Automated data quality checks are essential for ensuring the integrity and reliability of data in various systems. These checks can identify errors, inconsistencies, and anomalies in data sets, which is crucial for maintaining data hygiene. Automation in this context often involves the use of advanced technologies, such as Large Language Models (LLMs), which can analyze data with minimal human intervention.

LLMs are particularly effective in detecting data quality issues due to their extensive training on diverse content. They can convert tabular data into plain text, allowing them to scrutinize it similarly to experienced human analysts. This capability enables LLMs to identify potential errors based on textual content without needing explicitly defined rules. Additionally, they can handle the uncertainty associated with data quality issues, making them a valuable tool in data management ¹.

Moreover, various tools and frameworks exist to facilitate automated data quality checks, aiming to improve visibility into data quality issues and reduce incidents. However, successful implementation often requires a focus on process failures rather than just addressing bad records ⁵.