Core Insights - China aims to cultivate a robust data element industry chain, projecting a data industry scale of 7.5 trillion yuan by 2030 [1][3] - As the first country to incorporate data as a production factor, China has established a comprehensive data industry chain, with a data production total of 41.06 zettabytes in 2024, reflecting a 25% year-on-year growth [3] - The number of data-related enterprises in China exceeds 190,000, with the current data industry scale surpassing 2 trillion yuan [3] Data Sharing and Integration - Public data sharing is a crucial breakthrough for the marketization of data elements, with a 7.5% increase in local public data open platforms and a 7.1% rise in open data volume in 2024 [5] - The number of high-quality data sets has grown by 27.4% year-on-year, indicating a significant push towards integrating public and enterprise data [5] - The National Data Bureau is planning to establish a comprehensive data infrastructure by 2029 [5] Artificial Intelligence and Data Quality - Data has surpassed traditional production factors, becoming a core driver of AI breakthroughs and industrial transformation [6] - High-quality data sets are essential for enhancing AI model performance and reshaping the entire industry chain from R&D to commercial application [6] - The construction of high-quality data sets involves critical processes such as data collection, cleaning, annotation, and quality assessment [8] Data Annotation Industry - China's data annotation industry has surpassed 8 billion yuan in value, entering a new phase of scale and standardization [10] - The number of companies developing or applying AI has increased by 36% year-on-year, with high-quality data sets growing by 27.4% [10] - Companies utilizing large model data technologies and data application enterprises have seen year-on-year growth of 57.21% and 37.14%, respectively [10] Challenges in Data Development - Despite the acceleration in high-quality data set innovation, challenges remain, including low data stock and production, inconsistent data quality, lack of mainstream high-value data, and low data utilization efficiency [12]