Abstract

This paper aims to explore the role of artificial intelligence (AI) and alternative data in ‎enhancing the quality and efficiency of official statistics. It explores how machine learning ‎techniques and non-traditional data sources—such as mobile phone data, satellite imagery, ‎and administrative records—can be utilized to improve the accuracy of statistical indicators, ‎reduce the time required for statistical production, and lower operational costs compared to ‎traditional statistical methods.‎
The study adopts a descriptive–analytical approach by integrating official data with alternative ‎data sources and applying AI and machine learning techniques to assess their impact on the ‎production of official statistics. The results indicate the potential to increase indicator accuracy ‎by 10–20%, shorten data update cycles from 6–12 months to 1–3 months, and reduce data ‎collection and analysis costs by 20–30%. These improvements contribute to enhancing the ‎efficiency, timeliness, and reliability of official statistics and support evidence-based ‎policymaking.‎
The paper concludes with several recommendations, including integrating alternative data into ‎official statistical systems, adopting AI-driven advanced analytical tools, establishing clear ‎institutional and governance frameworks for data use, building the capacities of statistical ‎personnel, and strengthening collaboration among government agencies, research institutions, ‎and the private sector to ensure the production of high-quality and reliable statistical data for ‎informed decision-making.‎