用多个AI代理自动完成企业数据分析到可视化的大屏流程。
Data-to-Dashboard: Multi-Agent LLM Framework for Insightful Visualization in Enterprise Analytics
- 分模块的AI代理协同完成领域识别、概念提取和多角度分析。
- 在三个不同领域数据集上优于单次提示的GPT-4o，洞察更深入。
- 适合需要专家参与验证的企业级数据分析场景。
大型语言模型（LLM）的快速发展催生了多种用于数据分析的智能体系统，利用其能力提升洞察生成与可视化水平。本文提出一个自动化数据到大屏的智能体框架，由多个模块化LLM代理组成，具备领域识别、概念抽取、多视角分析生成及迭代自我反思能力。不同于现有图表问答系统，该框架通过检索领域相关知识并适应多样数据集，无需依赖封闭本体或问题模板，模拟业务分析师的分析推理过程。我们在三个跨领域数据集上评估该系统，相较于GPT-4o单次提示基线，在定制化评估指标和人工定性评价中均展现出更高的洞察力、领域相关性与分析深度。本工作提出了一种新颖的模块化流水线，打通从原始数据到可视化展示的路径，并为领域专家参与人机协作验证开辟新可能。所有代码已开源：https://github.com/77luvC/D2D_Data2Dashboard。
原文摘要 · Abstract (English)
The rapid advancement of LLMs has led to the creation of diverse agentic systems in data analysis, utilizing LLMs' capabilities to improve insight generation and visualization. In this paper, we present an agentic system that automates the data-to-dashboard pipeline through modular LLM agents capable of domain detection, concept extraction, multi-perspective analysis generation, and iterative self-reflection. Unlike existing chart QA systems, our framework simulates the analytical reasoning process of business analysts by retrieving domain-relevant knowledge and adapting to diverse datasets without relying on closed ontologies or question templates. We evaluate our system on three datasets across different domains. Benchmarked against GPT-4o with a single-prompt baseline, our approach shows improved insightfulness, domain relevance, and analytical depth, as measured by tailored evaluation metrics and qualitative human assessment. This work contributes a novel modular pipeline to bridge the path from raw data to visualization, and opens new opportunities for human-in-the-loop validation by domain experts in business analytics. All code can be found here: https://github.com/77luvC/D2D_Data2Dashboard
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