Digital model for monitoring national programs: the Kazakhstan experience - Uandykova, Mafura; Mirkassimova, Tolkyn; Mukhamejanova, Gulnar; Yeleukulova, Assel; Baikhojayev, Akhmed; Astaubayeva, Gulnar (Frontiers in Artificial Intelligence, 2025)
This paper presents a conceptual digital model for monitoring national programs designed to enhance their effectiveness, transparency, and performance in the context of digital transformation in public administration. The research identifies limitations of traditional monitoring approaches characterized by data fragmentation, lack of dynamic tracking, and insufficient focus on socio-economic outcomes. In response to these challenges, we propose an original Digital Model for National Program Monitoring (DMNPM) that integrates various data sources from Kazakhstan’s digital ecosystem (egov.kz, Smart Bridge, Open Data). The key scientific contribution of the model is its comprehensive approach, which includes predictive analytics capabilities based on machine learning for risk forecasting and causal relationship assessment, as well as built-in two-way feedback mechanisms. To demonstrate the practical applicability and potential of DMNPM, we present case studies of monitoring key strategic programs in Kazakhstan – “Digital Kazakhstan” and “Nurly Zhol,” as well as pilot national projects “Zhaily Mektep” and “Auyldyq Densaulyq Saqtau.” A quasi-experimental pilot across two national programs demonstrates measurable improvements in monitoring effectiveness and reporting efficiency compared to traditional manual processes. The research contributes to digital governance theory and monitoring methodology by offering a practical solution adapted for countries with actively developing digital infrastructure.