Exploring Differences Between Tabular Enterprise Data and Public Benchmarks
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Abstract (arXiv)
Tabular data dominate the landscape of data science, increasingly attracting innovative machine learning models and tailored benchmarks. Yet, little is known for enterprise data, where tables constitute the backbone of business operations. To broaden the benchmarking landscape for business applications, this work aims to actualize the characteristics of enterprise data by providing an analysis of data statistics and performance measurements of tabular models such as TabPFN, TabICL and ConTextTab. Through our analysis, we find enterprise data markedly differ from tabular benchmarks and we demonstrate that a tabular model that performs well on typical tabular benchmarks may perform poorly on real world enterprise data -- and vice versa. This lack of generalization underlines the need for additional benchmarks with enterprise-grade characteristics.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 18 of its 27 references that are in the map. First posted on arXiv 29 Jun 2026.
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18 of 27 references.
References · 18
- 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
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- 2017OpenML Benchmarking Suitescited by 169
- 2025TabArena: A Living Benchmark for Machine Learning on Tabular Datacited by 152
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- 2026TabICLv2: A better, faster, scalable, and open tabular foundation modelcited by 112
- 2024TabM: Advancing Tabular Deep Learning with Parameter-Efficient Ensemblingcited by 105
- 2024Why Tabular Foundation Models Should Be a Research Prioritycited by 103
- 2024Better by Default: Strong Pre-Tuned MLPs and Boosted Trees on Tabular Datacited by 87
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- 2024CARTE: Pretraining and Transfer for Tabular Learningcited by 69
- 2024TALENT: A Tabular Analytics and Learning Toolboxcited by 46
6 more
- 2025ConTextTab: A Semantics-Aware Tabular In-Context Learnercited by 31
- 2024TabReD: Analyzing Pitfalls and Filling the Gaps in Tabular Deep Learning Benchmarkscited by 29
- 2025SALT: Sales Autocompletion Linked Business Tables Datasetcited by 12
- 2025Unveiling Challenges for LLMs in Enterprise Data Engineeringcited by 8
- 2024Mind the Data Gap: Bridging LLMs to Enterprise Data Integrationcited by 7
- 2025Position: Foundation Models for Tabular Data within Systemic Contexts Need Groundingcited by 5
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