允中 发自 凹非寺 量子位 | 公众号 QbitAI 科技赛道从不缺"造梦者"，但能精准击中行业痛点的"破局者"往往寥寥。 在ToB世界里，真正称得上"标杆"的，或许不是那些自称 "通用AI模型玩家"的公司，而是另一类更务实的路径： 把数据整合、数据治理做深做透，帮助企业打破数据壁垒，把零散信息沉淀为可落地、可复用的智能资产。 这种"以数据赋能行业"的逻辑，让它们成为科技领域的独特存在。 如今，这一逻辑正在炙手可热的具身智能赛道被复刻。一家名为 简智机器人 的企业，不下场卷模型、不砸钱堆硬件，而是把精力投在 数据 治理与产线设计 上。 成立4个月就完成 3轮融资 、累计金额 超2亿元 ，服务 30余家 具身智能头部公司， 70%以上收入 来自海外。 要理解这家公司为何在短短数月内被资本和头部玩家集体押注，得先回到一个更底层的问题： 具身智能真正难在什么地方。 具身智能的核心瓶颈：数据困境远比想象中复杂 没人否认具身智能是AI的下一站，但要让机器人像人类一样灵活穿梭于物理世界，光有强大模型和充足算力远远不够。 行业早已形成共识： 数据，才是横亘在面前的强大壁垒。 而且 不同于语义文本可直接从互联网中获取 ，具身 ...

中国信通院的评测结果，对标国际数据治理规则，针对数据采集、清洗、标注到流通应用等全链条构建 评测规范，是国内企业参与国内数据交易、跨境数据流通及行业合作的重要信用凭证，有助于显著降低 数据应用方的信任成本与合规风险，在行业内具备高认可度。 中国信通院此次围绕数据集质量、合规性、应用价值等核心维度，严格依据数据智能服务产业相关标 准，对"新华.文风训练数据集"开展了专业化评测。该高质量数据集，是依托新华高质量数据集运营管 理系统汇聚的海量真实稿件数据，构建覆盖13种典型文风的基准数据集，为文风生成模型提供精准、多 元的训练语料。 本报讯(记者聂伟)12月13日，新华报业传媒集团"新华.文风训练数据集"获得由中国信通院颁发的高质量 数据集检测证书。据悉，这是全国首张传媒领域高质量数据集检测证书，标志着新华报业在数据治理、 数据集标准化建设与价值转化方面的实践获国家级权威认可，也为传媒领域数据要素合规流通与高效应 用提供了可借鉴的"新华方案"。 ...

"十四五"期间，我国石化行业数字化转型成效显著，但仍面临深层挑战。近日在天津举行的2025中国石 油和化工民营经济高质量发展大会——生产与科技服务业高端发展论坛上，专家指出，下一步，行业需 聚焦数据治理、系统融合和人才培育，联合院校、服务商等多方协同攻坚，在数字化转型的"深水区"中 走实走稳。 行业数字化转型提速安全效益实现"双赢" 此外，人才结构失衡成为制约企业深度转型的普遍瓶颈。段卫华直言："从企业的角度出发，现在需要 的是既具有行业知识又懂编程和数字化的人才。这类人才可以说是少之又少。" 而在生产一线，数智技术的应用带来了更多实实在在的好处。天津渤海化学（600800）股份有限公司副 总经理俞增亮表示，"十四五"期间，智能工厂核心系统已在公司全面落地，实现了安全风险的层级管 控，违章行为识别准确率超过92%，隐患闭环率达到98%。 彩客化学集团有限公司党委书记、副总裁段卫华也提供了一组数字：彩客集团子公司山东彩客新材料公 司重点打造了先进过程控制、智能仓储等8个智能制造场景，被评为山东省数字化车间，实现生产效率 提升27%，能源利用率提高38%，产品不良品率降低11.34%。 "这些数据充分印证了石化企 ...

