在数字化转型浪潮中，企业数据系统建设方案的科学性直接决定数据价值释放效率，而明确企业如何建设数据系统、认知数据治理系统对企业的应用价值， 成为企业突破数据孤岛、实现精细化运营的关键。许多企业在建设中面临标准混乱、消费不畅、兼容不足等难题，亟需成熟工具支撑全流程落地。本文将聚 焦 5 款主流数据治理与数据中台产品，重点解析阿里云旗下方案实践，为企业建设提供参考。 一、瓴羊 Dataphin（阿里云旗下数据治理 / 数据中台产品） 作为阿里巴巴全资子公司瓴羊智能科技的核心产品，Dataphin 是阿里云旗下企业级数据建设、治理、运营一体化平台，源自阿里十余年内部实践的产品化输 出，已服务超 5 万家企业，覆盖 20 个行业。 1. 核心功能与长尾词适配 2. 典型案例 财通证券借助 Dataphin 打通多系统数据，实现标准统一与即时接入，生成 300 + 市场标签，从全量运营转向精细运营，提升业务转化效率；台州银行通过其 构建统一数据门户，实现资产集中管理与可视化决策，加速小微业务创新。 二、腾讯云 WeData 腾讯云推出的全链路敏捷数据开发治理平台，聚焦 DataOps 协作与实时分析能力，深度适配金融与泛 ...

通过开发统一和无代码报表工具，油田将数据接口的平均交付周期从32.3天大幅压缩至2天，实现了数 据服务模式从"被动响应"到"主动赋能"的转变。同时，实施全流程数据质量管控，累计整改问题数据超 231万条，显著提升了数据可信度。 近日，胜利油田"数据治理赋能企业数字化转型"案例成功入选石油和化工行业"十四五"数字化转型优秀 案例，这标志着油田在数字化转型领域的创新实践成果成为行业标杆，为石油化工企业提供了可复制、 可推广的示范经验。 近年来，胜利油田以"数据驱动、平台赋能、服务协同"为核心，建立了覆盖全业务、全生命周期的数据 治理体系。其间，由主要领导牵头成立数据治理委员会，构建了"业务主导、技术支撑、管理提升"的协 同机制，系统梳理了68家单位、555个数据源、超18万张数据表、430余万个数据项，并建成统一数据 湖，有效打破了"数据孤岛"。 数据治理为油田带来显著效益：油气生产数据采集时间缩短75%，单井结算环节节约12小时，全年累计 节省工时超53万小时。2024年，油田率先通过数据管理能力成熟度评估四级认证，成为国内油气领域首 家获此认证的单位。(大众新闻记者顾松通讯员李贻晨徐萌) ...

Core Insights - Real World Data (RWD) is becoming a crucial engine for reshaping healthcare decision-making and promoting high-quality development in the medical industry [1][2] Summary by Sections RWD as a Policy Basis - Traditional healthcare policy relies on Randomized Controlled Trials (RCTs), which have limitations in population coverage and fail to reflect the multidimensional value of drugs. RWD addresses these issues by providing comprehensive data from various sources, including electronic health records and insurance claims [2] - RWD has three core advantages: broad population coverage, rich data dimensions, and continuous time span, allowing for dynamic tracking of patient treatment trajectories and long-term safety data [2] Empowering the Healthcare Industry - RWD can resolve challenges such as stalled drug development for rare diseases and inefficient chronic disease treatment plans due to lack of data support. It enhances the entire healthcare process by breaking down data barriers and providing real-world insights [5] - In drug development, RWD allows pharmaceutical companies to gather data on drug effectiveness and safety across diverse populations, accelerating the drug development process and increasing success rates [5][6] Clinical Application and Payment Decision - RWD aids in identifying clinical gaps and standardizing services, enabling healthcare institutions to improve treatment processes and service quality [6] - In payment decision-making, RWD quantifies the real value of drugs and services, serving as a scientific benchmark for healthcare funding allocation and optimizing resource distribution [6] Releasing Maximum Value from RWD - To fully leverage RWD, efforts must focus on data governance and the establishment of evaluation systems. A unified healthcare information platform has been created, but further development of a real-world database and standardization of data is necessary [8] - An evaluation mechanism centered on RWD should be established, covering three key stages: pre-market comprehensive value assessment, post-market entry evaluation, and ongoing re-evaluation of drugs within the healthcare system [9] Current Status and Future Directions - While significant progress has been made in applying RWD in healthcare decision-making, challenges remain, including data sharing mechanisms, analytical capabilities, and privacy concerns. Continuous efforts are needed to build a robust RWD-based decision support system [9][10]

