Exploring Differences Between Tabular Enterprise Data and Public Benchmarks

Myung Jun Kim; Maximilian Schambach; Frank Essenberger; Andre Sres; Johannes Höhne

cs.LGJun 29, 2026

Exploring Differences Between Tabular Enterprise Data and Public Benchmarks

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.

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Jun 29, 2026 · cs.LGJ/K move · Enter open · S save

Beyond IID: How General Are Tabular Foundation Models, Really?

Lennart Purucker, Andrej Tschalzev, Nick Erickson, Gioia Blayer+6

1Prior Labs · University of Mannheim · 4INRIA Saclay · 5Technion · 6ELLIS Institute Tübingen

Foundation models for predictive machine learning on tabular data have recently gained significant traction in academia and industry. Research communities across disciplines are increasingly evaluating tabular foundation models on diverse datasets and tasks. However, these task- and discipline-specific evaluations remain largely inaccessible to model researchers because benchmark software and evaluation protocols are fragmented. As a result, model researchers rely on standard benchmarks, which are mostly defined for tasks where tabular foundation models already excel. The most challenging scenarios are excluded, limiting meaningful progress in the field by focusing on marginal improvements on IID data rather than on broader, more demanding challenges. To overcome this, we introduce BeyondArena, the first unified holistic benchmark for tabular data that supports diverse task types (IID, temporal, grouped), across sample size and feature dimensionality scales, with diverse feature types (with text, with high cardinality) from a broad range of disciplines. To enable unified benchmarking beyond standard benchmarks, we introduce Data Foundry, a Python framework and metadata schema for curating tabular datasets for predictive machine learning. Our results across 11 models and 142 curated datasets show that existing tabular foundation models excel on tiny- to medium-sized IID data, while traditional tree-based and deep learning models still dominate on non-IID, large, and high-dimensional datasets. BeyondArena guides model research for the most demanding challenges in tabular data, enabling progress towards truly foundational tabular models.

Tabular Foundation Models Benchmark Datasets Foundation Model Machine Learning Beyond Retrieval Arena

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Abstract

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