2026

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Browsing 2026 by Subject "digital transformation"

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    Data Analyst and Data Scientist Professions: Demand, Requirements, and Labor Market Prospects
    (Національна академія статистики, обліку та аудиту, 2026) Yashchenko, L. O.
    This article presents a comprehensive study of the current state, requirements, and development prospects of the data analyst and data scientist professions in the context of digital economic transformation and dynamic changes in the labor market. It is demonstrated that the rapid growth of data volumes, the proliferation of analytical platforms, and the active adoption of artificial intelligence and machine learning technologies are driving the increasing strategic importance of data professionals in managerial decision-making processes. The empirical basis of the study is laid by analysis of job vacancies on the Work.ua platform (March 2026), enabling to assess the structure of demand, the level of competition, the requirements to applicants, and the salary characteristics. The findings reveal a structural imbalance between labor demand and supply, shown in the higher number of applicants relative to available vacancies, as well as a gap between salary expectations and actual employer offers. The study systematizes the key competencies of data analysts and data scientists, including technical skills (SQL, Python, BI tools), analytical competencies (statistics, modeling), as well as communicational and managerial skills. It is demonstrated that the modern labor market increasingly demands multidisciplinary professionals capable of working across the full data lifecycle – from data collection to the implementation of business solutions. Special emphasis is placed on the transformation of professional roles, reflected in the blurring of boundaries between business analysts, data analysts, and data scientists, as well as the growing importance of hybrid positions. The analysis revealed a clear trend of transition from descriptive analytics to predictive and prescriptive analytics, significantly enhancing the strategic value of analytical activities. The practical significance of the research lies in potential applications of its findings in improving academic programs, developing professional standards, and setting human capital development strategies in the digital economy context.
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    Implementation of Artificial Intelligence in EU Industry: Trends Аnalysis.
    (Національна академія статистики, обліку та аудиту, 2026) Polozova, T. V.; Salikhov, M. M.
    The article analyzes current trends in the implementation of artificial intelligence (AI) technologies in manufacturing in the European Union. It is established that since 2018, the EU has evolved from the formation of basic regulatory frameworks to a comprehensive architecture aimed at integrating AI into economic systems, production processes, and organizational models. It was found that the share of enterprises using at least one AI technology increased from 6.93% in 2021 to 17.27% in 2025, indicating a shift from fragmented use to systematic integration of AI in production and management processes. The analysis of high-tech (KTI) and traditional (non-KTI) sectors revealed clear structural differences: in 2025, the highest levels of AI adoption among KTI sectors were recorded in pharmaceutical manufacturing (41.32%), computer, electronic, and optical equipment production (37.44%), chemical manufacturing (28.45%), electrical equipment manufacturing (25.90%), and mechanical engineering (25.45%). This reflects the high intensity of digitalization in knowledge-intensive sectors, where AI is integrated into production and innovation processes. It was determined that national AI adoption trajectories in 2021–2025 form two groups: 1) countries with a high initial base (over 10%), including Denmark, Luxembourg, the Netherlands, Germany, Slovenia, Austria, Belgium, and Sweden, demonstrate stable digital transformation; 2) countries with a lower starting base, such as Estonia, Lithuania, Ireland, and Malta, show accelerated growth. For Ukraine, research priorities are justified: assessing the efficiency of digital transformation under limited resources, integrating ICT and AI into traditional manufacturing and service sectors, and identifying factors that stimulate digital innovation. The results may serve as a scientific basis for improving strategic documents on the digitalization of Ukraine's economy, promoting high-tech sectors, supporting traditional sectors through AI access, standardizing solutions and personnel training, and considering European regulatory approaches to integrate Ukrainian manufacturing into the EU digital space.