"绘制企业自身的数据蓝图首先要界定什么数据有用，以及有用数据在当下与未来的价值。对于高质量数据集，首先要锁定关键领域，以应用场景牵引数据 聚集，发挥数据飞轮效应。"丁涛表示，在业务实践中，国家能源集团在以下几个方面进行探索： 一是让数据赋能全产业链升级。国家能源集团业务涉及煤炭、电力、运输和化工等，依靠数据治理、数据模型引领性驱动产业链上下游协同跃迁与价值重 构，实现全要素效率跃升与生态繁荣，领航产业高质量智能化发展。。 人民网深圳8月27日电 （记者栗翘楚）随着数字技术持续迭代，数字化、网络化、智能化进程不断加快，围绕数据要素开发和价值挖掘的企业开始涌现，数 据产业加速发展。 8月26日，国家能源投资集团有限责任公司首席科学家丁涛在"2025人民数据大会"发表主旨演讲时表示，数据是企业内生需求，对于大多数企业来说，必须 要走向依靠数据、模型来进行管理，使产业链达到高效运行状态。 当前，随着全社会"用数"氛围更加浓厚，越来越多的企业投身数据市场，激活数据价值成为各界共识。有关方面的研究测算显示，中央企业成立数字科技类 公司近500家，约66%的行业龙头企业购买过数据，数据开发利用的热情不断提升。 （文章来源： ...

8月25日，运动科技公司Keep公布了截至2025年6月30日的2025上半年业绩报告。报告期内，Keep实现 营收8.22亿元（人民币，下同），非国际财报准则下经调整净利润1035万元，录得毛利4.29亿元，毛利 率由上一年度同期的46.0%提升至52.2%，实现大幅增长。 用户规模方面，2025年上半年，Keep平均月活跃用户和平均月度订阅会员分别为2249万和280万。会员 渗透率为12.4%，较2024年同期的11.1%有所提升。同时，每名月活跃用户的每月平均收入在 2025年上 半年同比增加至人民币6.1元，大幅提升。 财报显示，2025年Keep公司开启关键性战略转型，从内容驱动平台升级为AI赋能、数据驱动的健身智 能服务体。2025上半年核心进展包括：成功部署AI基础架构重构系统平台，分阶段将核心工具与功能 流重组为AI教练服务。 对于营收规模的收缩，KeepCEO王宁在业绩电话会上解释，这一收缩很大源于公司对自有品牌运动产 品做出主动的战略取舍，上半年主动进行品类结构优化及渠道精细运营等策略，收缩低毛利业务尤其是 居家大器械，发展装备、服装、穿戴等高潜力品类。"这是一次有质量的深蹲，也是下一次 ...

从内容驱动平台升级为AI赋能、数据驱动的健身智能服务体。 本文为IPO早知道原创 作者｜ Stone Jin 微信公众号｜ipozaozhidao 财报显示， 2025年Keep公司开启关键性战略转型，从内容驱动平台升级为AI赋能、数据驱动的健 身智能服务体。2025上半年核心进展包括：成功部署AI基础架构重构系统平台，分阶段将核心工具 与功能流重组为AI教练服务。 对于营收规模的收缩， Keep CEO王宁在业绩电话会上解释，这一收缩很大源于公司对自有品牌运 动产品做出主动的战略取舍，上半年主动进行品类结构优化及渠道精细运营等策略，收缩低毛利业务 尤其是居家大器械，发展装备、服装、穿戴等高潜力品类。"这是一次有质量的深蹲，也是下一次跳 跃的开始"。 据 2025年7月最新数据，Keep AI核心日活跃用户超过15万。其中，AI教练核心功能中的AI饮食记 录作为典型高频刚需场景，以低门槛的精准交互(用户仅需拍摄餐食照片，即可自动识别卡路里与营 养素)，展现出显著留存优势：该功能覆盖了1/3的AI对话用户，并呈现出深度留存特性——次日留 存率达50%，对App DAU的留存率攀升至79%。 王宁表示，截止到 7 ...

