# Data-to-Dashboard: Multi-Agent LLM Framework for Insightful Visualization in Enterprise Analytics

arXiv: https://arxiv.org/abs/2505.23695 · HTML: https://mlsanity.com/paper/2505.23695/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use.
- Builds on 13 of its 34 references that are in the map.
- First posted on arXiv 29 May 2025.

## Placed in

- LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/)

## Builds on (most cited first, 13 of 13)

- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023)
- [G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment](https://mlsanity.com/paper/2303.16634/) (2023)
- [ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning](https://mlsanity.com/paper/2203.10244/) (2022)
- [Inference-Time Scaling for Generalist Reward Modeling](https://mlsanity.com/paper/2504.02495/) (2025)
- [ChartBench: A Benchmark for Complex Visual Reasoning in Charts](https://mlsanity.com/paper/2312.15915/) (2023)
- [ChartInsights: Evaluating Multimodal Large Language Models for Low-Level Chart Question Answering](https://mlsanity.com/paper/2405.07001/) (2024)
- [InsightBench: Evaluating Business Analytics Agents Through Multi-Step Insight Generation](https://mlsanity.com/paper/2407.06423/) (2024)
- [Automated Data Visualization from Natural Language via Large Language Models: An Exploratory Study](https://mlsanity.com/paper/2404.17136/) (2024)
- [LLM4Vis: Explainable Visualization Recommendation using ChatGPT](https://mlsanity.com/paper/2310.07652/) (2023)
- [An LLM-Based Approach for Insight Generation in Data Analysis](https://mlsanity.com/paper/2503.11664/) (2025)
- [How Aligned are Human Chart Takeaways and LLM Predictions? A Case Study on Bar Charts with Varying Layouts](https://mlsanity.com/paper/2408.06837/) (2024)
- [ChartInsighter: An Approach for Mitigating Hallucination in Time-series Chart Summary Generation with A Benchmark Dataset](https://mlsanity.com/paper/2501.09349/) (2025)
- [What Exactly is an Insight? A Literature Review](https://mlsanity.com/paper/2307.06551/) (2023)

## Built on by (most cited first, 11 of 11)

- [CoDA: Agentic Systems for Collaborative Data Visualization](https://mlsanity.com/paper/2510.03194/) (2025)
- [AI Organizations are More Effective but Less Aligned than Individual Agents](https://mlsanity.com/paper/2604.10290/) (2026)
- [DataSage: Multi-agent Collaboration for Insight Discovery with External Knowledge Retrieval, Multi-role Debating, and Multi-path Reasoning](https://mlsanity.com/paper/2511.14299/) (2025)
- [HARMON-E: Hierarchical Agentic Reasoning for Multimodal Oncology Notes to Extract Structured Data](https://mlsanity.com/paper/2512.19864/) (2025)
- [NL2Dashboard: A Lightweight and Controllable Framework for Generating Dashboards with LLMs](https://mlsanity.com/paper/2601.06126/) (2026)
- [UPA: Unsupervised Prompt Agent via Tree-Based Search and Selection](https://mlsanity.com/paper/2601.23273/) (2026)
- [Agentic Control Center for Data Product Optimization](https://mlsanity.com/paper/2603.10133/) (2026)
- [Discovery Agents for Real-Time Analytics: Toward Proactive Insight Systems](https://mlsanity.com/paper/2605.27571/) (2026)
- [A Multi-Agent Platform for Automated Enterprise Analytics and Insight Generation](https://mlsanity.com/paper/2608.18740/) (2026)
- [From Question-First to Analyst-First: Domain-Expert Skills and Verified Knowledge Compilation for Proactive Enterprise Analytics](https://mlsanity.com/paper/2608.28594/) (2026)
- [InsightChain: Optimized Chain-of-Insight Analytics for LLM-driven Data Visualization](https://mlsanity.com/paper/2609.06438/) (2026)

## Abstract (arXiv)

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