# Unveiling Challenges for LLMs in Enterprise Data Engineering

arXiv: https://arxiv.org/abs/2504.10950 · HTML: https://mlsanity.com/paper/2504.10950/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Apr 2025.

## Built on by (most cited first, 9 of 9)

- [LLMs as World Models: Data-Driven and Human-Centered Pre-Event Simulation for Disaster Impact Assessment](https://mlsanity.com/paper/2506.06355/) (2025)
- [On Path to Multimodal Historical Reasoning: HistBench and HistAgent](https://mlsanity.com/paper/2505.20246/) (2025)
- [Make Still Further Progress: Chain of Thoughts for Tabular Data Leaderboard](https://mlsanity.com/paper/2505.13421/) (2025)
- [Something's Fishy In The Data Lake: A Critical Re-evaluation of Table Union Search Benchmarks](https://mlsanity.com/paper/2505.21329/) (2025)
- [EnterpriseLab: A Full-Stack Platform for developing and deploying agents in Enterprises](https://mlsanity.com/paper/2603.21630/) (2026)
- [Exploring Differences Between Tabular Enterprise Data and Public Benchmarks](https://mlsanity.com/paper/2606.30452/) (2026)
- [Single and Multi Truth Data Fusion using Large Language Models](https://mlsanity.com/paper/2606.28062/) (2026)
- [Towards Trustworthy and Cost-Efficient Data Integration: From Na"ive RAG to Agentic RAG](https://mlsanity.com/paper/2607.22319/) (2026)
- [ProCARE: Real-World Study Automation via Profile-Grounded Evidence Contracts](https://mlsanity.com/paper/2610.16175/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) promise to automate data engineering on tabular data, offering enterprises a valuable opportunity to cut the high costs of manual data handling. But the enterprise domain comes with unique challenges that existing LLM-based approaches for data engineering often overlook, such as large table sizes, more complex tasks, and the need for internal knowledge. To bridge these gaps, we identify key enterprise-specific challenges related to data, tasks, and background knowledge and extensively evaluate how they affect data engineering with LLMs. Our analysis reveals that LLMs face substantial limitations in real-world enterprise scenarios, with accuracy declining sharply. Our findings contribute to a systematic understanding of LLMs for enterprise data engineering to support their adoption in industry.