Data-to-Dashboard: Multi-Agent LLM Framework for Insightful Visualization in Enterprise Analytics (2505.23695v1)

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

• The paper introduces a novel multi-agent LLM framework that automates the data-to-dashboard pipeline by simulating human analytical reasoning.
• Evaluation shows the framework significantly outperforms single-prompt LLM baselines and traditional analyst-generated visualizations in generating insightful data visualizations.
• This system has practical implications for enterprise analytics by automating routine tasks and enabling more context-aware and relevant dashboard creation.

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

The paper "Data-to-Dashboard: Multi-Agent LLM Framework for Insightful Visualization in Enterprise Analytics" introduces an agentic system aimed at automating the entire data-to-dashboard pipeline using modular LLM agents. Unlike existing systems that rely on predefined question templates or closed ontologies, this framework simulates the analytical reasoning processes of business analysts by leveraging domain-relevant knowledge across diverse datasets. The authors, Ran Zhang and Mohannad Elhamod, present a modular pipeline that involves domain detection, concept extraction, multi-perspective analysis generation, and iterative self-reflection, aimed at providing more insightful, domain-relevant, and analytically deep outputs compared to existing approaches.

Key Contributions

The paper highlights several contributions:

1. Novel Multi-Agent System: The authors propose a modular agent-based framework that transitions raw business data into a dashboard environment through context-aware reasoning, showcasing significant potential for applications in business analytics.
2. Domain Detection and Concept Extraction: The system incorporates mechanisms for detecting domains and retrieving knowledge to facilitate precise feature selection and multifaceted analytical reasoning, thereby enhancing accuracy and robustness.
3. Simulation of Analytical Reasoning: The workflow emulates the cognitive processes of business analysts, emphasizing iterative improvements and reflective interpretation, which are crucial for yielding higher-quality insights.

These contributions are thoroughly evaluated using tailored G-Eval metrics and qualitative assessments by domain experts, affirming the framework's potential to supersede traditional methodologies for enterprise data analysis.

Numerical Results and Evaluation

The framework shows substantial improvements over the GPT-4o single-prompt baseline, particularly in terms of insightfulness, novelty, and analytical depth. By implementing a two-stage process (data-to-insight and insight-to-chart), the system effectively transforms raw datasets into insightful visualizations. The authors report significant lifts in refined metrics such as G-Eval, yielding deep and novel insights. Furthermore, the comparison against a well-referenced Kaggle dataset demonstrates the system's capability to outperform popular analyst-generated visualizations, validating its effectiveness in practical settings.

Theoretical and Practical Implications

The paper posits several implications for both theory and practice in AI-driven enterprise analytics:

• Theoretical Advancements: By circumventing traditional question-answer paradigms through an agentic, reflective architecture, the system highlights the potential of LLMs in aligning closely with human-like analytical workflows. The integration of domain-knowledge marks a pivotal shift towards generalized, yet contextually aware analytics systems.
• Practical Applications: This framework can significantly enhance productivity within enterprise environments by automating routine analytical tasks while empowering domain experts to focus on human-in-the-loop validation and decision-making. The resulting dashboards can adopt flexible, context-aware narratives that are highly relevant for strategic operations without manual intervention.

Future Developments

The proposed framework paves the way for enhanced AI-driven visual analytics tools. Future research could focus on refining memory-based reflection mechanisms, potentially adopting more sophisticated self-consistency methods to refine domain inference accuracy. Additionally, expanding the contextual repository of domain-knowledge could further bolster the framework's applicability across a broader range of industrial domains, from marketing and finance to operations and beyond. Integrating these components could yield a more robust and dynamic analytical toolset that warrants attention from both academia and industry specialists.