Prospects for Improving Information and Analytical Support for Government Decision-Making
Research Article
Abstract
Over the past decade, the computing systems have experienced significant technological advancements, which led to continuously evolving and expansion of the software applications for the automation of government decision-making process including policy analysis process. In this regard, the traditional ministerial information analysis techniques received a digital assistant – an enhanced decision support system that supposed to maximize the functionality and performance of every organization unit where it is used. The adoption of information systems for decision support on levels of government management may transform the government from the classical administration tradition to the culture of intelligent public administration, a class of control techniques that use various artificial intelligence computing approaches like neural networks at all levels of public administration and the policy development process. The author emphasizes the efficiency of use of DSS on every stage of the political cycle, that may increase the quality of decisions and policies by allowing to make changes and discarding non-effective options before the policy is made. Moreover, the new generation DSS for public management is expected to overcome another edge, it is time to learn how to operationalize the ideas received from the information and policy analysis in order to offer a problem solution instead of just giving various ideas to choose. Next stage for DSS is making policy decisions.
References
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Vidgen R., Shaw S., Grant D.B. 2017. Management Challenges in Creating Value from Business Analytics. – European Journal of Operational Research. Vol. 261. No. 2. P. 626-639. DOI: 10.1016/j.ejor.2017.02.023.
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Levkin I.M. 2008. Osnovy informatsionno-analiticheskoi raboty. SPb: Izd-vo SZAGS. 206 s. (RUS.)
Morozova O.A., Loseva V.V., Ivanova L.I. 2024. Informatsionnye tekhnologii v gosudarstvennom i munitsipal'nom upravlenii. M.: Yurait. 156 s. (RUS.)
Povroznik N.G. 2017. Informatsionno-analiticheskie tekhnologii gosudarstvennogo i munitsipal'nogo upravleniya. Perm': Izd-vo PGNIU. 164 s. (RUS.)
Angalakudati M., Balwani S., Calzada J., Chatterjee B. 2014a. Business Analytics for Flexible Resource Allocation under Random Emergencies. – Management Science. Vol. 60. No. 6. P. 1552-1573. DOI: 10.1287/mnsc.2014.1919.
Angalakudati, Mallikarjun, J. Calzada, V. Farias, J. Gonynor, M. Monsch, A. Papush, G. Perakis. 2014b. Improving Emergency Storm Planning Using Machine Learning. – 2014 IEEE PES T&D Conference and Exposition. http://dx.doi.org/10.1109/TDC.2014.6863406.
Dingelstad J., Borst R.T., Meijer A. 2022. Hybrid Data Competencies for Municipal Civil Servants: An Empirical Analysis of the Required Competencies for Data-Driven Decision-Making. – Public Personnel Management. Vol. 51. No. 2. P. 1-33.
Einhorn H.J., Hogarth R.M. 1981. Behavioral Decision Theory: Processes of Judgment and Choice. – Journal of Accounting Research. Vol. 19. No. 1. P. 1-31. http://dx.doi.org/10.2307/2490959.
Ge H., Wang B. 2019. Research on Management Model of Network Public Opinion Based on Big Data Technology. – Journal of Physics: Conference Series. Vol. 1345. Is. 5. P. 1-5.
Gregory R., Keeny R.L. 1994. Creating Policy Alternatives Using Stakeholder Values. – Management Science. Vol. 40. No. 8. P. 1035-1048. DOI: 10.1287/mnsc.40.8.1035.
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. Vol. 26. Is. 1-2. P. 147-169.
Ibrahim O. 2018. Design and Investigation of a Decision Support Systems for Public Policy Formulation: academic dissertation for the Degree of Doctor of Philosophy in Computer and Systems Sciences. Stockholm: Stockholm University.
Janssen M., Wimmer M.A. 2015. Introduction to Policy-making in the Digital Age. – Policy Practice and Digital Science: Integrating Complex Systems, Social Simulation and Public Administration in Policy Research. Springer International Publishing. P. 1-14.
Kinra A., Beheshti-Kashi S., Buch R., Nielsen T.A.S., Pereira F. 2020. Examining the Potential of Textual Big Data Analytics for Public Policy Decision-making: A case study with driverless cars in Denmark. – Transport Policy. Vol. 98. P. 68-78.
Lavalle S., Shockley R., Hopkins M.S., Lesser E. 2011. Big Data, Analytics and the Path from Insights to Value. – MIT Sloan Management Review. P. 21-32.
McGee Z. A. Jones B.D. 2019. Reconceptualizing the Policy Subsystem: Integration with Complexity Theory and Social Network Analysis. – Policy Studies Journal. Vol. 47(S1). S138–S158. DOI: 10.1111/psj.12319.
Moore M.H. 2013. Recognizing Public Value. Cambridge: Harvard University Press. 430 p.
Poel M., Meyer E. T., Schroeder R. 2018. Big Data for Policymaking: Great Expectations, but with Limited Progress? – Policy & Internet. Vol. 10. No. 8. DOI: 10.1002/poi3.176.
Suominen A., Hajikhani A. 2021. Research Themes in Big Data Analytics for Policymaking: Insights from a Mixed-Methods Systematic Literature Review. – Policy & Internet. June 14. P. 1-21.
Vidgen R., Shaw S., Grant D.B. 2017. Management Challenges in Creating Value from Business Analytics. – European Journal of Operational Research. Vol. 261. No. 2. P. 626-639. DOI: 10.1016/j.ejor.2017.02.023.
Wahyunengseh R.D., Hastjarjo S. 2020. Big Data Analysis of Policies on Disaster Communication: Mapping the Issues of Communication and Public Responses in the Government Social Media. – IOP Conference Series Earth and Environmental Science. Vol. 717. No. 1: 012004. DOI: 10.1088/1755-1315/717/1/012004.
Article
Received: 15.05.2025
Issue
Section
POLITICAL PROCESSES AND PRACTICES
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