With the in-depth development of measurement business informatization, how to establish the monitoring, analysis and management mechanism of measurement assets’ life-cycle data has become a key problem to be solved. This paper aims to improve the effectiveness of measurement asset life-cycle management through big data analysis technology. Based on the architecture and functions of the measurement production scheduling platform (MDS), this paper uses ETL technology to achieve data extraction and conversion, and analyzes massive data through offline data mining and real-time data mining. The research results show that through clustering analysis, potential correlation analysis and trend prediction, the weak links in the life cycle of measured assets can be effectively identified, the management process can be optimized, and the management efficiency can be improved. The research in this paper is of great significance to the refined management of power metering assets, and provides theoretical and practical support for further improving management efficiency.