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

Yike Yuan, Virum Ranka, Tina Lasisi, Lin Ma

arXiv:2608.16045v1cs.DBcs.AI

TL;DR

Data-analysis agents often leave implicit whether they have faithfully understood messy workbooks before answering downstream questions. This paper makes Data Exploration explicit and evaluates it directly, finding that current systems miss logical structure and that stronger support often improves downstream correctness.

  • Problem

    Current data-analysis workflows expose final answers without separately evaluating whether systems faithfully understand messy workbooks’ logical structure before analysis.

  • Method

    The paper introduces two benchmarks evaluating structured Data Exploration artifacts covering tables, columns, semantic roles, keys, relationships, and profiling signals.

  • Results

    Current LLMs and data-analysis agents miss logical schema, implicit entity, and relationship structure, while stronger Data Exploration support often improves downstream correctness.

  • Takeaways & Limitations

    Data Exploration should be an explicit, inspectable evaluation stage and human-in-the-loop checkpoint in LLM data-analysis workflows.

  • Takeaways & Limitations

    Data Exploration artifacts do not ensure downstream re-grounding, so their support must be actively used by the system.

Abstract

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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.

1 INTRODUCTION

The paper argues that Data Exploration—the explicit construction of a dataset-grounded understanding before analysis—is an overlooked prerequisite for reliable, inspectable LLM data analysis. It introduces benchmarks and downstream experiments showing that evaluating and strengthening this stage exposes failures and can improve task performance.

  • Data Exploration as a first-class stage: Data Exploration identifies logical tables, column semantics, keys, relationships, and quality issues before downstream analysis begins.It makes dataset understanding a distinct, inspectable phase rather than an implicit byproduct of producing a final answer.
  • Data Exploration as a first-class stage: Explicit Data Exploration artifacts externalize a system’s inferred workbook understanding in a structured, dataset-grounded representation before downstream execution.The proposed workflow inserts this artifact between the uploaded workbook and downstream analysis.
  • Benchmarks and evaluation: The paper evaluates dataset understanding through two settings: a real multi-sheet Vitamin D workbook benchmark and 12 extended DSBench tasks with schema-fixed artifacts and ground-truth annotations.Both settings assess structured artifacts covering logical tables, columns, semantic roles, relationships, profiling signals, and source grounding.
  • Findings: Benchmark results identify persistent failures in logical schema recovery, implicit entity identification, and relation inference, beyond surface-level reading errors.These failures show why end-to-end correctness alone may not establish faithful dataset understanding or trustworthy reasoning.
  • Findings: Stronger Data Exploration support improves downstream correctness, while structured artifacts provide domain experts a checkpoint to inspect, correct, and reuse dataset understanding.The experiments vary the amount and reliability of support before task execution across the real workbook and selected DSBench tasks.

2 BACKGROUND AND MOTIVATION

Reliable analysis of messy workbooks requires understanding their logical data objects, valid records, field meanings, and cross-sheet relationships before answering requests. Because this dataset comprehension is often implicit despite its central role in human analysis, the paper formalizes it as Data Exploration.

  • Data analysis over messy workbooks: Messy-workbook analysis must infer relevant data objects, valid records, field meanings, and cross-sheet relationships before producing reliable downstream outputs.Downstream outputs include answers, tables, plots, cleaned datasets, feature files, and summaries.
  • LLM development for data analysis: tool use and reasoning: Tool use and inference-time reasoning help LLMs inspect files, execute code, retrieve information, and decompose requests into intermediate steps.These capabilities support task solving but do not by themselves make dataset understanding a distinct analytical stage.
  • Specialized LLM-based tools for data analysis: Specialized LLM data-analysis tools typically fold dataset understanding into question answering and code execution rather than treating it as a distinct analysis stage.Examples include PandasAI, SheetCopilot, and ChatGPT’s data-analysis workflow.
  • What real data analysts do first: Human analysts treat dataset comprehension as an early necessity, with profiling shared across all analyses and exploratory analysis remaining iterative even when a downstream question is known.Profiling assesses data quality, identifies structural properties, and builds a mental model of the dataset.
  • Data Exploration: Spreadsheet workbooks can hide logical structures behind their physical layouts, so the paper formalizes this neglected inference step as Data Exploration.Visible cell organization may only partially reflect the analytical objects needed downstream.

