Data-centricity as the key enabler of digital government: Is Russia ready for digital transformation of public sector

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Abstract

The leaders of digitalization are developing digital government as a platform that stimulates public-private partnerships in the creation of innovative applications and services. The primary and permanent core of this platform is data, and it is the data ready for the information sharing and the provision of seamless public services. Growing digital world has formed the culture of interaction between government and society based on Open Data. The great importance has the ability of consumers to understand the meaning (semantics) determined by the provider, which is essential for the publication of Linked Open Data. The application and dissemination of data models serve for adequate semantic interpretation during information sharing and aim to achieve semantic interoperability. Both data-centric and model-oriented approaches are fundamental for the digital transformation of public administration. In this paper, we give a brief review of the leading countries digitalization experience and use it to outline the criteria characterizing the implementation of a data-centric paradigm. We have conducted the study showing if Russian e-government is ready for the digitalization in terms of the following areas: (1) the practice of information sharing; (2) the preconditions for the shift to a data-centric and model-oriented paradigm; (3) the regulatory barriers of digital transformation. We believe that the lack of data-centricity can become a serious obstacle to fulfill the plans of the “Digital Economy of Russian Federation” program. Therefore, we give some recommendations to introduce the data-centric approach that would enable the use of disruptive digital technologies and support the development of digital government in Russia.

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Akatkin, Y., & Yasinovskaya, E. (2019). Data-centricity as the key enabler of digital government: Is Russia ready for digital transformation of public sector. In Communications in Computer and Information Science (Vol. 947, pp. 439–454). Springer Verlag. https://doi.org/10.1007/978-3-030-13283-5_33

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