Core Points - The Ministry of Commerce and five other departments released a work plan to support Beijing in implementing the WTO Electronic Commerce Agreement, focusing on enhancing digital trade and governance [1][2][3] Group 1: Digital Trade Enhancement - The work plan emphasizes improving the digitalization of trade, including the promotion of electronic documents such as electronic bills, warehouse receipts, invoices, and contracts [1] - It aims to facilitate paperless trade through the Beijing "dual hub" airport service platform, ensuring a fully paperless process for customs supervision and business operations [1] - The plan includes expanding the "single window" service to support advance declaration for customs clearance and creating fast-track systems for large events in Beijing [1] Group 2: Data Governance - The work plan outlines tasks to support the establishment of a comprehensive data factor pilot zone in Beijing and the implementation of a data registration system [2] - It aims to enhance the role of the Beijing International Data Trading Alliance to increase its influence in the data sector [2] - The plan focuses on improving cross-border data flow convenience and expanding the "green channel" services for data export [2] Group 3: International Cooperation - The work plan supports Beijing in broadening cooperation with WTO members in areas such as personal data protection, data flow, and digital trade standards [2] - The WTO Electronic Commerce Agreement, reached by 71 members including China, aims to create a stable and predictable regulatory environment for global digital trade [3] - China is the first WTO member to implement the agreement, marking a shift from leading rule-making to leading rule implementation [3]

在数字经济浪潮奔涌、AI技术日新月异的今天，数据作为新型生产要素，其治理水平直接关系到企业 的数字化转型成败与核心竞争力。近日，国内数据治理领域的领军企业——中翰软件正式宣布，已基本 完成其数据治理产品体系的智能化改造，成功构建以AI技术为基座的"AI+软件"新一代数据治理平台。 这标志着中翰软件不仅再次引领行业技术变革，也预示着企业数据治理正式从"成熟阶段"跨越至全新 的"智能阶段"。 二十年砥砺前行：一部不断自我革新的数据治理发展史 中翰软件的成长史，几乎与中国企业数据治理的演进史同频共振。公司成立于2006年，彼时，企业财务 电算化刚刚普及，整体信息化尚处初级阶段。中翰软件以其敏锐的市场洞察力，捕捉到企业对规范数据 管理的初步需求，推出了UMC-物资编码管理平台，开启了数据治理以编码管理为核心的"起步阶段"， 为企业奠定了数据标准化的基石。 随着企业信息化初步成型，业务系统增多，"数据孤岛"问题显现。2010年前后，中翰软件在国内首家推 出主数据管理（MDM）概念及系统，通过数据标准化促进共享，推动数据质量治理进入"过渡阶段"。 此后，公司持续创新，版本迭代迅速：2011年发布MDM 3.0，2012年 ...