王宁表示，截止到7月，KeepAI DAU为15~20万，预计到今年年底，AI DAU可达100万以上。截止到目 前，KeepAI 收入已突破100万元人民币。此外，公司预测，2026年AI ARR将有机会突破2亿元人民币。 对于营收规模的收缩，KeepCEO王宁在业绩电话会上解释，这一收缩很大源于公司对自有品牌运动产 品做出主动的战略取舍，上半年主动进行品类结构优化及渠道精细运营等策略，收缩低毛利业务尤其是 居家大器械，发展装备、服装、穿戴等高潜力品类。"这是一次有质量的深蹲，也是下一次跳跃的开 始"。 据2025年7月最新数据，KeepAI核心日活跃用户超过15万。其中，AI教练核心功能中的AI饮食记录作为 典型高频刚需场景，以低门槛的精准交互(用户仅需拍摄餐食照片，即可自动识别卡路里与营养素)，展 现出显著留存优势：该功能覆盖了1/3的AI对话用户，并呈现出深度留存特性——次日留存率达50%， 对App DAU的留存率攀升至79%。 8月25日，运动科技公司Keep（03650）公布了截至2025年6月30日的2025上半年业绩报告。报告期内， Keep实现营收8.22亿元（人民币，下同），非国际财报准 ...

Core Insights - The food retail industry is experiencing a shift towards digitalization, driven by the inefficiencies and high losses of traditional retail formats, leading to a focus on specialized food categories and accelerating the chain process in food retail [1][6][9] - The overall digitalization level in the food retail sector is low, and the increase in chain rates will promote digital transformation, focusing on efficiency upgrades and experience reconstruction [1][9] - The digital reconstruction of the food retail industry is based on the concept of "people-goods-scene," with the cash register system serving as a key data touchpoint, alongside supply chain management and omnichannel operation systems [1][12] Digitalization Demand Background - The food retail industry has a low level of digitalization, primarily characterized by decentralized community stores and family-run shops, but is entering an accelerated phase of digital transformation due to the rise of new business formats [9] - Digitalization can integrate the supply chain, optimize procurement costs, and enhance management efficiency while reducing inventory waste [9] - The transformation will focus on improving supply chain management efficiency and reconstructing consumer experience through omnichannel operations [9] Evolution of Food Retail Formats - The transition from traditional supermarkets to specialized new formats is accelerating, with the emergence of brand snack chains, community fresh supermarkets, and other vertical formats [6] - Focusing on specific categories allows startups to quickly establish brand recognition and reduce SKU complexity, leading to lower operational costs [6] - New formats optimize supply chain efficiency by reducing intermediaries and adopting direct sourcing methods [6] Digitalization Framework - The core of food retail digitalization lies in reconstructing the collaborative relationship between people, goods, and scenes [12] - The digitalization of "people" focuses on consumer-centric omnichannel systems, while "goods" emphasizes transparent and controllable supply chain management [12] - The cash register system acts as a critical data hub, forming a "iron triangle" with supply chain management and omnichannel operation systems [12] Cash Register System Insights - The cash register system enhances operational efficiency through integrated payment, inventory management, and dynamic promotions, serving as a data hub for the food retail industry [19] - Different food categories require tailored cash register systems to meet their unique sales and promotional needs [19] - The competitive landscape shows that LeMon holds a leading market share of 38.9%, with a CR3 of 82.0% in the food retail cash register system market [21] Supply Chain Management System Insights - The supply chain management (SCM) system connects production and sales, maintaining supplier relationships and managing logistics [26] - It enhances efficiency through demand forecasting, cost control via supplier collaboration, and risk mitigation through real-time tracking [26] - The competitive landscape includes traditional ERP, comprehensive supply chain, and retail digitalization vendors, each with distinct strategies [29] Omnichannel Operation System Insights - The omnichannel operation system integrates online and offline data flows, creating a unified customer experience and enhancing marketing strategies [33] - It focuses on data accumulation, customer engagement, and operational analysis to drive business decisions [33] - The competitive landscape includes traditional ERP, marketing cloud vendors, and retail digitalization firms, all aiming to optimize their offerings [35] Future Market Outlook - The food retail market is substantial, with the GMV expected to exceed 7 trillion yuan in 2024 and grow to 8.7 trillion yuan by 2029 [38] - Growth drivers include the expansion of lower-tier markets and the rise of instant retail models, emphasizing the importance of digitalization as a competitive factor [38] - Companies that can leverage digitalization will have significant growth opportunities in the evolving market landscape [38] Digitalization Trends - The food retail digitalization vendors are building a technology ecosystem based on cloud-native architecture, data-driven approaches, and intelligent applications [45] - The integration of AI technologies into supply chain management and user operations is expected to enhance decision-making and operational efficiency [45]

