2025/04/15 by Jan-Micha Bodensohn, Ulf Brackmann, Bodensohn, Jan-Micha +7
Business, Management and Accounting · Computer Science · #Big Data and Business Intelligence #Data Mining Algorithms and Applications #Databases (cs.DB) #FOS: Computer and information sciences
paper · pdf · doi:10.48550/arxiv.2504.10950
openalex publication_date 2025/04/15 · openalex created_date 2025/10/14 · openalex updated_date 2026/07/28
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.