由新华社品牌工作办公室、新华网、新华社海南分社主办的2025企业家博鳌论坛系列活动于12月2日至5 日在海南博鳌举办。作为论坛系列活动之一，2025AI生态共建论坛暨《睿德数字经济创新发展大数据 平台》发布会于3日在博鳌举办。 贾昊表示，当前AI发展正从通用普惠阶段深化至垂直行业应用阶段，但在规模化落地过程中，仍面临 数据治理、算力成本、安全可信及复合型人才短缺等挑战。人工智能的未来既需要开放拥抱，也离不开 理性建构。他认为，只有在数据可信、安全可控、人才充沛的前提下，持续推动技术与行业的深度融 合，才能真正赋能千行百业，助力数字时代高质量发展。 在人才跨界融合方面，覃卓认为，当前兼具专业技术与行业洞察能力的复合型人才较为稀缺。为系统化 培养适应时代需求的未来人才，北大法宝与多所高校合作，共同开设法律大数据、法律AI等课程，着 力推进产学研协同育人。在提升垂类AI可信安全方面，他介绍，北大法宝基于二十余年积累，已建立 严谨的数据采集、标注与审核流程，并通过为AI回答提供法条原文链接等方式，确保结果可溯源、可 验证，有效维护信息准确性，增强用户信任。 本次圆桌论坛聚焦垂直领域AI落地的真问题、真场景及安全问题， ...

简而言之：有效的数据治理并非在于控制，而在于赋能更快、更值得信赖的决策。探索切实可行的策略，构建能 够赋能团队而非限制团队的治理框架。 大多数设计糟糕的数据治理方案之所以失败，是因为它们像安全检查站一样，旨在发现问题而非预防问题。在带 领数据团队经历多次治理转型之后，我发现，最佳的治理方案应该秉持积极主动的思维，专注于创造价值，而不 是被动地试图减少歧义或仅仅关注合规性。 治理的转变在于从把关式治理转变为赋能式治理。我热爱治理领域的原因在于，如果设计得当，就能与最终用户 携手合作，并在解决他们的问题中发挥关键作用。目标是在确保数据质量和信任的前提下，实现数据和数据访问 的民主化。如果中心化团队主导治理，而分散化的团队却在执行工作，那么治理必然会失败。 负责治理的人员必须对业务有透彻的了解。目标不应侧重于管道故障等技术指标，而应侧重于与战略重点相关的 增值指标。如果客户留存是战略重点，那么在提高客户留存率的同时，用于预防客户流失的数据产品的使用量也 需要增长。 一 治理失灵之时 我在工作初期，就深刻体会到简洁、完善的治理机制的重要性。举个例子，当时市场营销团队使用追踪参数进行 归因分析，但缺乏统一标准。每个团队 ...

Core Viewpoint - The European Union (EU) is proposing a series of regulatory relaxations in digital governance, marking a significant policy shift from its previous stringent regulations, such as the General Data Protection Regulation (GDPR) and the Artificial Intelligence Act (AI Act) [1][5]. Group 1: Regulatory Changes - The EU Commission has announced a package of reforms aimed at simplifying and adjusting digital and technology regulations, including delaying the implementation of high-risk AI system regulations by up to 16 months [1][3]. - Adjustments to the GDPR will allow for more lenient reasons for rejecting data subject access requests and provide companies greater flexibility in using anonymized and pseudonymized data for AI model training [2][3]. - Changes to cookie management will enable users to control certain "low-risk" cookies through a unified browser interface [2]. Group 2: Motivations Behind the Reforms - The EU aims to enhance technological competitiveness, as previous strict regulations have created structural barriers to innovation [3][4]. - The reforms are a response to industry demands for reduced administrative burdens, particularly benefiting startups and small businesses by simplifying regulations and creating a unified European business wallet [3][4]. - External pressures from the U.S. have also influenced the EU's decision to relax digital regulations, with potential trade implications tied to U.S. tariffs on EU steel and aluminum products [4]. Group 3: Implications and Reactions - The proposed reforms reflect a pragmatic approach by EU institutions, aiming to unify regulations across AI, cybersecurity, and data governance while reducing fragmentation for companies operating across member states [4][6]. - Critics, including privacy advocates and some political figures, argue that these changes may weaken fundamental protections and shift the EU's regulatory focus towards a more industry-driven model [5][6]. - The legislative process for these reforms is expected to be contentious, requiring broad consensus among EU institutions and member states, with potential for significant political negotiations [6][7].

