A Survey on Data Quality Dimensions and Tools for Machine Learning

TL;DR

This survey reviews recent data quality tools for machine learning, discusses their strengths and limitations, and explores emerging trends like large language models to improve data quality in data-centric AI.

Contribution

It provides a comprehensive comparison of 17 data quality tools, introduces a framework of DQ dimensions and metrics, and proposes a roadmap for future open-source DQ tool development.

Findings

Comparison of 17 DQ tools and their features

Identification of challenges and emerging trends in DQ for ML

Potential of LLMs and generative AI in DQ evaluation

Abstract

Machine learning (ML) technologies have become substantial in practically all aspects of our society, and data quality (DQ) is critical for the performance, fairness, robustness, safety, and scalability of ML models. With the large and complex data in data-centric AI, traditional methods like exploratory data analysis (EDA) and cross-validation (CV) face challenges, highlighting the importance of mastering DQ tools. In this survey, we review 17 DQ evaluation and improvement tools in the last 5 years. By introducing the DQ dimensions, metrics, and main functions embedded in these tools, we compare their strengths and limitations and propose a roadmap for developing open-source DQ tools for ML. Based on the discussions on the challenges and emerging trends, we further highlight the potential applications of large language models (LLMs) and generative AI in DQ evaluation and improvement for ML. We believe this comprehensive survey can enhance understanding of DQ in ML and could drive progress in data-centric AI. A complete list of the literature investigated in this survey is available on GitHub at: https://github.com/haihua0913/awesome-dq4ml.