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

TL;DR

This paper systematically reviews 151 data quality tools for data warehouses, highlighting the limited AI support for DQ management and proposing a shift towards AI-driven DQ solutions to enhance automation and explainability.

Contribution

It provides a comprehensive evaluation of existing tools' AI capabilities for DQM and outlines design requirements for future AI-augmented data quality management solutions.

Findings

Only 10 tools support AI-augmented DQM.

Most tools focus on data cleansing rather than AI for DQ.

AI features like explainability are scarce in current tools.

Abstract

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''.