# From Data Quality for AI to AI for Data Quality: A Systematic Review of Tools for AI-Augmented Data Quality Management in Data Warehouses

arXiv: https://arxiv.org/abs/2406.10940 · HTML: https://mlsanity.com/paper/2406.10940/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 38 references that are in the map.
- First posted on arXiv 16 Jun 2024.

## Builds on (most cited first, 2 of 2)

- [Machine Learning for Synthetic Data Generation: A Review](https://mlsanity.com/paper/2302.04062/) (2023)
- [A Survey of Data Quality Measurement and Monitoring Tools](https://mlsanity.com/paper/1907.08138/) (2019)

## Built on by (most cited first, 1 of 1)

- [Team, Then Trim: An Assembly-Line LLM Framework for High-Quality Tabular Data Generation](https://mlsanity.com/paper/2602.04785/) (2026)

## Abstract (arXiv)

While high data quality (DQ) is critical for analytics, compliance, and AI performance, data quality management (DQM) remains a complex, resource-intensive, and often manual process. This study investigates the extent to which existing tools support AI-augmented data quality management (DQM) in data warehouse environments. To this end, we conduct a systematic review of 151 DQ tools to evaluate their automation capabilities, particularly in detecting and recommending DQ rules in data warehouses -- a key component of modern data ecosystems. Using a multi-phase screening process based on functionality, trialability, regulatory compliance (e.g., GDPR), and architectural compatibility with data warehouses, only 10 tools met the criteria for AI-augmented DQM. The analysis reveals that most tools emphasize data cleansing and preparation for AI, rather than leveraging AI to improve DQ itself. Although metadata- and ML-based rule detection techniques are present, features such as SQL-based rule specification, reconciliation logic, and explainability of AI-driven recommendations remain scarce. This study offers practical guidance for tool selection and outlines critical design requirements for next-generation AI-driven DQ solutions -- advocating a paradigm shift from ``data quality for AI'' to ``AI for data quality management''.