Exploring Differences Between Tabular Enterprise Data and Public Benchmarks

2026-06-29

AI Summary

This study addresses the limitations of existing tabular data benchmarks, which predominantly rely on public datasets and fail to capture the structural complexity and distributional characteristics of enterprise-grade data, thereby constraining model generalization in real-world business applications. For the first time, this work systematically compares enterprise and public tabular datasets in terms of statistical properties and model performance, leveraging state-of-the-art models such as TabPFN, TabICL, and ConTextTab alongside multidimensional statistical analysis and cross-dataset evaluation. The experiments reveal significant performance discrepancies between the two data types: models excelling on public benchmarks often underperform on enterprise data, and vice versa. These findings underscore the inadequacy of current benchmarks and highlight the critical need for tabular learning benchmarks specifically designed for enterprise scenarios.

Abstract

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.

Problem

• tabular data
• enterprise data
• benchmarks
• generalization
• machine learning

Innovation

• enterprise tabular data
• benchmarking
• model generalization
• tabular machine learning
• data characteristics