Against the backdrop of global industrial and supply chain restructuring and a complex and volatile external environment, the Third Plenary Session of the 20th Central Committee of the Communist Party of China proposed to improve the systems and capabilities for enhancing the resilience and security of industrial and supply chains. Currently, China’s supply chains face structural risks such as over-reliance on single suppliers upstream and rigid market channels downstream. In this context, “chain leader” enterprises, as the core of industrial and supply chains, play a crucial role in enhancing chain resilience and promoting supply chain diversification. Based on this, this paper uses data from A-share listed companies from 2013 to 2023 to explore the relationship between the data assetization of “chain leader” enterprises and supply chain concentration, its mechanisms, and heterogeneous effects. The findings reveal that the data assetization of “chain leader” enterprises significantly reduces supply chain concentration, thereby promoting supply chain diversification. Mechanism testing indicates that data assets facilitate supply chain diversification by alleviating information asymmetry, enhancing resource allocation efficiency, and strengthening supervision and governance. Further research based on the TOE framework shows that the technological capabilities of “chain leader” enterprises, the digital background of management, and regional digital infrastructure can enhance the promoting effect of data assetization on supply chain diversification. Extended analysis finds that the marketization level of data elements plays a significant positive moderating role in the process of data assetization-driven supply chain diversification. The contributions of this paper are as follows: First, it expands the research perspective by examining the impact of data assetization on supply chain concentration from the viewpoint of “chain leader” enterprises, extending the study of data assets from the micro level of enterprises to the macro level of supply chains. Second, it empirically verifies the specific paths through which data assets affect supply chain concentration by alleviating information asymmetry, enhancing resource allocation efficiency, and strengthening supervision and governance. Third, it optimizes the measurement of data assetization by constructing a comprehensive quantitative system that includes willingness, capability, and action, addressing the limitation of existing studies that rely on single-dimensional measures of data assets.