Leveraging AI for Better Data Quality and Insights

Abstract

The exponential growth of data across industries has highlighted the critical importance of data quality management for ensuring reliable insights and decision-making. Artificial intelligence has emerged as a transformative force in this domain, offering sophisticated approaches to detect errors, inconsistencies, and anomalies in complex datasets. This article explores the fundamental principles of data quality control, examines AI-powered methodologies including machine learning algorithms, deep learning architectures, and natural language processing techniques, and investigates their domain-specific applications across healthcare, finance, marketing, manufacturing, and government sectors. Despite significant advancements, challenges persist related to scalability, human-AI collaboration, privacy concerns, model interpretability, and adaptation to evolving data patterns. Emerging trends such as explainable AI, human-in-the-loop frameworks, transfer learning, federated approaches, real-time monitoring, and quantum computing applications promise to further enhance AI's effectiveness in elevating data quality standards and unlocking greater value from organizational data assets.