每经记者：彭斐每经编辑：张益铭 在AI（人工智能）狂飙突进、数据要素市场火热的当下，曾被视为"数据湖"龙头的易华录 （SZ300212，股价15.50元，市值111.58亿元）却陷入了财务危机。 1月30日晚间，易华录发布的2025年度业绩预亏公告显示，预计2025年度归母净利润亏损额高达21.76亿 元至27.91亿元，公司净资产也将转为负值，面临被实施退市风险警示（*ST）的处境。 《每日经济新闻》记者注意到，尽管易华录在智慧交通等领域的新签合同实现逆势增长，但巨额的资产 及信用减值计提成为吞噬利润的"黑洞"。 为了"止血自救"，易华录自2025年四季度以来动作频频：加速处置新加坡子公司及山东聊云信息技术有 限责任公司（以下简称聊云公司）等参股公司股权，并毅然终止了超级存储及人工智能训练资源库两大 募投项目，拟将3.55亿元剩余募资永久补流。 然而，在市场环境压力与技术迭代的双重挤压下，易华录能否通过战略收缩换取生存空间仍待市场检 验。 减值"黑洞"吞噬利润 根据易华录发布的业绩预告，2025年全年，公司预计实现营业收入3.98亿元至5.39亿元，与上年同期的 4.65亿元相比，呈现出一定的波动区间。尽管 ...

Core Viewpoint - Jinan aims to become a "national first-class smart pioneer city" by 2026, focusing on five key areas: infrastructure, data elements, industrial upgrades, public services, and smart government [1] Group 1: Digital Infrastructure Development - By 2025, Jinan will establish a unified big data platform, creating a "data lake" that aggregates 47.4 billion data entries and supports 544.3 million data sharing instances [1] - In 2025, the city's computing power reached 6451P, with intelligent computing accounting for 83.2% [1] - Jinan will enhance its infrastructure by promoting collaborative computing power development, upgrading intelligent network infrastructure, and accelerating the construction of intelligent perception systems [1] Group 2: Data Annotation Industry - Jinan plans to develop the data annotation industry by establishing an industrial park and attracting 60 data annotation companies [2] - The focus will be on specialized data annotation in sectors like artificial intelligence and healthcare to improve market competitiveness [2] - A supply-demand matching conference will be organized to discuss key issues in high-quality dataset construction and talent development [2] Group 3: Smart Governance and Digital Economy - By 2025, the revenue from core digital economy enterprises in Jinan is expected to exceed 19% of the total [3] - Jinan will enhance smart governance through the establishment of a centralized smart management system, enabling comprehensive perception and efficient response [3] - The city aims to create a unified framework for urban operations, enhancing data integration and application [3] Group 4: No-Proof City Initiative - Jinan is optimizing government service processes to achieve 85% of service items without the need for material submission or form filling [4] - The city is developing applications for quick hotel check-ins and electronic certificates to streamline administrative processes [4] - The initiative includes the integration of various service codes to enhance user experience and efficiency [4] Group 5: AI Industry Development - Jinan is focused on high-quality development of the AI industry, leveraging local computing resources to support AI transformation tools and model resources [6] - The city aims to foster intelligent native enterprises and develop end-to-end solutions for AI applications [6] - Initiatives include enhancing smart healthcare applications and integrating AI in public cultural services [6]

1月20日，济南市人大代表，济南市大数据局党组书记、局长张熙在接受齐鲁晚报·齐鲁壹点专访时表 示，2026 年，济南数智化建设将迈入高质量发展新阶段。将锚定"全国一流数智先锋城市"目标，以"人 工智能 +"和"数据要素 ×"为双轮驱动，以全域数字化转型为主线，聚焦基础设施、数据要素、产业升 级、民生服务、数智政府五大重点领域，全力实现城市核心竞争力与市民生活品质双向提升。 围绕重点领域 积极发展专业化数据标注 数字基础设施是数字济南建设的底座支撑。去年，济南打造了市一体化大数据平台，建成全市通用共 享"数据湖"，累计汇聚数据474亿条，保障数据共享调用54.43亿次，有效支撑了公积金、不动产、行政 审批、义务教育入学等应用场景。 张熙表示，2025年，济南规模以上数字经济核心产业企业营业收入占比预计达19%以上，数字经济成为 高质量发展的关键增量。今年，济南将深化行业算力应用效能，加强济南政务云建设，提升全栈服务和 共性支撑能力，为数字政府提供坚实底座支撑。 今年，济南加强城市智慧治理。依托智慧泉城运行管理中心建强城市智能中枢，实现态势全面感知、趋 势智能研判、协同高效处置；构建统一规划、统一架构、统一标准、 ...

