近日，工业和信息化部等八部门印发的《"人工智能+制造"专项行动实施意见》提出，推动建立企 业首席数据官制度，持续推进数据管理能力成熟度国家标准贯标，夯实企业数据治理基础。 这并非首份提及首席数据官（英文简称为CDO）的重磅文件。近年来，《加快数智供应链发展专 项行动计划》等多份文件鼓励建立首席数据官制度，将这一群体逐渐推到聚光灯下。作为数字时代 的"新C位"，首席数据官是数字资产的"掌舵人"，肩负着管理和运用企业数据资产、充分挖掘数据价 值、驱动业务创新和业务转型变革的关键使命。 "据不完全统计，全国已有超过160个城市建立了政府首席数据官制度。"长期研究数据治理的电子 科技大学公共管理学院副教授韩啸告诉科技日报记者，近年来，政府部门、企事业单位对首席数据官的 重视程度正在快速提升，呈现出从试点探索到全面推广的发展态势。 首席数据官是个什么"官"？他们面临着哪些挑战？未来发展前景如何？科技日报记者采访了多位专 家。 激活"沉睡"的数字富矿 2019年，党的十九届四中全会首次将数据列为与劳动、资本、土地、知识、技术、管理并列的生产 要素。 "就像'体检单'，企业健康与否一目了然。"杨平介绍，以前银行贷前调查要派 ...

Core Insights - The 2025 Data Asset Summit organized by Sanwei Tiandi focused on the development path of data assetization, emphasizing the dual-driven innovation of "AI + Data" [1][2] - The chairman of Sanwei Tiandi highlighted the rapid evolution of data governance frameworks and standards, driven by new policies, models, and technologies [1] - The integration of AI and data is seen as a core driving force for new productive capabilities, with a focus on cost reduction, efficiency improvement, and quality enhancement [2] Group 1: Data Governance and Strategy - Sanwei Tiandi proposed a "Three-Stage, Five-Step Method" for data governance, aiming for comprehensive visibility, control, usability, and trust in data assets [2] - The transition of data from resources to capital involves four stages: resourceization, productization, assetization, and capitalization, requiring foundational work in data asset inventory, quality assessment, and compliance registration [3] - The company plans to deepen ecological collaboration, technological innovation, scenario expansion, and product iteration to support clients in achieving intelligent transformation [3] Group 2: Industry Applications and Case Studies - The CIO of Tianyao Darentang shared insights on the pharmaceutical industry's transformation, noting that AI has evolved from a tool to a core engine for collaborative evolution in healthcare [2] - TCL's data management director discussed overcoming data silos by establishing a layered control system, achieving significant data cleansing and standardization over five years [2] - Experts from various institutions provided multi-dimensional references on element integration, master data governance, and practical applications of DCMM [3]