国家数据局就《关于培育数据流通服务机构 加快推进数据要素市场化价值化的意见（征求意见稿）》 向社会公开征求意见。其中提到，推动数据流通服务平台企业专业化发展。支持各类数据流通服务平台 企业围绕产业链、供应链和平台生态，促进数据流通利用和价值共创。支持产业互联网平台企业围绕产 业链数据开发利用，创新数据汇聚加工、流通交易等新模式。支持数据基础设施运营方以数据基础设施 为载体，发挥保障数据安全合规高效流通的优势，探索多主体共同开展数据价值共创的新机制。鼓励云 服务平台企业强化数据汇聚、生态链接功能，探索"一站式"数据流通利用新模式。 (本文来自第一财经) ...

来源：视觉中国 鲜涛分析，当前通用商业大数据服务行业面临三大痛点：一是数据原材料市场还不成熟，公共数据的开放范围和深度有待提升；二是制度建设明显跟不上 技术发展和市场需求；三是复合型数据人才严重短缺。"这些痛点相互交织，共同构成了行业需要跨越的主要障碍。" 在鲜涛看来，企查查的上市意义重大。"其示范意义主要体现在两个方面：资本市场认可度的跃升与行业背书，以及为非金融领域企业树立估值标杆。"他 强调，企查查成功登陆资本市场，本身就是对数据服务行业商业模式的高度认可，为同类型企业提供了关键的实践依据。 近日，中国市场监管报刊发《商查平台应为政府治理赋能》一文，引发业界对商查平台与政府部门协作关系的深入思考。文章指出，商查平台应成为政府 部门提升治理效能的数字化帮手，通过整合分散的市场监管、司法、知识产权等公开数据，有效降低信息获取门槛，助力构建诚信经营、公平竞争的市场 环境。 在此背景下，企查查科技股份有限公司冲击IPO的举动备受瞩目。10月10日，企查查向上海证券交易所主板提交IPO申请并获受理，成为国家数据局等17 部门联合发布《"数据要素×"三年行动计划（2024—2026年）》以来，上交所首家受理拟IPO ...

Core Viewpoint - The construction of a digital strong province in Shandong during the 14th Five-Year Plan period has made significant progress, focusing on six key areas of development that enhance the digital economy and infrastructure, ultimately supporting high-quality economic and social development [1][7]. Group 1: Data Value Release - Shandong has achieved breakthroughs in data value release by promoting market-oriented data element allocation reforms and establishing a comprehensive data platform, providing over 54 billion data sharing services [1]. - The province has launched the first marine big data trading service platform in the country, with a total transaction volume exceeding 1.92 billion yuan since 2023 [1]. Group 2: Digital Economy Development - The digital economy is projected to account for over 49% of GDP by 2024, supported by over 600 major technology innovation projects and the establishment of 30 digital industry clusters [2]. - Shandong ranks third nationally in the number of core digital economy entities, with 416,400 legal entities in the sector [2]. Group 3: Digital Government Construction - The digital government initiative has seen significant advancements, with the "Love Shandong" service platform registering 120 million users and providing over 1.2 billion electronic certificates [3]. - The province has implemented a unified "Lu Tong Code" and a policy fulfillment platform that has disbursed over 10 billion yuan in funds [3]. Group 4: Digital Society Construction - Shandong has made progress in building smart cities, with all 16 cities achieving "city brain" status and ranking first in the number of cities in the national digital city top 100 [4]. - The province has developed a comprehensive digital public service system, enhancing services in education, employment, and healthcare [4]. Group 5: Digital Infrastructure - Shandong has established two national-level backbone internet connection points, becoming the first province with a "dual hub" status, and has opened 272,000 5G base stations [5]. - The total computing power in the province exceeds 23.18 exaflops, with intelligent computing power accounting for 51.04% [5]. Group 6: Digital Development Environment - The province has developed a robust regulatory framework, issuing 823 standards in the digital field and enhancing network and data security measures [6]. - Shandong is actively promoting digital literacy and has initiated various campaigns to foster a strong digital development atmosphere [6].

重庆高速集团已构建起覆盖全路网的智慧化运营体系，为公众出行提供更安全、高效的交通服务。图为 工作人员在该集团智慧运行中心查看高速公路相关实时数据。孙凯芳摄（人民视觉） 12月22日，国家数据局发布第四批次30个公共数据"跑起来"示范场景。加上前三批次，目前已有100个 示范场景对外发布。国家数据局副局长陈荣辉表示，为充分释放公共数据价值，国家数据局组织实施公 共数据"跑起来"示范场景建设。下一步，将持续推进示范场景建设与推广，推动场景从"点上突破"走 向"面上开花"，以公共数据开发利用引领撬动各方数据融合应用，更好释放数据要素价值。 场景领域不断拓展 公共数据开发利用是数据要素市场化配置改革的先导工程。当前，公共数据"跑起来"示范场景建设进展 如何？在国家数据局12月22日举行的第四场"数据价值化 我们在行动"新闻发布会上，陈荣辉介绍了3个 方面的情况。 打造场景样板，数据赋能作用显现。第一批公共数据"跑起来"示范场景经过一年建设，各项任务已经基 本完成。社会效益和产业带动作用初步显现，形成了可复制、可推广的建设模式。比如，在产业发展领 域，海关总署依托跨境贸易通关数据，助力中小微企业降低融资成本；物流数据开 ...

