Expanding Analytics to All Data with GenAI, Graph, and Visual Analytics // Weidong Yang

Existing BI and big data solutions primarily consumes structured data, which accounts for only about 20% of enterprise information, leaving vast amounts of unstructured data underutilized. In this talk, we introduce GraphBI, which aims to address this challenge by combining GenAI, graph technology, and visual analytics to unlock the full potential of enterprise data. Technologies like Retrieval-Augmented Generation (RAG) and GraphRAG enhance summarization and Q&A but often function as black boxes, making verification difficult. In contrast, GraphBI takes a different approach: using GenAI for data pre-processing, transforming unstructured data into a graph-based format. This transparent, step-by-step workflow ensures trustworthiness and transparency of the analytics process. In this talk, we’ll walk through the GraphBI workflow, covering best practices and challenges, including: Architectural considerations for projects of varying scales; Data pre-processing, including knowledge map extraction and entity resolution; And Iterative analytics with a BI-focused graph grammar. This approach uniquely surfaces business insights by effectively incorporating all types of data.