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

• URL: http://arxiv.org/abs/2505.23695v1
• Date: Thu, 29 May 2025 17:32:15 GMT
• Title: Data-to-Dashboard: Multi-Agent LLM Framework for Insightful Visualization in Enterprise Analytics
• Authors: Ran Zhang, Mohannad Elhamod,
• Abstract summary: We present an agentic system that automates the data-to-dashboard pipeline through modular LLM agents.<n>Unlike existing chart systems, our framework simulates the analytical reasoning process of business analysts.<n>Our approach shows improved insightfulness, domain relevance, and analytical depth, as measured by tailored evaluation metrics.
• Score: 2.7933239275667545
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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

Related papers

• Deep Research Agents: A Systematic Examination And Roadmap [79.04813794804377]
  Deep Research (DR) agents are designed to tackle complex, multi-turn informational research tasks.<n>In this paper, we conduct a detailed analysis of the foundational technologies and architectural components that constitute DR agents.
  arXiv  Detail & Related papers  (2025-06-22T16:52:48Z)
• IDA-Bench: Evaluating LLMs on Interactive Guided Data Analysis [60.32962597618861]
  IDA-Bench is a novel benchmark evaluating large language models in multi-round interactive scenarios.<n>Agent performance is judged by comparing its final numerical output to the human-derived baseline.<n>Even state-of-the-art coding agents (like Claude-3.7-thinking) succeed on 50% of the tasks, highlighting limitations not evident in single-turn tests.
  arXiv  Detail & Related papers  (2025-05-23T09:37:52Z)
• TAIJI: MCP-based Multi-Modal Data Analytics on Data Lakes [25.05627023905607]
  We envision a new multi-modal data analytics system based on the Model Context Protocol (MCP)<n>First, we define a semantic operator hierarchy tailored for querying multi-modal data in data lakes.<n>Next, we introduce an MCP-based execution framework, in which each MCP server hosts specialized foundation models optimized for specific data modalities.
  arXiv  Detail & Related papers  (2025-05-16T14:03:30Z)
• AIRepr: An Analyst-Inspector Framework for Evaluating Reproducibility of LLMs in Data Science [5.064778712920176]
  Large language models (LLMs) are increasingly used to automate data analysis through executable code generation.<n>We present $itAIRepr, an $itA$nalyst - $itI$nspector framework for automatically evaluating and improving the $itRepr$oducibility of LLM-generated data analysis.
  arXiv  Detail & Related papers  (2025-02-23T01:15:50Z)
• Data Analysis in the Era of Generative AI [56.44807642944589]
  This paper explores the potential of AI-powered tools to reshape data analysis, focusing on design considerations and challenges. We explore how the emergence of large language and multimodal models offers new opportunities to enhance various stages of data analysis workflow. We then examine human-centered design principles that facilitate intuitive interactions, build user trust, and streamline the AI-assisted analysis workflow across multiple apps.
  arXiv  Detail & Related papers  (2024-09-27T06:31:03Z)
• A Survey on Multimodal Benchmarks: In the Era of Large AI Models [13.299775710527962]
  Multimodal Large Language Models (MLLMs) have brought substantial advancements in artificial intelligence. This survey systematically reviews 211 benchmarks that assess MLLMs across four core domains: understanding, reasoning, generation, and application.
  arXiv  Detail & Related papers  (2024-09-21T15:22:26Z)
• PUB: Plot Understanding Benchmark and Dataset for Evaluating Large Language Models on Synthetic Visual Data Interpretation [2.1184929769291294]
  This paper presents a novel synthetic dataset designed to evaluate the proficiency of large language models in interpreting data visualizations. Our dataset is generated using controlled parameters to ensure comprehensive coverage of potential real-world scenarios. We employ multimodal text prompts with questions related to visual data in images to benchmark several state-of-the-art models.
  arXiv  Detail & Related papers  (2024-09-04T11:19:17Z)
• InsightBench: Evaluating Business Analytics Agents Through Multi-Step Insight Generation [79.09622602860703]
  We introduce InsightBench, a benchmark dataset with three key features.<n>It consists of 100 datasets representing diverse business use cases such as finance and incident management.<n>Unlike existing benchmarks focusing on answering single queries, InsightBench evaluates agents based on their ability to perform end-to-end data analytics.
  arXiv  Detail & Related papers  (2024-07-08T22:06:09Z)
• MatPlotAgent: Method and Evaluation for LLM-Based Agentic Scientific Data Visualization [86.61052121715689]
  MatPlotAgent is a model-agnostic framework designed to automate scientific data visualization tasks. MatPlotBench is a high-quality benchmark consisting of 100 human-verified test cases.
  arXiv  Detail & Related papers  (2024-02-18T04:28:28Z)
• Distributed intelligence on the Edge-to-Cloud Continuum: A systematic literature review [62.997667081978825]
  This review aims at providing a comprehensive vision of the main state-of-the-art libraries and frameworks for machine learning and data analytics available today. The main simulation, emulation, deployment systems, and testbeds for experimental research on the Edge-to-Cloud Continuum available today are also surveyed.
  arXiv  Detail & Related papers  (2022-04-29T08:06:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.