宋李民 编者按： "十五五"规划建议提出，健全数据要素基础制度，建设开放共享安全的全国一体化数据市场，深化数据 资源开发利用。国家数据局多次发声，呼吁全社会加大对数据领域的投入，着力培育"为数据付费买 单"的市场意识。这一倡导，不仅是对数据要素市场化配置的顶层指引，更是打通数据价值转化堵点、 构建健全数据要素市场的关键一步。多位专家将深入解读数据资源开发利用的市场培育逻辑与实践路 径。 随着人工智能技术从"以模型为中心"向"以数据为中心"转变，高质量数据已成为决定AI系统能力上限、 驱动产业智能化升级的核心要素。在此背景下，培育数据付费意识，建立健全科学高效的付费机制，不 仅是深化数据要素市场化配置改革的关键举措，更是"十五五"时期全面实施"人工智能+"行动的重要保 障。 数据付费是激活"人工智能+"新动能的必然要求 数据的价值并非蕴藏于静态存储之中，而是在具体应用场景中被激活和创造，只有当市场愿意为高质量 数据支付合理费用时，其潜力才能充分释放。 在人工智能迈向高质量发展的重要阶段，加快培育"为优质数据付费"的市场意识尤为迫切。这不仅关乎 能否构建健康、可持续的商业模式，更关乎我国能否在数智时代把握发展主动 ...

2025年，数据要素市场化改革步入"深水区"。政策持续发力，技术加速迭代，企业如何抓住数据要素带来的战略红利，将海量数据真正转化为驱动增长的 核心资产？ 由大数据技术标准推进委员会（CCSA TC601）组织百家领先企业和权威专家共同编制的《数据资产管理实践指南（8.0版）》正式发布！作 为连续九年迭代的行业"风向标"，今年的8.0版首次系统描绘了从 "数据资源化"到"数据资产化"再到"数据资本化" 的完整价值跃迁路径，是各行各业管理 者、数据从业者把握未来方向的必备工具书。 一、核心升级：首次清晰勾勒数据价值化"三级跳" 过去，我们谈数据治理，多聚焦于质量、安全和内部应用。8.0版指南明确指出，数据资产管理已发生范式跃迁： 资源化（打好地基）：解决数据"有没有、好不好、安不安全"的问题，是价值释放的前提。指南详解八大管理职能（模型、标准、质量、安全等）的内在 联系与智能化演进。 资产化（实现价值）：核心是让数据的价值"看得见、摸得着、可计量"。指南系统阐释了数据资产登记、确权、估值、成本核算、入表、内外部流通六大 核心管理活动，直击企业最关心的"如何入表"、"如何定价"、"如何交易"等实操难题。 资本化（放 ...

Core Insights - The rise of data labeling as a new profession in Guizhou's mountainous regions is driven by the increasing demand for high-quality datasets in the context of the digital economy and generative artificial intelligence [1][3]. Industry Overview - Guizhou is actively cultivating a complete data labeling industry chain, with policies in place to incentivize companies. For instance, companies achieving 300 data labeling employees or 30 million yuan in revenue can receive a reward of 1 million yuan, with the highest reward reaching 10 million yuan [3][5]. - The local data labeling industry has begun to take shape, with 67 companies and over 9,100 employees currently involved [5]. Employment and Talent Development - The South National Data Labeling Incubation Base has expanded its workforce to over 700 employees, with plans to create more than 2,000 jobs in the future [3][5]. - The integration of education and industry is emphasized, with partnerships established between local educational institutions and companies to provide practical opportunities for students. Over 400 students have interned at the company, with about 20% securing positions post-internship [4][5]. Technological Advancements - The nature of data labeling work is evolving from simple tasks to more complex roles requiring specialized knowledge, driven by advancements in AI technology [5]. - Companies are collaborating with educational institutions to develop their own labeling platforms to enhance efficiency in data processing [5]. Economic Impact - Guizhou's digital industry is projected to grow at an annual rate of 16.2%, with an expected scale of approximately 280 billion yuan by the end of the year, including software and IT services surpassing 100 billion yuan [5].

