When ChatGPT set off a global wave of enthusiasm, enterprises rushed into the boom of generative AI (GenAI) applications. Yet a harsh truth stands in front of them: more than 80% of AI projects stall after the proof of concept (POC) stage and fail to generate real business value in production.
Where Is the Problem?
Many enterprises attribute the problem to models that are not powerful enough or scenarios that are not clear enough. But the real core bottleneck is hidden at the lowest layer: data. More precisely, it is severe data fragmentation.
Our Insight: In the GenAI Era, Data Fragmentation Has Mutated and Escalated
Traditional data fragmentation usually means data scattered across different databases or business systems. In the GenAI era, however, the meaning of this problem has fundamentally changed. Today's AI Agents need far more than a single type of structured data. To complete a complex task, an Agent may need to understand all of the following at the same time:
- PDF contract clauses in object storage
- Customer transaction records in a database
- Excel financial reports on a cloud drive
- Even meeting recordings from instant messaging tools
These data are not only physically scattered across different systems. More importantly, multimodal data such as contract clauses and transaction records are logically isolated from one another. Important semantic relationships are hidden between them, but traditional IT architectures cannot connect them effectively. An AI model becomes like a detective trying to solve a case with only scattered clues, unable to form a complete chain of understanding. As a result, it cannot cross the gap from "demo" to "production."
A Systemic Challenge That a Single Tool Cannot Solve
Faced with this challenge, many enterprises instinctively respond by "adding another tool." However, whether it is a new ETL tool, vector database, or data middle platform, each addition is only another patch on an already complex architecture. It creates a vicious cycle in which complexity compounds. Point solutions are destined to fail. The only viable path is a unified platform-based solution.
Download the Full Whitepaper for a Systematic Solution
To deeply analyze and address this core pain point, MatrixOrigin has jointly released the whitepaper Data Intelligence Foundation for GenAI with InfoQ, an authoritative technology media platform.
In this whitepaper, you will get:
- An in-depth analysis of "data fragmentation": a comprehensive understanding of its concrete manifestations, root causes, and response strategies in the GenAI era.
- A complete implementation roadmap: a detailed step-by-step guide from data ingestion, transformation, and construction to activation.
- A detailed technical architecture analysis: an in-depth look at how a hyper-converged engine can solve data fragmentation at the source.
Scan the QR code below to download the full report for free and build a real competitive advantage for your enterprise in the AI era.
About InfoQ
InfoQ, a global technology media platform under Geekbang Technology, has been a leading force in the dissemination of knowledge and innovation in software development and related fields since entering China in 2007.
About MatrixOrigin
MatrixOrigin is a leading technology and service provider for Data & AI platforms. Its core team comes from well-known technology companies in China and abroad, with broad industry and international perspectives. MatrixOrigin's core product, MatrixOne Intelligence, is an enterprise-oriented AI-native multimodal data intelligence platform. It uses artificial intelligence technologies, including large models, and an innovative hyper-converged data foundation to help enterprises unify the management and governance of multimodal data, transforming private-domain data into AI-Ready data assets.