Data Analysis in the Wild: Benchmarking Large Language Models Against Real-World Data Complexities

• URL: http://arxiv.org/abs/2607.06482v1
• Date: Tue, 07 Jul 2026 16:43:05 GMT
• Title: Data Analysis in the Wild: Benchmarking Large Language Models Against Real-World Data Complexities
• Abstract summary: We introduce DataGovBench, a benchmark designed to evaluate Large Language Models in practical scenarios.<n> Comprehensive experiments with state-of-the-art LLMs reveal significant performance gaps across both tasks.<n>DataGovBench provides a challenging benchmark for advancing research on LLMs capable of both answering analytical queries and discovering insights from data.
• Score: 16.09389994769264
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Current benchmarks for evaluating Large Language Models (LLMs) in data analysis often fail to reflect real-world settings. They typically focus on fact retrieval from small tables and overlook the challenges of large multi-tabular datasets, external knowledge integration, and exploratory insight discovery. We introduce DataGovBench, a benchmark derived from governmental open data designed to evaluate LLMs in practical scenarios. The benchmark includes two tasks: Table QA that requires solving complex decomposable questions and producing textual answers or visualizations, and Table Insight that evaluates the ability of models to generate expert-level findings through exploratory data analysis. Comprehensive experiments with state-of-the-art LLMs, both with and without agentic frameworks, reveal significant performance gaps across both tasks. These results suggest that current LLM-based systems remain far from satisfying the demands of real-world data analytics. DataGovBench provides a challenging benchmark for advancing research on LLMs capable of both answering analytical queries and discovering insights from data. Code and sample data are available at https://github.com/SoHasegawa/datagovbench.

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