Is Data Really the New Oil?

Abstract

This study examines whether data can truly be considered “the new oil” by analysing the strategic role of data assets and data-driven decision-making in modern organisations. The research explores how raw data can be transformed into valuable insights through data analytics and machine learning, and how these insights contribute to improved corporate performance, competitiveness, and strategic decision-making. The theoretical framework is based on data asset management, data science, and the CRISP-DM methodology, which provides a structured approach to extracting business value from data. The empirical part of the study presents a case study from the telecommunications sector, focusing on customer churn prediction. Using publicly available data, the research applies logistic regression and random forest models to demonstrate how analytical tools can predict customer behaviour and support targeted retention strategies. The results show that advanced machine learning methods significantly outperform traditional models in identifying customers at risk of churn, highlighting the tangible economic value of data-driven approaches through cost reduction and revenue stabilisation. Beyond the firm level, the study also examines the relationship between digitalisation and national competitiveness by comparing Hungary, Estonia, and Romania using international indicators such as DESI, DII, and GDP. The findings reveal that while digitalisation contributes to competitiveness, the relationship is complex and non-linear, influenced by institutional, human capital, and structural factors. Overall, the research concludes that data only becomes a valuable resource when it is properly collected, processed, analysed, and embedded into decision-making processes. Organisations and national economies that invest in digital capabilities, analytical expertise, and data culture gain a sustainable competitive advantage in the digital age.