Core Insights - The article discusses the transformation of supply chain management in the pharmaceutical industry, particularly through the collaboration between Liuyao Group and Huawei Cloud, leveraging AI to optimize complex supply chain operations [4][11][25]. Group 1: Industry Challenges - Liuyao Group faces a complex supply chain with over 10,000 SKUs, multiple warehouses, and stringent compliance and time constraints, which creates a systemic challenge in operations [4][6]. - The pharmaceutical industry is experiencing increased pressure due to the normalization of centralized procurement, stricter cold chain traceability, and comprehensive compliance regulations [6][10]. - The inefficiencies in China's logistics, where logistics costs account for approximately 18% of GDP compared to 8% in the U.S., highlight the need for significant improvements in supply chain efficiency [9][10]. Group 2: AI Integration and Transformation - Liuyao Group has partnered with Huawei Cloud to reconstruct its supply chain decision-making system using AI, focusing on data governance, demand forecasting, and intelligent scheduling [4][11][12]. - The integration of AI technologies, such as data lakes and predictive models, allows for real-time visibility and intelligent decision-making within the supply chain [14][19]. - The AI-driven supply chain system enables Liuyao to optimize complex operations, reducing decision-making time and costs while improving efficiency by 15% to 18% [18][19]. Group 3: Future Trends - By 2027, over 50% of large multinational companies are expected to adopt AI and advanced analytics for supply chain management, indicating a global trend towards intelligent supply chains [8][10]. - In China, over 60% of large enterprises are projected to implement AI and intelligent scheduling systems in key supply chain areas within the next three years, reflecting a structural shift in the industry [10][22]. - The shift from experience-driven to intelligent-driven supply chains is becoming a critical variable in determining operational quality, marking a significant turning point for the pharmaceutical distribution industry [25][26].

Core Insights - The implementation of data lakes addresses the limitations of traditional databases in handling the explosive growth of data volume and complexity, providing a unified and scalable infrastructure for storing structured, semi-structured, and unstructured data [2][7] - Data lakes serve as the foundation for modern analytics and artificial intelligence, enabling real-time insights, self-service business intelligence, and predictive modeling [2][6] Group 1: Definition and Importance of Data Lakes - A data lake is a centralized storage system that allows organizations to store all types of data in its raw format until needed for analysis, contrasting with traditional data warehouses that require data to be structured before storage [6][7] - The construction of a data lake is crucial for organizational success, as it provides a flexible, cost-effective, and future-proof solution for data storage and analysis [7][10] - Data lakes enable organizations to combine historical and real-time data, supporting advanced use cases such as predictive analytics and fraud detection [6][10] Group 2: Core Architecture of Data Lakes - Data lakes are organized into multiple layers that work together to transform raw information into valuable business insights, including ingestion, storage, processing, governance, and consumption layers [11][20] - The ingestion layer brings data from various sources into the data lake, preserving its original format for later analysis [12] - The storage layer holds raw data in scalable and cost-effective repositories, supporting all data types [13][14] - The processing layer cleans, validates, and enriches data, organizing it into different zones for business analysis [15] - The governance layer ensures data remains trustworthy, secure, and compliant throughout its lifecycle [16] - The consumption layer provides tools for users to extract value from data, enabling self-service analytics while maintaining governance controls [17] Group 3: Implementation Steps and Best Practices - The first step in implementing a data lake is to clarify objectives and identify key use cases, translating them into key performance indicators (KPIs) [23] - Selecting the appropriate cloud platform is crucial, with options like AWS, Azure, and GCP offering various tools for storage, analysis, and governance [24][26] - Designing a layered architecture helps maintain data organization and trustworthiness, with clear definitions for raw, refined, and business-ready data [27][28][29] - Implementing governance and security measures from the outset is essential, including data ownership, access controls, and compliance tracking [31] - Continuous monitoring, optimization, and documentation of data processes are necessary to ensure the data lake remains scalable and efficient [33][42] Group 4: Real-World Case Studies - Shell Energy built a data lake on Microsoft Azure to integrate IoT, operations, and energy management data, reducing data preparation time by 60% and enhancing collaboration between data scientists and business teams [55] - Comcast utilized a Databricks data lake to integrate customer interaction, billing, and service data, enabling near-real-time analysis and improving customer retention rates [56] - HSBC adopted a cloud-based data lake to upgrade its risk management and compliance framework, enhancing the accuracy and transparency of regulatory reporting [57]

