Exploring Differences Between Tabular Enterprise Data and Public Benchmarks

• URL: http://arxiv.org/abs/2606.30452v1
• Date: Mon, 29 Jun 2026 15:21:17 GMT
• Title: Exploring Differences Between Tabular Enterprise Data and Public Benchmarks
• Abstract summary: Tabular data dominate the landscape of data science, increasingly attracting innovative machine learning models and tailored benchmarks.<n>This work aims to actualize the characteristics of enterprise data by providing an analysis of data statistics and performance measurements.
• Score: 2.2301226527276783
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Beyond IID: How General Are Tabular Foundation Models, Really? [76.03669797389378]
  Research communities are increasingly evaluating foundation models on diverse datasets and tasks.<n>Standard benchmarks are mostly defined for tasks where tabular foundation models already excel.<n>We introduce BeyondArena, the first unified holistic benchmark for tabular data.
  arXiv  Detail & Related papers  (2026-06-29T14:55:52Z)
• MulTaBench: Benchmarking Multimodal Tabular Learning with Text and Image [52.47434184153733]
  We introduce MulTaBench, a benchmark of 40 datasets split equally between image-tabular and text-tabular tasks.<n>We show that the gains from target-aware representation tuning generalize across both text and image modalities.
  arXiv  Detail & Related papers  (2026-05-11T14:12:05Z)
• Towards Universal Tabular Embeddings: A Benchmark Across Data Tasks [15.117602507724726]
  Tabular representation learning models aim to learn universal representations of data that transfer across tasks and domains.<n>Existing methods are often evaluated under task-specific settings that make direct comparison difficult.<n>We introduce TEmBed, a benchmark for systematically evaluating embeddings across four representation levels.
  arXiv  Detail & Related papers  (2026-04-23T14:05:38Z)
• OmniTabBench: Mapping the Empirical Frontiers of GBDTs, Neural Networks, and Foundation Models for Tabular Data at Scale [82.32822441887886]
  We introduce OmniTabBench, the largest benchmark to date, comprising 3030 datasets spanning diverse tasks.<n>We conduct an unprecedented large-scale empirical evaluation of state-of-the-art models from all model families on OmniTabBench, confirming the absence of a dominant winner.
  arXiv  Detail & Related papers  (2026-04-08T08:31:43Z)
• Universal Embeddings of Tabular Data [0.0]
  Tabular data in relational databases represents a significant portion of industrial data.<n>We present a novel framework for generating universal, i.e., task-independent embeddings of tabular data for performing downstream tasks without predefined targets.
  arXiv  Detail & Related papers  (2025-07-08T11:45:29Z)
• TabFSBench: Tabular Benchmark for Feature Shifts in Open Environments [45.368146581808276]
  Tabular data is widely utilized in various machine learning tasks.<n>Previous research has primarily concentrated on mitigating distribution shifts, whereas feature shifts, have garnered limited attention.<n>This paper conducts the first comprehensive study on feature shifts in tabular data and introduces the first tabular feature-shift benchmark (TabFSBench)
  arXiv  Detail & Related papers  (2025-01-31T07:40:34Z)
• TabReD: Analyzing Pitfalls and Filling the Gaps in Tabular Deep Learning Benchmarks [30.922069185335246]
  We find two common characteristics of tabular data in typical industrial applications that are underrepresented in the datasets usually used for evaluation in the literature. A considerable portion of datasets in production settings stem from extensive data acquisition and feature engineering pipelines. This can have an impact on the absolute and relative number of predictive, uninformative, and correlated features compared to academic datasets.
  arXiv  Detail & Related papers  (2024-06-27T17:55:31Z)
• LaTable: Towards Large Tabular Models [63.995130144110156]
  Tabular generative foundation models are hard to build due to the heterogeneous feature spaces of different datasets. LaTable is a novel diffusion model that addresses these challenges and can be trained across different datasets. We find that LaTable outperforms baselines on in-distribution generation, and that finetuning LaTable can generate out-of-distribution datasets better with fewer samples.
  arXiv  Detail & Related papers  (2024-06-25T16:03:50Z)
• Rethinking Pre-Training in Tabular Data: A Neighborhood Embedding Perspective [71.45945607871715]
  We propose Tabular data Pre-Training via Meta-representation (TabPTM)<n>The core idea is to embed data instances into a shared feature space, where each instance is represented by its distance to a fixed number of nearest neighbors and their labels.<n>Extensive experiments on 101 datasets confirm TabPTM's effectiveness in both classification and regression tasks, with and without fine-tuning.
  arXiv  Detail & Related papers  (2023-10-31T18:03:54Z)
• Leveraging Data Recasting to Enhance Tabular Reasoning [21.970920861791015]
  Prior work has mostly relied on two data generation strategies. The first is human annotation, which yields linguistically diverse data but is difficult to scale. The second category for creation is synthetic generation, which is scalable and cost effective but lacks inventiveness.
  arXiv  Detail & Related papers  (2022-11-23T00:04:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.