Dear Colleagues,

Data are recognized as a key element for the realization of scientific administration and effective decision-making in government. Governments around the world are pushing data policies to enhance the country. The UK, for example, has established a National Data Strategy, which contains 1) unlocking the value of data across the economy, 2) securing a pro-growth and trusted data regime, 3) transforming the government’s use of data to drive efficiency and improve public services, 4) ensuring the security and resilience of the infrastructure on which data rely, and 5) championing the international flow of data as its main missions.

Data-driven governments should reflect the opportunities and challenges that data have. Data policy at the government level covers a wide range and is characterized by processing public data. At the same time, it can have a comprehensive impact on the daily life of citizens. Therefore, government-level data require an appropriate convergence of administrative sustainability, infrastructure, and the advanced and mature technologies. A data governance at the government level should be discussed to coordinate the promotion of data policies of different government agencies. From a technical point of view, it is necessary to investigate the method of constructing a data catalog and applying FAIRness to data that exist distributedly in public institutions.

This Special Issue discusses various theoretical and practical topics for the establishment and realization of sustainable data policies in the public sectors. It covers major issues related to data governance, such as technological elements and application plans necessary to realize a data-driven government such as artificial intelligence, big data, and data science, laws and systmes, open data and their disclosure, data-driven public services, and privacy issues.

Prof. Dr. Haklae Kim
Prof. Dr. Jangwon Gim
Prof. Dr. Dongjun Suh
Guest Editors

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• sustainable data governance
• national data policy
• data catalog across government agencies
• advanced technology adoption in governments
• knowledge management
• open data
• data governance
• data interoperability
• general aspects of data quality
• data quality management
• data quality assessment
• roles and responsibilities for data quality
• data refinement
• data profiling
• application domain data
• standardization
• machine-readable data
• artificial intelligence
• machine learning
• big data analytics
• data-driven technology
• sustainable smart technology
• AI- and ML-based applications