（据新华社电） 《 人民日报 》（ 2025年12月18日 11 版） (责编：胡永秋、杨光宇) 走进贵州贵阳市白云区的一家数据标注孵化基地，此起彼伏的鼠标点击声传来。项目经理赵玉徕正在指 导组内的年轻人在电脑屏幕上将一张张真实道路图片里的车辆、路沿、红绿灯逐一框线标注，再精准填 写"无遮挡""路沿高15厘米"等属性信息。另一边的年轻人，正逐帧为动漫画面补充解释说明。 近年来，随着生成式人工智能、数据要素市场化加快推进，高质量数据集需求旺盛。在此背景下，贵州 的山区县乡不断"长"出一座座数据标注基地。为数据打上标签，正成为贵州年轻人的新职业。 2025年，贵州出台鼓励政策，对数据标注企业给予奖励：企业当年数据标注从业人员首次达300人或营 收达3000万元，即可获得100万元奖励。根据企业规模，奖励分为多档，最高奖励达1000万元。"我们已 建成多个企业特色园区和超8万平方米的白云数据空间产业化基地。"白云区大数据发展管理局局长吴利 豪介绍，白云区规划数个数据标注基地，预计将带动超5000人就业。 "要撑起这样大规模的产业布局，人才是关键。"吴利豪介绍，为此，白云区已与当地多所开设大数据专 业的院校建立紧密联 ...

近日，天津市数据局公布"2025年天津市可信数据空间创新发展试点名单"。浪潮智慧科技（天津）有限公司作为联合申报单位之一，成功入选"企 业可信数据空间创新发展试点"。这不仅体现了公司在数据可信流通与要素市场化配置方面的技术实力与实践能力获得权威认可，也为其进一步参 与天津数据要素市场建设奠定了坚实基础。 本次试点工作是天津市深化数据要素市场化改革的重要举措，旨在聚焦可信数据空间在多行业、多企业层面的创新应用场景与可持续运营模式， 打破数据流通壁垒，保障数据安全有序流动，充分释放数据要素核心价值，助力天津数字经济高质量发展。浪潮智慧科技凭借与天津东疆星链数 字科技有限公司等合作方的协同优势，成功获得试点资格，并将共同开展企业级可信数据空间的试点建设。 此次入选是浪潮智慧科技与浪潮云协同发力、共拓数据要素市场的重要里程碑。未来，双方将进一步深化协作，联合各界合作伙伴，深耕企业可 信数据空间的多元应用场景，积极探索数据资产化与资本化的创新路径，为天津市构建健康繁荣、安全可控的数据要素市场生态贡献浪潮力量。 | 序号 | 申报单位 | 试点名称 | | --- | --- | --- | | | | 企业可信数据空间 ...

Core Viewpoint - The article emphasizes the importance of establishing a payment mechanism for high-quality data as a fundamental requirement for the marketization and valuation of data, which is essential for fostering a robust data market and enhancing the digital economy's growth potential [1][2][4]. Group 1: Importance of Paying for Quality Data - Paying for quality data is essential for recognizing its intrinsic value as a new production factor and is a key measure to overcome current data circulation challenges [1][2]. - The payment mechanism serves as a cost compensation and innovation incentive for data "factorization," ensuring that the value of data is recovered and distributed effectively [2][5]. - The market demand for quality data is rapidly increasing, with over 4,500 data products listed on the Shanghai Data Exchange in 2024, leading to a transaction volume exceeding 4 billion RMB, reflecting a diverse supply and rising value [3][4]. Group 2: Mechanisms and Challenges - The payment mechanism acts as a benchmark for data "valuation," helping to reflect market demand for different types and qualities of data, thus guiding data flow to areas where it can create the most value [4][5]. - There are significant challenges in recognizing the value of quality data, including a lack of stable payment expectations and insufficient capabilities of data circulation service institutions [7][8]. - The establishment of a payment mechanism is crucial for building a fair and efficient data market system, addressing issues like information asymmetry and enhancing transaction efficiency [5][6]. Group 3: Market Awareness and Cultural Shift - Cultivating a market culture that values payment for quality data is necessary for deepening market-oriented reforms and stimulating the internal growth of the digital economy [8][9]. - Government departments are encouraged to take the lead in establishing data transaction-related systems and promoting a consensus on the value of data [8][9]. - Enterprises should recognize the strategic value of paying for quality data as a means to enhance competitiveness and innovation capabilities, viewing it as a critical investment for future growth [9][10]. Group 4: Future Directions - There is a need to accelerate the development of a data factor market and enhance the positive incentives for paying for quality data, which will help eliminate low-quality data and ensure reasonable returns for quality providers [10][11]. - Establishing a unified data quality governance system and developing third-party evaluation and certification mechanisms are essential for improving market trust and reducing risks associated with information asymmetry [10][11]. - Supporting various data transaction models and financial products, such as data asset pledge financing and data insurance, will facilitate the flexible allocation of data across different scenarios [10][11].