ESI：2025数据资产驱动苏州制造业数字化转型的机制研究报告

Core Insights - Data assets are identified as the core driving force behind the digital transformation of the manufacturing industry, particularly in Suzhou, where the release of data asset value is crucial for achieving high-quality industrial development [1][2]. Group 1: Data Asset Definition and Importance - The report outlines the basic connotation, classification system, and key characteristics of data assets, emphasizing that high-quality, governable, and structured data assets are essential for optimizing production and enhancing management efficiency [1]. - The evolution of data assets is described in three stages: "business dataization," "data resourceization," and "data assetization," highlighting the strategic resource role of data assets in intelligent decision-making and business model innovation [1][2]. Group 2: Technological Empowerment - The report analyzes how cutting-edge technologies such as blockchain, artificial intelligence (AI), and virtual reality (VR) empower data asset management, with blockchain ensuring data rights and trustworthy circulation, AI enhancing data analysis and application efficiency, and VR aiding in the construction of high-value digital knowledge assets [1][2]. - Practical applications in manufacturing, such as predictive maintenance and flexible customization, demonstrate how data assets can be effectively transformed into productivity, exemplified by a clothing company that achieved a 50% reduction in order response time and a 50% increase in capacity through an industrial internet platform [1]. Group 3: Challenges in Data Asset Management - Suzhou's manufacturing sector faces significant challenges in data asset management, including data silos due to non-unified equipment protocols, lack of standards leading to "one item, multiple codes," a shortage of professional talent, security concerns hindering data sharing, and unclear paths for realizing data asset value [2]. - The report proposes strategies to address these challenges, such as building an industrial internet ecosystem, solidifying data quality foundations, creating vertical domain corpora, and transitioning from "experience-driven" to "data-driven" approaches [2].