The paper provides an overview of the data level in digital twin (DT) systems, focusing on the data at various phases and its application in different sectors. It highlights the importance of data in building virtual models, making cyber-physical connections, and running intelligent operations. The study discusses the current development status and challenges in DT data analysis, including data collection, storage, association, fusion, sorting, and coordination. It also explores the unique features of DT systems compared to traditional big data analysis, emphasizing real-time integration and simulation. The paper further examines the sector-wise implementation of DT data analysis, particularly in manufacturing and urbanization, detailing the data management and analysis features in these sectors. Finally, it outlines the future prospects and challenges of DT technology from a data perspective.The paper provides an overview of the data level in digital twin (DT) systems, focusing on the data at various phases and its application in different sectors. It highlights the importance of data in building virtual models, making cyber-physical connections, and running intelligent operations. The study discusses the current development status and challenges in DT data analysis, including data collection, storage, association, fusion, sorting, and coordination. It also explores the unique features of DT systems compared to traditional big data analysis, emphasizing real-time integration and simulation. The paper further examines the sector-wise implementation of DT data analysis, particularly in manufacturing and urbanization, detailing the data management and analysis features in these sectors. Finally, it outlines the future prospects and challenges of DT technology from a data perspective.