3 DATA EXPLORATION

Data Exploration is a pre-analysis phase that converts workbook layouts into an explicit, analysis-ready logical description for downstream analysis. The paper operationalizes this understanding as a schema-fixed JSON artifact that is comparable, auditable, correctable, and automatically scorable.

  • Definition: Data Exploration describes logical data objects, columns and semantic roles, keys, relationships, and lightweight quality or profiling signals.It turns the physical layout of uploaded workbooks into metadata that downstream analysis can rely on.
  • Workbook Understanding: Reliable workbook analysis requires recognizing identifiers, timepoints, repeated measurements, non-data rows, and measurement-quality structure before feature construction.The Vitamin D workbook illustrates why locating measurement cells alone is insufficient; DSBench objects may likewise be latent in spreadsheet layout.
  • Evaluation Artifact: A schema-fixed JSON artifact makes dataset understanding comparable across systems, auditable and correctable by domain experts, and automatically scorable without hidden internal states.It provides a concrete implementation of the “walk before you run” principle by describing the dataset before downstream reasoning begins.

4 DATA EXPLORATION BENCHMARK

The benchmark makes Data Exploration explicit by requiring systems to infer a schema-fixed JSON artifact from raw Excel workbooks before downstream analysis. It evaluates logical dataset structure—including tables, schemas, relationships, provenance, and profiling—against human-edited gold artifacts using alignment and component-level scoring.

  • Benchmark construction: The benchmark extends selected DSBench tasks and a real-world Vitamin D workbook with an explicit Data Exploration prompt, output template, ground-truth metadata, and automatic evaluator.Raw workbooks and original downstream questions remain unchanged.
  • Benchmark task: Systems must produce conforming JSON that recovers the dataset’s logical structure, including normalized or derived tables when they better represent the underlying organization.The task is performed before any downstream analysis and targets logical structure rather than spreadsheet-tab layout.
  • Artifact specification: The artifact captures logical tables and row counts; physical and logical column names, types, roles, and dictionary fields; keys and relations; source provenance; and profiling-relevant structure.These fields are evaluated at the level of logical dataset entities rather than spreadsheet tabs.
  • Ground truth and alignment: Each dataset has a human-editable gold JSON recording expected logical tables, columns, relations, provenance fields, and profiling structure for evaluation.Predicted and gold objects are aligned so equivalent logical objects can differ in naming or normalization.
  • Evaluation: The raw score combines table, column, and relation F1 with type, role, naming, row-count, provenance, field, profiling, and outlier-quality components, omitting inapplicable terms.Alignment uses structural, semantic, and provenance cues, while unmatched objects count as false positives or negatives.

5 BENCHMARK TESTING RESULTS

Benchmark results show that Data Exploration remains difficult on both selected multi-sheet DSBench workbooks and a real Vitamin D workbook, with persistent weaknesses in latent logical structure and relation recovery. Case studies show that systems can read visible tables while missing implicit entities and tables required for a faithful analytical schema.

