Core Viewpoint - Traditional SaaS applications like CRM and ERP systems will not be replaced but will evolve to serve as the infrastructure for AI Agents, which will enhance the importance of data definition and interpretation within enterprises [2][10][15] Group 1: The Role of AI Agents - AI Agents will not eliminate traditional software systems; instead, they will necessitate a clearer separation between how tasks are performed and the sources of facts [2][10] - The effectiveness of AI Agents is contingent upon their ability to access and understand the correct data from various systems, highlighting the need for accurate and structured input data [2][9] - The emergence of AI Agents creates significant entrepreneurial opportunities for companies that can help businesses manage and structure their unstructured data [3][10] Group 2: Data Management Challenges - A significant portion of enterprise knowledge (80%) exists in unstructured data, which is becoming increasingly difficult to manage [2] - The complexity of data definitions within organizations leads to discrepancies in key metrics like Annual Recurring Revenue (ARR), complicating the role of AI Agents in providing accurate information [7][11] - The traditional approach of consolidating data into warehouses has only partially succeeded, as operational teams still rely on individual systems for real-time transactions [8][10] Group 3: Evolution of Systems - CRM and ERP systems will transition from user-centric interfaces to machine-oriented APIs, allowing AI Agents to interact with these systems programmatically [12][15] - The core value of enterprise systems lies in their ability to encapsulate chaotic data, which will remain essential despite changes in interface and interaction methods [13][15] - The demand for a clear, authoritative source of truth will only increase as AI Agents become more prevalent in business processes [14][15] Group 4: Future of Data Infrastructure - The combination of data warehouses, semantic layers, and governance tools will form the foundation for AI Agent workflows, evolving beyond traditional reporting systems [10][12] - The valuation of AI platforms will increasingly depend on their ability to define and manage facts, rather than just their user interfaces [14][15] - Companies that can create exceptional AI Agent experiences based on reliable data sources will have a competitive advantage in the evolving landscape [15]

模型的回归 有些想法是永恒的。 "数据模型"的概念——一种描述信息连接方式的结构化方式 。 已经存在了几十年。但长期以来，建模 一直默默地处于幕后。大多数团队专注于管道、分析或仪表板。 然而，随着组织越来越依赖数据，一些有趣的事情发生了： 该模型又回来了。 只是这一次，它并不存在于桌面上或孤立的文件中。 它存在于云端。它是共享的、协作的，并且与数据堆栈的每个部分深度连接——从 Snowflake 和 dbt 到 治理系统和 AI 辅助决策 。 这就是我们谈论 现代数据建模时的意思。 这不仅仅关乎表格和键。它关乎上下文、协作和信任——能够以一种每个人（从工程师到高管）都能理 解和依托的方式描述数据。 动态建模 过去，模型只是快照——漂亮的图表很快就会过时。 如今，它们已经成为了生命系统。 现代建模平台，例如 SqlDBM 、dbt 以及其他云原生领域的平台，都将模型视为共享工作区。团队可以 通过浏览器设计结构、注释含义、执行标准，并直接连接到生产数据库或版本控制系统。 你可以将其视为数据架构领域的"Google Docs 时刻"：人们实时协作，发表评论，合并更改，并立即看 到效果。这种从静态文档到实时协作的转变 ...