Core Insights - The article discusses the challenges faced by membership-based retail, particularly focusing on the human resource aspects that are often overlooked in the context of rapid expansion and competition in the market [2][10]. Group 1: Membership Retail Dynamics - Membership retail requires a customer-centric and data-driven approach, contrasting with traditional retail's focus on traffic and sales [3][8]. - The need for continuous engagement and "freshness" for members is crucial, necessitating strong user insight and operational design capabilities among staff [3][5]. - Supply chain management in membership retail emphasizes "selection and high cost-performance," requiring precise alignment with member needs and robust control over the supply chain [5][10]. Group 2: Talent Acquisition and Retention Challenges - Rapid expansion in membership retail leads to significant talent acquisition challenges, with a competitive landscape making it difficult to find qualified personnel [10][13]. - There is a mismatch between the skills required for new roles in membership retail and the traditional standards used for evaluation, complicating recruitment efforts [10][13]. - Retaining talent is particularly difficult in key positions, with high turnover rates observed in procurement, operations, and member services [13][18]. Group 3: Training and Development Systems - Many companies face a "heavy construction, light operation" issue in talent development, often neglecting ongoing training after initial setup [15][16]. - A continuous training system is essential, covering the entire employee lifecycle and integrating learning into daily work [15][16]. - Feedback mechanisms should be established to ensure that insights from frontline employees are utilized for operational improvements [15][16]. Group 4: Learning from Successful Models - Successful companies like Hai Di Lao and Pang Dong Lai combine culture, training, and incentive mechanisms to enhance employee engagement and service quality [18][22]. - The focus should be on creating a work environment where employees feel valued and recognized, which in turn enhances customer service [18][22]. Group 5: Future Talent Structure and AI Integration - The membership retail industry must evolve its talent structure to include hybrid roles that combine business acumen with digital skills [26][28]. - Companies need to foster a data-driven culture to leverage AI for better decision-making in product selection and marketing strategies [30][31]. - Integrating technology and business operations is crucial for maximizing the value of talent and ensuring sustainable growth [32][33].

Summary of Key Points from the Conference Call Industry Overview - The autonomous driving industry is at a pivotal point transitioning from L2 to L3 commercialization, with full-stack self-research manufacturers and third-party suppliers gaining a competitive edge [1][4] - Major players in the autonomous driving sector include Tesla, Xpeng, Li Auto, NIO, and third-party suppliers like Momenta and Yunrong Qixing [1][5] Core Insights and Arguments - The development of cloud-based intelligent computing centers and mass production of high-performance chips are crucial drivers for the industry [1] - Companies are investing heavily in R&D, with Tesla's HW5.0 featuring 4D millimeter-wave radar and Li Auto's L series equipped with laser radar [6][10] - Regulatory policies significantly impact the industry, with L2 standardization and multiple regions opening L4 commercialization pilot projects [8] Technological Developments - Xpeng is shifting to a pure vision solution to enhance visual perception and reduce hardware costs, while Huawei's ADS 4.0 supports high-speed L3 commercialization [3][12] - The VLA model integrates visual, language, and behavioral modules to optimize vehicle decision-making [3] - The industry is witnessing a shift towards data-driven development, with companies showcasing their cloud-based world models and parameter scales [29] Competitive Landscape - Leading companies in autonomous driving include Tesla, Xpeng, Li Auto, NIO, and Xiaomi, with significant contributions from domestic suppliers like SUTENG, Hesai Technology, and others [5][26] - Traditional manufacturers are increasingly opting for third-party solutions to shorten product cycles and reduce time costs [17] R&D and Investment Trends - Companies like NIO have invested over 10 billion yuan in R&D for three consecutive years, but face challenges in achieving commercial breakthroughs [14] - Xiaomi's growth in the autonomous driving sector is driven by its potential rather than current capabilities, with expectations for its models to feature laser radar [16] Consumer Perception and Market Trends - The development of intelligent driving technology includes advancements in features like high-speed NOA and parking functionalities [32] - Safety features are evolving, with the introduction of proactive avoidance systems to enhance driving experience [33] Investment Opportunities - Investors should focus on leading autonomous driving solution providers and full-stack self-research manufacturers, especially as regulatory frameworks evolve [36]

