Purpose – Supply chain data scientists’ ability (experience and education in quantitative and business fields) and their analytical skills have significant impacts on the effectiveness of operational and social performance, which in turn increases environmental and financial performance of data-driven supply chains. However, not enough empirical research has been conducted to explore such linkages. This paper therefore tests a theoretical framework based on data collected from data-driven global agrifood supply chains.

Design/methodology/approach – Before applying structural equation modelling on a data set collected from 318 key decision makers, a comprehensive process of exploratory factor analysis, reliability and validity tests is used to develop the constructs.

Findings – The results show that supply chain data scientists’ ability has strong significant impacts on analytical skills (p = 0.00) and the effectiveness of operational and social performance, which in turn significantly increases environmental and financial performance. Similarly, analytical skills (internal applications with supply chains and external applications to macro environment) are found to be important antecedents of the effectiveness of operational and social performance and they also indirectly enhance environmental and financial performance of data-driven global agrifood supply chains. Our findings also show that environmental performance has a positive and significant relationship with financial performance. Additionally, the results are strongly supported by fit indices and explained variances (R2 values).

Practical implications – The study examines important performance determinants of today’s data-driven supply chains. It also enhances the role and understanding of supply chain data scientists and their contribution to supply chain performance.

Originality/value – This study is the first step in developing and confirming complicated interrelationships with the specific characteristics of selected data-driven global agri-food supply chains. Consequently, the empirical evidence provides guidelines for future research that focuses on how supply chain data scientists can utilize their ability and analytical skills to create business value from large datasets.