为实现数据接入全流程高效管控，新疆电力公司精心打造以 "元数据维护 - 需求获取 - 自动化接入 - 质 量校核 - 目录挂载 - 链路监测"全闭环体系。在元数据支撑上，历时 3 个月建立全链条管理体系，制定 29 项规范，保障 9 套核心系统 700 余万字段、9 万余张表的元数据质量，动态监测异动并闭环处置。接 入流程上，整合总部任务与省侧工单，依托元数据自动生成待接入清单；创新开发数据接入自动化工 具，按数据库类型差异化配置接入方式，通过调用华为数据中台接口与 RPA 协同，实现 CDM 全量 / 增 量、DRS 增量作业批量创建，单界面即可完成操作。质量与监测方面，创新 "接入即校核""上传即校 核" 机制，自动生成四类校验规则；贯通全链路监测，1.5 万余条链路实现异常自动告警，确保数据可 靠可用。 "一站式" 自动化接入工具彻底革新接数模式：将以往跨平台、多流程节点配置的繁琐操作，简化为单 一界面完成所有配置，大幅缩减操作环节与业务复杂度；效率层面，单张表接入时间显著缩短，效率提 升 86.7%，同时实现接数流程 "标准化" 与 "智能化"，新入职技术人员上手周期大幅压缩，显著降低人 员培训成本， ...

Core Insights - Shengli Oilfield's "Data Governance Empowering Enterprise Digital Transformation" case has been recognized as an excellent example of digital transformation in the petroleum and chemical industry during the 14th Five-Year Plan, establishing a benchmark for innovation practices in the sector [1] Group 1: Digital Transformation Achievements - Shengli Oilfield has established a comprehensive data governance system covering all business areas and the entire lifecycle, driven by "data-driven, platform-enabled, and service-coordinated" principles [1] - A data governance committee led by senior management has been formed, creating a collaborative mechanism that integrates business leadership, technical support, and management enhancement [1] - The oilfield has systematically organized data from 68 units, 555 data sources, over 180,000 data tables, and more than 4.3 million data items, resulting in the establishment of a unified data lake that effectively breaks down "data silos" [1] Group 2: Efficiency Improvements - The development of a unified no-code reporting tool has significantly reduced the average delivery cycle of data interfaces from 32.3 days to 2 days, transforming the data service model from "passive response" to "active empowerment" [1] - Full-process data quality control has been implemented, resulting in the rectification of over 2.31 million problematic data entries, which has notably enhanced data credibility [1] - Data governance has led to substantial benefits, including a 75% reduction in oil and gas production data collection time, a 12-hour savings in single well settlement processes, and a total of over 530,000 hours saved annually [1] Group 3: Recognition and Certification - In 2024, Shengli Oilfield will be the first in the domestic oil and gas sector to achieve a Level 4 certification in data management capability maturity assessment [1]

Core Viewpoint - The retail industry is transitioning from high-speed growth to stock competition, necessitating the digital transformation of "people, goods, and venues" through the integration of generative AI and data applications to reshape growth trajectories [1][2][44]. Group 1: Digital Transformation in Retail - The shift from a demand-driven economy to a member-based economy is evident as consumer rationality increases, prompting companies to focus on user retention and value extraction [4]. - Retailers must leverage digital technologies to enhance consumer insights, expand touchpoints, and optimize product selection and promotion based on data [2][6]. Group 2: Generative AI and Data Applications - Generative AI and data governance are crucial for maximizing AI value, with 71% of companies planning to strengthen data-driven decision-making [20][23]. - The integration of generative AI in marketing and customer service is leading to significant efficiency improvements, with over 90% of companies adopting these technologies [48][51]. Group 3: Sector-Specific Insights - In the beauty sector, domestic brands are rapidly increasing market share from 43.7% in 2022 to 55.7% in 2024, utilizing KOLs and UGC to establish a marketing loop [9]. - The footwear and apparel industry faces intense competition, requiring companies to develop proprietary product capabilities and brand recognition to stand out [11]. - The home goods sector is shifting towards overseas expansion, with companies focusing on building their own brands rather than merely exporting [14][66]. Group 4: Global Market Expansion - 93% of retail companies are pursuing overseas business, with Asia-Pacific, Europe, and North America as primary targets due to their high purchasing power and mature channels [66]. - Generative AI is facilitating localization efforts by overcoming language and cultural barriers, enabling efficient marketing and customer service in foreign markets [69]. Group 5: Supply Chain and Decision-Making Enhancements - Generative AI is optimizing supply chain efficiency by improving demand forecasting and real-time decision-making, with efficiency improvements ranging from 10% to 30% [62]. - The integration of generative AI in internal decision-making processes is transforming traditional experience-driven approaches into data-driven strategies [42][56].