  • Benchmark setup: The benchmark covers one real multi-sheet Vitamin D workbook and 12 targeted DSBench tasks with nontrivial multi-sheet or spreadsheet-specific structures.The DSBench subset is grouped into 4 simple, 5 middle, and 3 difficult tasks, using the same split for direct Data Exploration and downstream experiments.
  • DSBench findings: Data Exploration remains nontrivial because the largest persistent gaps concern logical understanding and relation recovery rather than surface-level table or column extraction.Quality varies by component: systems may perform relatively well on structural recovery or summaries while still making substantial latent-schema errors.
  • DSBench findings: Claude’s task-08 schema collapses a scenario–hub region into one table, missing the gold decomposition into Scenarios, Hubs, and ScenarioDepotInputs.This is a latent relational decomposition error rather than a naming or formatting difference; Claude predicts one relation where the gold artifact contains five.
  • DSBench findings: GPT’s task-10 artifact misses the implicit pair-level table encoded by the Bonus matrix, despite recovering several ordinary row-wise workbook structures.The omission removes relations linking each bonus record to the participating teams and winner, demonstrating failure to infer a logical object from a non-row-wise layout.
  • Vitamin D findings: The Vitamin D workbook also exhibits consequential Data Exploration failures, including low relation-recovery and lightweight quantitative-characterization scores.This shows that the difficulty extends beyond the DSBench extension to a real analytical workbook.

6 DOES DATA EXPLORATION IMPROVE DOWNSTREAM ANALYSIS?

Across the Vitamin D workbook and selected DSBench tasks, stronger Data Exploration support generally improves downstream correctness, especially when solving latent structure, relationship, and spreadsheet-logic problems. The case studies show that support helps procedural decomposition, dependency reasoning, and answer grounding, but only when actively used and verified.

  • Overall downstream results: Stronger Data Exploration support generally improves downstream correctness across the real workbook and DSBench settings.On the Vitamin D workbook, performance rises from control to middle to treatment, especially for downstream feature-table outputs; DSBench shows the same pattern with greater difficulty-dependent variation.
  • Overall downstream results: Data Exploration is most helpful on middle and difficult tasks requiring latent structure, relations, and intermediate spreadsheet logic rather than surface extraction.Treatment is usually strongest, although middle occasionally slightly outperforms treatment when externally provided information is not fully leveraged.
  • Financial-model case study: GPT improves from 15/20 in Control to 16/20 in Middle and 17/20 in Treatment on a difficult financial-model task.Middle improves dependency reconstruction for final questions, while Treatment localizes a circularity source; GPT still misses Q3 and Q6, so support does not eliminate formula-level errors.
  • Financial-model case study: Claude improves monotonically, from 6/9 in Control to 7/9 in Middle and 9/9 in Treatment.Middle supports procedural decomposition of the hub reassignment chain, while Treatment reduces residual answer-grounding errors when correct computations must map to multiple-choice selections.
  • Financial-model case study: Gemini rises from 6/9 in Control to 9/9 in Middle, then falls to 7/9 in Treatment, showing that supplied artifacts can fail when downstream reasoning is not re-grounded.Middle correctly applies the workbook-specific reassignment logic, whereas Treatment introduces an incorrect reassignment path; the broader case shows that support must be actively used and procedurally verified.

7 DISCUSSION AND FUTURE WORK

The discussion argues that Data Exploration should become a first-class benchmark axis because it is practically important yet under-evaluated. It also outlines future work on broader datasets, reusable artifacts, and human-in-the-loop analysis pipelines.

  • Data Exploration is practically important and currently under-evaluated, motivating its inclusion as a benchmark axis alongside downstream question answering.
  • Future work should expand dataset diversity, quantify links between exploration components and downstream gains or failures, and support caching, editing, and reuse of exploration artifacts.The upfront exploration cost may be amortized when verified, dataset-specific artifacts support multiple downstream tasks.
  • Data Exploration matters because possible analysis questions and interpretations depend critically on what the data contains, shaping AI-assisted analysis pipelines.

8 CONCLUSION

Reliable LLM data-analysis tools should understand a workbook before answering downstream questions. This paper makes that pre-analysis step explicit as Data Exploration and evaluates it through benchmark settings using real and schema-fixed workbook artifacts.

  • Reliable data-analysis tools should form a faithful, dataset-grounded understanding of a workbook before answering downstream questions.
  • The paper makes this pre-analysis step explicit as Data Exploration.
  • The benchmarks include a real multi-sheet Vitamin D workbook and selected DSBench tasks with schema-fixed Data Exploration artifacts and automatic component-level evaluation.
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