林镇阳：数据是AI的养料，数智融合是必然趋势

Core Insights - The "2025 China Enterprise Competitiveness Conference" highlighted the extension of the "smile curve" theory from industrial manufacturing to the data factor market, indicating that the highest added value is found at both ends of the value chain [2][5] - AI technology is reshaping the data value chain, with diminishing value in data transmission and simple processing, while emphasizing the importance of high-quality data supply and innovative application scenarios [2][5] - The traditional data supply model has been restructured, driven by the proliferation of computing power, which shifts the upstream data industry from "quantity" to "quality" [2][5] Data Supply and Application - The upstream focuses on high-quality data collection and governance, while the downstream emphasizes scenario monetization and value realization [2][5] - Technologies such as trusted data spaces are addressing challenges in data circulation and control, leading to the emergence of diverse intelligent integration scenarios like model factories and MaaS services [2][5] Data Engineering and AI Integration - Data engineering is identified as the core of future R&D for AI companies, information technology firms, and traditional manufacturing enterprises [2][5] - The integration of business, AI, and data is essential for releasing data value, transitioning from traditional data capabilities to AI-driven data construction [2][5] Intelligent Transformation Logic - The core logic of enterprise digital transformation is to build knowledge engineering based on data, utilizing AI large model training to supply high-quality datasets [6] - This process aims to create a business closed loop of "data - model - application - data," facilitating iterative improvements in data engineering [6] - The transformation not only reduces costs and enhances efficiency for enterprises but also supports the high-quality construction of data assets in the zero-level market of the data factor market [6]