Data quality management in the context of state digital transformation in the field of intellectual property

In the era of state digital transformation, data has become a strategic resource that determines the efficiency of public administration, the quality of public services, and the level of public trust. The aim of this study is to analyze the factors affecting data quality in the field of intellectual property and to examine management methods that ensure information aligns with user needs and the objectives of digital government. The research methodology includes an analytical review of regulatory documents and academic literature, classification of data quality indicators systems analysis of the data lifecycle, and description of practical mechanisms for audit and monitoring. The study proposes a classification of quality criteria and their alignment with user stories, identifies risks that undermine data quality, and suggests appropriate management measures. The scientific novelty of the study lies in the development of a methodological framework for systemic data quality management in the field of intellectual property. The practical significance consists in shaping an approach to building a data quality management system oriented toward user needs and national digitalization goals. The study concludes that a comprehensive approach integrating regulatory, organizational, and technological measures is required, and further research could involve empirical testing of the proposed solutions using existing processes in the field of intellectual property.

1. Scelta, G. et al. (2019), “Frontier Technology Quarterly: Data Economy: Radical transformation or dystopia?” available at: https://www.researchgate.net/publication/331073664_Frontier_Technology_Quarterly_January_2019_Data_Economy_-_radical_transformation_or_dystopia (accessed: 22.02.2026).

2. Nobatov, A. M. and Babanazarov, N. Sh. (2023), “The role of big data technologies in the economy”, Science bulletin, vol. 2, no. 2 (59), pp. 42–45.

3. Buzmakova, M. V. and Polushkina, I. N. (2024), “The impact of big data on economic activity and challenges of their valuation”, Modern management technologies, no. 1 (109), available at: https://sovman.ru/article/10905/ (accessed: 10.03.2026).

4. Chernyshov, K. D. (2024), “The issue of personal data protection in the digital economy”, Science Bulletin, no. 12 (81). pp. 174–178.

5. Dzusova, S. S. (2020), “Digital transformation and prospects for the development of financial technologies in the public administration sector”, Financial Life, no. 1, pp. 69–73.

6. Baishev, A. V. (2023), “Characteristics of data quality”, Vestnik of Tuvan State University. Technical Sciences, vol. 1, no. 1, pp. 6–13.

7. Sebastian-Coleman, L. (2012), Measuring Data Quality for Ongoing Improvement: A Data Quality Assessment Framework, Morgan Kaufmann Publishers Inc. San Francisco United States

8. Wang, R. Y. and Strong, D. M. (1996), “Beyond accuracy: What data quality means to data consumers”, Journal of Management Information Systems, vol. 12, no. 4, pp. 5–33.

9. DAMA International (2020), DAMA-DMBOK: Data Management Body of Knowledge, 2nd ed., translated by Agafonov, G., Olimp-Business, Moscow, Russia.

10. Rahul, K. and Banyal, R. K. (2020), “Data life cycle management in big data analytics”, Procedia Computer Science, vol. 173, pp. 364–371.

11. Miroshnichenko, M. A., Eremina, I. A. and Perelovich, R. S. (2023), “Consolidation of state data in the national data management system”, Natural-Humanitarian Studies, no. 2 (46), pp. 406–411.

12. Khatri, V. and Brown, C. V. (2010), “Designing data governance”, Communications of the ACM, vol. 53, no. 1, pp. 148–152.

13. Tuguskina, G. N., Rozhkova, L. V. and Salnikova, O. V. (2019), “Knowledge management in modern organizations”, University Proceedings. Volga Region. Social Sciences, no. 2 (50), pp. 210–218. – DOI 10.21685/2072-3016-20192-20.