Core Insights - The retail industry is accelerating its digital and intelligent transformation as year-end promotional seasons approach, with successful retailers adopting a "tech company" mindset to leverage data as a strategic asset [1][5] - Data management is crucial for modern retail operations, impacting everything from inventory management to fraud detection, especially during peak shopping periods when data volume and associated risks increase [1][2] - Retailers that prioritize data visibility and control can scale their operations and enhance security while providing seamless consumer experiences [1][2] Group 1: Challenges and Pressures - The holiday peak season imposes three main pressures on retail systems: scalability, fault tolerance, and labor shortages, leading to potential system failures or performance declines during sudden traffic surges [2] - Any downtime during peak periods can result in significant sales losses and negatively impact overall customer satisfaction [2] - Transaction processing and fraud detection systems must remain stable under increased traffic, necessitating robust data management platforms that can handle additional loads without failures [2] Group 2: Consumer Trust and Data Governance - According to PwC's "2024 Consumer Voice Survey," 74% of consumers in the Asia-Pacific region are concerned about privacy and data sharing, highlighting the importance of consumer trust in retail competition [3] - Retailers must demonstrate responsible data usage to maintain customer loyalty, with a strong governance framework and zero-trust architecture being essential [3] - Security design principles can minimize data exposure risks, while a unified governance framework ensures consistent data security and compliance across mixed environments [3] Group 3: Role of AI and Real-Time Data - AI and machine learning are critical for optimizing demand forecasting, personalizing experiences, and enhancing fraud detection during peak shopping periods [3][4] - Retailers benefit from both historical and real-time data processing, as historical data informs demand prediction models while real-time data is vital for immediate responsiveness [3][4] - Real-time data collection systems enable dynamic decision-making, allowing retailers to identify anomalies instantly and push personalized offers at the right moment [4] Group 4: Conclusion - The ability to responsibly manage data, maintain system scalability under pressure, and continuously build consumer trust is becoming a key determinant of success in the competitive retail market [4][5] - Retailers that integrate data visibility and governance into their core strategies will be better positioned to stabilize operations, enhance customer experiences, and achieve business growth during critical shopping periods [5]

Core Insights - The leasing industry is facing increasingly complex data management demands, leading to the emergence of various big data governance platforms that provide robust solutions [2][3][6] - These platforms enhance data organization efficiency, ensure data consistency and accuracy, and facilitate seamless data integration across departments, thereby promoting overall business collaboration [2][3][4] - Innovative technologies such as intelligent analytics enable real-time access to critical business insights, significantly improving decision-making efficiency during digital transformation [2][4][7] Data Management Solutions - The solutions offered by companies like Jiangsu New and Huawei Cloud FusionInsight MDM focus on comprehensive data classification and management, ensuring data accuracy and consistency through effective quality control measures [3][4] - IBM InfoSphere MDM and Informatica provide strong data integration capabilities, allowing enterprises to manage data from multiple sources and enhance data availability and accuracy [5][6] - The MDG (Master Data Governance) solution from SAP emphasizes master data management, quality control, and compliance checks, significantly reducing data error rates by over 30% for 85% of surveyed companies [4] Industry Trends - The importance of data governance in the leasing industry is growing, with companies needing to select appropriate big data governance platforms to achieve efficient management [7][8] - The platforms are designed to support both large enterprises and small to medium-sized companies, helping streamline processes and ensure compliance with industry regulations [8] - Successful implementation of data governance strategies can lead to improved data accuracy and reduced time costs during digital transformation [7][8]