来源：新华网 9月20日，以"智能仿真与数据治理"为主题的2025国际仿真大会在浙江杭州开幕。来自30多个国家和地 区的高校、科研机构及企业的1000多位科技代表齐聚一堂，共话仿真技术前沿发展与未来走向。 新技术发布环节亮点纷呈：达索系统推出"数字心脏"高精度医疗仿真平台，中国飞机强度研究所发布 CAE软件SABRE2025，上海桓领公司展示的国产流体仿真软件ASO在部分应用中效率显著提升。 大会同步设置14个专题分论坛，覆盖数据治理、人工智能、科学计算、生命科学等领域，为产学研深度 融合与跨界合作搭建交流平台。北京航空航天大学与巴西国家理论与应用数学研究所（IMPA）签署合 作协议，80余家单位就产业对接、企业出海、技术合作等达成合作意向。 会议期间还举办闭门会议、产业对接会、企业出海咨询会及仿真技术标准研讨会，并配套举办首届国际 仿真科技展，集中展示工业仿真软件、工业超算、航空航天、低空经济、汽车仿真等领域先进技术与产 品。会后，参会代表赴杭州科技企业及科研机构实地考察，进一步推动仿真技术产学研用紧密对接。 （刘彦青/文 国际仿真大会组委会/供图） 本次大会聚焦建模仿真技术在数字化转型中的关键价值，明确提 ...

Core Insights - The World Trade Organization's report highlights the significant impact of artificial intelligence (AI) on global trade dynamics, emphasizing the need for multilateral cooperation to ensure inclusive growth rather than exacerbating disparities [1][5] Group 1: Opportunities and Potential of AI in Trade - AI technologies can enhance efficiency in various sectors such as consulting and R&D, potentially increasing global total factor productivity by an additional 0.68% annually [1] - By 2040, global trade is projected to increase by 34% to 37%, with global GDP rising by 12% to 13%, and trade in digitally deliverable services, including AI services, expected to grow by 42% [1] - AI can optimize supply chains, improve customs and compliance efficiency, reduce cross-border communication costs, and assist small and medium-sized enterprises (SMEs) in entering international markets [1][2] Group 2: AI's Role in Market Participation - 90% of companies utilizing AI report trade benefits, with 56% indicating improved risk management capabilities [2] - AI can facilitate broader market participation, particularly benefiting SMEs and developing countries by overcoming barriers such as high compliance costs and insufficient market information [2] - The technology can provide new export opportunities for low-income countries through remote services and online applications [2] Group 3: Risks and Challenges Posed by AI - AI may exacerbate the digital divide, with high-income economies having advanced capabilities compared to low-income countries, potentially leading to a 14% income growth in high-income countries versus only 8% in low-income countries by 2040 [3] - The disparity in AI adoption rates, with over 60% in large enterprises compared to 41% in small enterprises, highlights the challenges faced by lower-income economies [3] - AI's impact on labor markets could threaten jobs in sectors like translation and customer support, which are crucial for low-income countries' export opportunities [3] Group 4: Policy Recommendations for Inclusive Growth - Countries are urged to prepare policies, infrastructure, and capacity building to support AI development, including maintaining low tariffs and open markets [4] - Investment in education and training programs for AI, as well as improvements in data governance and infrastructure, are essential to bridge the skills gap [4] - Support for SMEs in AI adoption and a balanced approach to intellectual property and competition policies are necessary to prevent market concentration [4] Group 5: Importance of International Cooperation - The report stresses the need for enhanced international cooperation in AI governance, particularly in trade-related aspects, to avoid regulatory fragmentation [5] - Inclusive collaboration should involve both high-income and low-income countries in the global AI governance framework [5] - Strategic actions today will determine AI's future impact, with proactive measures potentially transforming AI into a driver of global trade and inclusive growth [5]