Core Insights - The People's Bank of China (PBOC) is actively implementing long-term rectification measures following the third round of inspections by the Communist Party, focusing on addressing deep-rooted and common issues in the financial sector [1][2] Group 1: Financial Infrastructure Development - The PBOC is accelerating the construction of technology-enabled monitoring and regulatory facilities, including the development of cybersecurity management systems and expanding the monitoring scope of the financial cybersecurity situation awareness platform [1] - There is a strong emphasis on enhancing treasury construction and management, ensuring the safe and stable operation of the treasury system, and advancing the national treasury project [1] - The PBOC is also focusing on strengthening the regulation and interconnectivity of financial market infrastructure, promoting the implementation of interbank and exchange market connectivity projects [1] Group 2: Information Technology Reform - The PBOC is advancing information technology reforms by establishing project management guidelines and actively promoting the integration of information systems across the organization [1] - There is a plan to enhance centralized management of data centers and to increase the usage rate of data lakes and central bank cloud services [1] Group 3: Financial Legislation Progress - The Anti-Money Laundering Law has been officially implemented, and significant progress has been made in revising the PBOC Law [2] - The PBOC is collaborating with the National People's Congress to advance the review of the Financial Stability Law draft and has achieved milestones in the revision of the Commercial Bank Law, Bill Law, and Foreign Exchange Management Regulations [2] Group 4: Future Work Plans - The PBOC plans to integrate inspection rectification into the implementation of the Central Committee's decisions, maintaining a stable and progressive work approach to foster a favorable monetary and financial environment for economic recovery [2] - There is a commitment to further solidify responsibilities for mitigating financial risks and to continue building a self-controlled, safe, and efficient financial infrastructure system [2]

Core Insights - 85% of big data projects fail, and despite a 20% growth in the $15.2 billion data lake market in 2023, most companies struggle to extract value from text data [2][25] - The reliance on general-purpose large language models (LLMs) like ChatGPT is costly and ineffective for structured data needs, with operational costs reaching $700,000 daily for ChatGPT [2][25] - Companies are investing heavily in similar LLMs without addressing specific industry needs, leading to inefficiencies and wasted resources [8][10] Data and Cost Analysis - ChatGPT incurs monthly operational costs of $3,000 to $15,000 for medium applications, with API costs for organizations processing over 100,000 queries reaching $3,000 to $7,000 [2][25] - 95% of the knowledge in ChatGPT is irrelevant to specific business contexts, leading to significant waste [4][25] - 87% of data science projects never reach production, highlighting the unreliability of current AI solutions [7][25] Industry-Specific Language Models - Business Language Models (BLMs) focus on industry-specific vocabulary and general business language, providing targeted solutions rather than generic models [12][25] - BLMs can effectively convert unstructured text into structured, queryable data, addressing the challenge of the 3.28 billion TB of data generated daily, of which 80-90% is unstructured [21][25] - Pre-built BLMs cover approximately 90% of business types, requiring minimal customization, often less than 1% of total vocabulary [24][25] Implementation Strategy - Companies should assess their current text analysis methods, as 54% struggle with data migration and 85% of big data projects fail [27][25] - Identifying industry-specific vocabulary needs is crucial, given that only 18% of companies utilize unstructured data effectively [27][25] - Organizations are encouraged to evaluate pre-built BLM options and leverage existing analytical tools to maximize current infrastructure investments [27][28]