Exploring Differences Between Tabular Enterprise Data and Public Benchmarks
2026-06-29 • Machine Learning
Machine Learning
AI summaryⓘ
The authors studied how machine learning models work on business-related tables, which are different from typical data used in research. They found that models doing well on common table data often struggle with real enterprise data, and vice versa. This shows that current testing methods may not be good enough for real business uses. The authors suggest creating new tests that better match the kind of data businesses actually use.
tabular datamachine learning modelsenterprise databenchmarkingTabPFNTabICLConTextTabgeneralizationperformance measurement
Authors
Myung Jun Kim, Maximilian Schambach, Frank Essenberger, Andre Sres, Johannes Höhne
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.