引言 在生成式 AI 时代，全球数据总量正以惊人速度增长，据 IDC 预测，2025 年将突破 180ZB，其中 80% 为非结构化内容，传统数据分析在应对多模态信息和 打破结构化数据技术壁垒方面尽显乏力，"人工找数 + 手动分析" 的模式严重抑制甚至沉没了数据价值。与此同时，数据分析的传统体系从 ETL 到前端报 表，虽解决了 "有报表看" 的问题，却在即时用数、用数闭环等场景中暴露出数据链路长、门槛高、决策行动滞后、实时性不足等结构性痛点。 在这样的背景下， TabTab.ai 选择从一个被巨头忽视、却蕴藏着巨大爆发潜力的赛道切入 —— 为用户提供全链路 Data Agent。 不同于单一工具只关注公 域数据的获取，TabTab.ai 围绕数据全链路（获取、准备、建模、洞察、可视化）构建了全栈自动化的 Multi-Agent 系统，能够按照自然语言意图自主规 划、执行与校验，输出可维护、可追溯的结果。TabTab.ai 带来的价值在于 重构人机交互模式，通过自然语言对话，把"结构化读写 + 自动推理"变成分钟级 的闭环，使得数据能主动响应"数找人" 需求，彻底绕过分层建设的传统数据分析链路，迎来 Vibe ...

Core Viewpoint - The core objective of China's data infrastructure is to address issues related to data supply, circulation, and utilization while ensuring data security, aiming for a system where data can be effectively supplied, circulated, utilized, and secured [3][6]. Group 1: Data Infrastructure Goals - The primary goal is to resolve the existing problems of data being "unable to circulate, slow to flow, and poorly utilized" [3]. - China's data infrastructure is defined as a new type of infrastructure that provides services for data collection, aggregation, transmission, processing, circulation, utilization, operation, and security [3]. Group 2: Effectiveness Indicators - The effectiveness of data infrastructure can be measured by the volume of data in circulation; significant platforms like Didi, Meituan, and Ctrip demonstrate effective data infrastructure with billions of users [4]. - The second indicator is the security of the data circulation process, which is crucial for ensuring efficient and trustworthy data flow [5]. Group 3: Key Technologies - Six key technology routes have been identified to ensure both data circulation and security: blockchain technology, privacy computing technology, data networking technology, data components, trusted data space technology, and data sandbox technology [5]. - Current technologies like blockchain and privacy computing are not yet mature enough for widespread application due to efficiency issues, particularly in sectors like finance where they are currently utilized [5]. Group 4: Future Directions - The future of national data infrastructure is expected to converge into a singular "space," "platform," or "network" where data can flow efficiently and securely [10]. - The construction of this space will involve various technologies, but the essential requirement is the presence of numerous data supply entities, application scenarios, and service providers [10]. Group 5: Addressing Data Inequality - The need to bridge the "data gap" across different industries is emphasized, with a focus on ensuring that all sectors, including manufacturing and agriculture, can leverage data for digital transformation [12]. - The national data infrastructure aims to solve the "data equality" issue, enabling artificial intelligence and other technologies to thrive by providing high-quality data [14].