The Implementation Of Distance. Paedagoria: Jurnal Kajian, Penelitian Dan Pengembangan Pendidikan, 6356(4), 61–68. https://doi.org/10.31764
Syaharuddin, Supriyanto, E. E., Septyanun, N., Harun, R., Islahudin, Apriansyah, D., & Saputra, E. (2020). ANN Back Propagation in forecasting and policy analysis on family planning programs : A case study in NTB Province. Journal of Physics: Conference Series SEA-STEM 2020, 1(1), 1–7. https://doi.org/10.1088/1742-6596/1882/1/012036
Taylor, L. (2017). What is data justice? The case for connecting digital rights and freedoms globally. Big Data & Society, 4(2), 205395171773633. https://doi.org/10.1177/2053951717736335
Timur, M., Pradhanawati, A., Purnaweni, H., & Kismartini, K. (2021). Policy Analysis of Socio-Economic Development of Coastal Areas in Central Java Province. ICISPE 2020. https://doi.org/10.4108/eai.9-10-2020.2304711
United Nations. (2020). E-Government Survey 2020 - Digital Government in the Decade of Action for Sustainable Development: With addendum on COVID-19 Response. In United Nations E-Government Surveys (Vol. 1, Issue 1).
Wijaya, T., & Camba, A. (2021). The politics of public–private partnerships: state–capital relations and spatial fixes in Indonesia and the Philippines. Territory, Politics, Governance, 0(0), 1–20. https://doi.org/10.1080/21622671.2021.1945484
Yalcin, A. S., Kilic, H. S., & Delen, D. (2022). The use of multi-criteria decision-making methods in business analytics: A comprehensive literature review. Technological Forecasting and Social Change, 174(December 2020), 121193. https://doi.org/10.1016/j.techfore.2021.121193
Yang, M., Chen, X., & Luo, Z. (2021). Optimal ordering policy for platelets: Data-driven method vs model-driven method. Fundamental Research, July. https://doi.org/10.1016/j.fmre.2021.07.013
Yu, D., Zhu, G., Wang, X., Zhang, C., Soltanalizadeh, B., Wang, X., Tang, S., & Wu, H. (2021). Assessing effects of reopening policies on COVID-19 pandemic in Texas with a data-driven transmission model. Infectious Disease Modelling, 6(2021), 461–473. https://doi.org/10.1016/j.idm.2021.02.001
Zhang, H., Zang, Z., Zhu, H., Uddin, M. I., & Amin, M. A. (2022). Big data-assisted social media analytics for business model for business decision making system competitive analysis. Information Processing and Management, 59(1), 102762. https://doi.org/10.1016/j.ipm.2021.102762
How to Cite
Copyright (c) 2022 by the author
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright @2022. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) which permits unrestricted to copy and redistribute the material in any medium or format, remix, transform, and build upon the material for any purpose, even commercially.
This work is licensed under a Creative Commons Attribution 4.0 International License.