Core Viewpoint - The implementation of Data Agents appears straightforward but is fraught with challenges, primarily due to software engineering difficulties. A unified semantic layer is crucial for success, and neglecting aspects like scenario focus, iterative technical frameworks, or semantic models can lead to stagnation in prototype stages [2][6][12]. Group 1: Importance of Semantic Layer - The significance of building a semantic layer for Data Agents is widely recognized, with both domestic and international investments increasing in this area. Tencent Cloud WeData has been an early investor in this domain [7][12]. - The semantic layer encompasses four main aspects: concepts, data relationships, metrics, and dimensions, which are essential for providing accurate and unified data access interfaces for Agents [8][12]. Group 2: Technical Challenges and Solutions - The primary technical challenges in integrating Data Agents into existing enterprise platforms include data governance issues and the difficulty in evaluating the effectiveness of Data Agents [14][15]. - To address these challenges, a focus on specific scenarios for unified semantic layer construction and evaluation systems is recommended [15][18]. Group 3: Future of Data Roles - Data Agents are not expected to replace data engineers or scientists but will automate some execution tasks. This will lead to a fusion of roles, requiring professionals to possess a broader skill set related to Agents and large language models (LLMs) [10][11]. - Understanding the basic principles of Agents and LLMs is essential for effectively utilizing large model technologies [11]. Group 4: Recommendations for Enterprises - Companies are advised to focus on scenario-specific semantic abstraction and address existing data governance issues to build a robust semantic layer [16][17]. - It is crucial to establish an iterative technical framework and a comprehensive Agent operation system to monitor, evaluate, and modify the Data Agent effectively [18].

企业如何选择合适的数据治理产品 在数字化转型的浪潮下，数据已成为企业的核心资产。然而，许多企业却发现：数据越多，问题反而越多——报表对不上、客户信息混乱、合规风险频发。 这些问题的根源，往往不是技术不够先进，而是缺乏有效的数据治理。 那么，企业到底要不要做数据治理？什么时候该做？又该如何选择一款适合自己的数据治理产品？ 本文将从企业规模、业务需求和行业特点出发，结合真实案例与主流厂商分析，为你提供一份清晰、实用的数据治理产品选型指南。 不是所有企业都需要立刻上马复杂的数据治理系统。我们可以从企业规模和业务复杂度两个维度来判断是否到了该启动治理的阶段。 从企业规模来判断 • 员工50至300人，年营收在5000万元到5亿元之间 • 员工少于50人、年营收低于5000万元的小微企业数据量小 往往已开始使用CRM、ERP、财务系统等多个业务系统，数据不一致、报表冲突等问题逐渐显现。这类中小企业建议启动轻量级的数据治理，比如统一关 键字段定义、明确数据责任人、建立简单的元数据管理机制。 系统简单，通常使用Excel或基础ERP即可满足需求，暂时不需要系统性的数据治理，但可以做一些基础的数据规范。 • 员工300人以上 ...

Core Insights - The article emphasizes the increasing importance of speed in data acquisition, risk assessment, and market response for companies in the context of volatile global commodity trade and supply chain risks [1][3][4] - The shift in competitive barriers in the commodity trading industry is moving from "resources" to "data assets," highlighting the necessity for companies to adopt data-driven strategies to remain competitive [3][4] Company Overview - Zhongji Ningbo Group, a leading private commodity trading company in China, achieved a total revenue of 141.597 billion yuan in 2024, with its main business covering oil products, chemicals, non-ferrous goods, and agricultural products [1] - The company has partnered with Tencent Cloud to build a global real-time data platform, enhancing its competitive edge in the industry [1][7] Digital Transformation - The digital transformation strategy at Zhongji Ningbo Group is led by its president, aiming to create a unified data platform to eliminate data silos and improve operational efficiency [7][9] - The integration of Tencent Cloud's technology has enabled the company to overcome challenges related to data fragmentation and improve real-time data processing capabilities [9][10] Data Management and Efficiency - The implementation of a data middle platform has significantly improved data flow efficiency, allowing for real-time data synchronization across over 30 business systems [9][10] - The new system enables rapid data processing, with transaction calculations being executed in milliseconds, which is crucial for managing risks in high-stakes commodity trading [6][10] Market Performance - In the first half of 2025, Zhongji Ningbo Group's import and export volume reached 3.258 billion USD, marking a 17% year-on-year increase, with exports growing by 23%, outperforming the industry average [11] - The data middle platform has become a key asset for the company, contributing to its competitive advantage in the market [11][13] Industry Implications - The advancements in digital capabilities are not only benefiting Zhongji Ningbo Group but are also being extended to other enterprises, showcasing the potential for digital transformation across the industry [13][14] - The integration of digital and traditional sectors is seen as a pathway for Chinese companies to gain a competitive edge in the global market [14]