Analyzing Iranian Public Sector Big Data System Requirements Based on System Design Thinking

Journal of Systems Thinking in Practice

دوره 3، شماره 3 - شماره پیاپی 10، آذر 2024، صفحه 1-17   

نوع مقاله: Research Article

شناسه دیجیتال (DOI): 10.22067/jstinp.2024.84259.1093

نویسندگان

Soheil Paydar Fard^* ؛ Ali Rajabzadeh Ghatari؛ Mahmoud Dehghan Nayeri

Department of Industrial Management, Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran.

چکیده

The contemporary world is marked by generation and consumption of vast volume, high velocity, and considerable diverse data, leading us to the concept of big data. In this study, a system design thinking approach was employed to identify the requirements of Iran's public sector big data system. National big data systems would help governments to support their decisions by data and answer to national problems faster. Given the complexity and time-intensive nature of traditional system requirement analysis methods, their practical application in the industry has been declined. Therefore, in this research, system design thinking as an agile alternative for identifying system requirements has been discussed. To accomplish this, the LDA machine learning method has been utilized to analyze approximately 88,000 articles, a thematic analysis on around 600 Instagram and Twitter posts has been conducted, and six experts representing targeted problem persona were interviewed. The objective of this research is to extract insights to serve as a foundation for formulating big data policies in Iran. Findings reveal that Iran big data system requirements can be classified into four categories which indicate on increasing managed access to data while considering security and privacy, encouraging private and public sectors cooperation, transformation to smart governance, and establishing national data organization which would be responsible of data ID documents.

کلیدواژه‌ها

Big data؛ System design thinking؛ System thinking؛ Policy؛ Latent dirichlet allocation (LDA)؛ Topic modelling

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