Research of an innovative model of Russian document service in the context of big data

Abstract

The article is devoted to the development and evaluation of the effectiveness of an innovative model for servicing Russian documents amid the rapid growth of data volumes and the fragmentation of departmental information systems, which leads to delays, elevated error rates, and excessive costs in the delivery of public services. The aim of the study is to substantiate the architecture and operating principles of a model that employs big data technologies to ensure a unified digital profile of the citizen and the organisation, proactive service provision, and the automation of standard decisions. Methodologically, the work relies on a systems analysis of document workflow as a complex socio-technical system; the modelling of conceptual, functional, and architectural schemes; content analysis of the regulatory framework; comparison of open statistical indicators for 2022-2025; and the calculation of key KPIs (average processing time, error ratio, CSI, unit operating costs) using standard statistical packages. The results demonstrate a substantial advantage of the proposed model over the traditional one: the average time for providing mass services is reduced to hours (a decrease of about 95%); the frequency of errors caused by human factors and data misalignment falls by more than two orders of magnitude to a statistically negligible level; and aggregate operating costs decrease by 75-80% due to automation, the abandonment of paper-based document workflow, and the optimisation of human resources. The citizen satisfaction index rises from low values to consistently high ones, reflecting improved transparency, predictability, and convenience of services and setting a new standard of client centricity. The discussion highlights the synergistic effect among speed, data quality, and resource savings, as well as the model's scalability to adjacent domains (healthcare, education, the social sphere) and the potential for integration with Al for proactive services. The key prerequisites for successful implementation are identified: a unified master data register, interdepartmental APIs, regulatory amendments to the once-only principle, robust information security and data ethics, project offices, and quality metrics embedded in governance. The study concludes that large-scale implementation of the model is strategically advisable and that personnel must be trained with competencies in data analytics and the management of digital services.