Walk Before You Run: The Importance of Data Exploration for Data Analysis Agents

2026-08-16

AI Summary

This study addresses the limitation of existing data analysis agents that overlook exploratory phases, resulting in inadequate comprehension of complex tabular data. We propose establishing data exploration as a primary evaluation objective for LLM-based analysis. By constructing a multi-table workbook benchmark and extending DSBench, we validate this approach through structured artifact evaluation. Experimental results demonstrate that explicit data exploration compensates for logical structural deficiencies in strong models, significantly improving downstream task accuracy and human-AI collaboration efficiency. These findings confirm the critical value of data exploration as an independent, inspectable stage and a verification node in human-machine workflows, thereby offering a novel paradigm for reliable data analysis.

Abstract

LLM-based data-analysis tools are increasingly used to help users analyze messy spreadsheets and workbooks, from answering questions over uploaded files to generating code, summaries, and visualizations. These systems are often evaluated by the correctness of their final downstream answers. However, reliable data analysis also depends on an earlier step: understanding what the dataset contains before solving the requested task. For complex workbooks, this Data Exploration step includes identifying the logical tables behind physical sheets, interpreting column semantics, recovering keys and relationships, and detecting quality issues. In current tools and benchmarks, this step is usually left implicit, creating a gap between downstream task performance and the dataset understanding needed for reliable, human-checkable analysis. Our key contribution is to identify this overlooked gap, make Data Exploration a first-class evaluation target, and show through downstream experiments that stronger Data Exploration support improves task performance. To evaluate dataset understanding directly, we introduce two benchmark settings: a real multi-sheet workbook benchmark based on a Vitamin D study dataset, and an extension of DSBench with schema-fixed Data Exploration artifacts. In both settings, systems are evaluated by the quality of a structured artifact capturing tables, columns, semantic roles, relationships, and profiling signals. Our results show that strong LLMs and data-analysis agents still miss important logical structure even when they read spreadsheet content. Furthermore, explicit Data Exploration support often improves downstream correctness, suggesting it should be treated as a first-class, inspectable stage in LLM data-analysis workflows and a natural human-in-the-loop checkpoint where domain experts can review and correct the artifact before downstream analysis proceeds.

Problem

• Data Exploration
• Data Analysis Agents
• Dataset Understanding
• LLM Evaluation
• Spreadsheet Analysis

Innovation

• Data Exploration
• Data Analysis Agents
• Benchmark
• Structured Artifact
• Human-in-the-loop