Group 1: Industry Transformation - The global automotive industry is undergoing profound changes driven by the "new four modernizations," with a focus on the transition from electrification to intelligence and from local market dominance to global value chain restructuring [1] - The period from now until 2030 is critical for cultivating intelligent driving culture and popularizing lower-level intelligent driving technologies, necessitating clear development goals and strategies from major companies [1][2] Group 2: Safety and Technology Challenges - The penetration rate of L2-level intelligent vehicles in China has surpassed 50%, leading the world, but recent serious traffic accidents related to intelligent driving have raised safety concerns [2][3] - Current intelligent vehicle safety technologies are evolving along two main paths: "rule-driven" and "data-driven," each with its own advantages and limitations [3][4] Group 3: Cognitive-Driven Approach - A "cognitive-driven" approach is proposed to combine the advantages of both "rule-driven" and "data-driven" systems, enhancing adaptability and transparency in decision-making processes [4][5] - The stability of automotive safety heavily relies on the performance of automotive-grade chips, which must meet stringent reliability standards [5][6] Group 4: Competitive Landscape - The cost structure of vehicles is shifting, with electronic hardware and AI becoming increasingly significant, projected to rise from less than 25% to 70% by 2030 [7][8] - Companies are encouraged to break traditional industry boundaries and collaborate with technology firms to enhance their competitive edge in the intelligent and AI-driven automotive landscape [8][9]

真格基金合伙人刘元分享了自己对于AI时代下创新企业的一些洞察：首先，中国公司出海甚至创业一 开始就在做全球化的产品，这正在成为一种趋势；其次，AI创业者的年龄前所未有的年轻，传统的工 作经验不再重要甚至成为负担；最后，市场破除对于模型技术壁垒的迷信，开始重新回归对于应用场景 的探索。 此外，论坛上，高金宣布，即日起高金"科技强国人才培养专项计划"正式启动，并同步面向新兴战略产 业、具备鲜明科创属性的广大企业实际控制人、联合创始人或主要股东开放招生。据悉，该计划针对优 秀的科创人才，并以"产学研"协同的培养模式，全方位赋能科创企业成长，帮助科创企业更加快速地发 展。 中证报中证网讯(记者黄一灵)8月1日，"AI(人工智能)时代下的未来范式"主题论坛在沪举办，本次论坛 由上海交通大学上海高级金融学院(高金)和上海交通大学人工智能学院联合主办。 上海交通大学人工智能学院执行院长、上海交大工业创新研究院理事长王延峰表示："目前我们正处在 以数据驱动为核心、以大模型为代表的新一轮发展浪潮，未来中国AI发展的关键在于突破先进算力和 底层技术的瓶颈，同时积极探索出一条适合中国国情的AI发展路径。" 恒生电子（600570） ...

Core Insights - The implementation of ChatBI has significantly improved data analysis efficiency in retail and consumer industries, allowing for quick answers to business questions through simple inquiries [1][2][3] - The success of ChatBI depends on the readiness of the enterprise, including data maturity assessment and organizational support [4][5] Data Analysis Maturity Assessment - Enterprises should evaluate their data maturity before implementing ChatBI, focusing on data integration, key performance indicators, and data quality [4][5] - A scoring model is suggested for enterprises to determine their readiness, with scores above 80 indicating readiness to proceed, while lower scores suggest the need for further preparation [5] Implementation Strategy - A phased approach is recommended for ChatBI implementation, starting with pilot projects in specific departments before broader rollout [6][9] - The importance of assembling a dedicated team with key roles such as project manager, data engineer, and business analyst is emphasized for successful implementation [8] Overcoming Challenges - Common challenges during implementation include data quality issues, user acceptance, and security concerns, which can be addressed through strategies like building a data platform and focusing on core user needs [10][12] - The need for organizational change is highlighted, as successful adoption of ChatBI requires a shift in how data is perceived and utilized within the company [12][13] Conclusion - ChatBI represents a shift towards a data-driven culture in organizations, emphasizing the importance of user engagement and the practical application of data insights [13]