Core Insights - The National Bureau of Statistics of China is implementing a data efficiency enhancement initiative for state-owned enterprises, with 12 central enterprises as pilot leaders, aiming to improve data governance and resource utilization by 2027, benefiting over 100,000 SMEs [1][2] Group 1: Data Utilization and Management - State-owned enterprises are identified as key players in the development and utilization of data resources, with a focus on enhancing core competitiveness and promoting collaborative innovation in the industrial chain [1][2] - The initiative includes 10 key tasks centered around innovating data management mechanisms, fostering the data industry, enabling industrial transformation, and optimizing the innovation environment [2] Group 2: Collaboration and Innovation - The initiative encourages collaboration among state-owned enterprises and their partners across various sectors, including energy, transportation, and agriculture, to create a trusted data space and promote data sharing and innovation [2] - The National Oil and Gas Pipeline Group has created a comprehensive data network, aggregating over 10 billion core data entries, significantly improving data quality and operational efficiency in the natural gas supply chain [3] Group 3: Technological Integration - China Southern Power Grid has established a trusted data space that integrates advanced technologies like blockchain and privacy computing, enhancing data flow and collaboration among over 370 ecosystem partners [4] - China Machinery Industry Group has developed an "Agricultural Machinery Cloud" that connects the entire lifecycle of agricultural machinery, processing over 10 billion data entries daily [5] Group 4: Data-Driven Applications - China Mobile has created a big data platform that processes over 2000PB of core data, improving customer service and operational efficiency across various scenarios [6] - The China Automotive Technology and Research Center is focusing on carbon footprint accounting and data interoperability across the automotive supply chain, enhancing international rule recognition [6] Group 5: Future Directions - The initiative emphasizes the importance of enterprises taking the lead in data utilization, fostering multi-party collaboration, and integrating diverse data sources to drive industrial transformation and improve public welfare [6]

Core Insights - The article emphasizes the transformative impact of the "Artificial Intelligence +" initiative on the insurance industry, highlighting its role as a core driver for the sector's transition from traditional services to intelligent and inclusive solutions [2][3][4] - The integration of AI into actuarial practices is identified as a key area for enhancing operational efficiency and decision-making capabilities within insurance companies [4][6] Policy and Strategic Framework - The "Artificial Intelligence +" initiative was first included in the government work report in March 2024, with a commitment to further integrate digital technologies with market advantages by 2025 [2] - The State Council's recent opinions on implementing the "Artificial Intelligence +" initiative emphasize the importance of innovation in service industries and the application of AI across various sectors, including finance and logistics [2] Industry Adoption and Innovation - Insurance companies are increasingly moving from isolated AI applications to comprehensive integration across their operations, as seen in the case of Guomin Pension Insurance, which aims to reshape its entire value chain using AI [3][6] - China Ping An has focused on AI applications in the healthcare and pension sectors, developing a unique competitive edge through its "Five Intelligence" strategy, which encompasses marketing, service, operations, management, and business practices [3] Actuarial Technology Revolution - The article discusses a significant shift in actuarial technology, driven by AI, which enhances the accuracy and reliability of actuarial assumptions critical for pricing and risk management [4][5] - The application of AI in actuarial practices is expected to lead to more precise pricing and improved risk quantification, ultimately benefiting customer interests [6] Data Governance Challenges - The insurance industry faces challenges in data governance, including insufficient data integration and standardization, which hinder the effective use of AI [7][8] - Experts suggest that enhancing data governance is essential for the successful integration of AI in insurance, advocating for improved data quality control and security measures [7][8] Future Directions - The article highlights the need for insurance companies to strengthen their data governance frameworks, ensuring comprehensive data management and security to support AI initiatives [8][9] - The establishment of clear data security responsibilities and guidelines is crucial for protecting sensitive customer information and facilitating the effective use of AI technologies [9]