Enhancing Data Quality and Integrity with AI: A Deep Learning Perspective Author: Joseph Oluwaseyi, Fajinmi John

Abstract

Data quality and integrity are critical for accurate decision-making, yet organizations face challenges such as incomplete, inconsistent, and erroneous data. Traditional data management approaches often struggle to maintain high data quality, especially in large-scale, dynamic environments. Artificial Intelligence (AI), particularly deep learning, offers advanced solutions for improving data quality and integrity through automation, pattern recognition, and anomaly detection. This paper explores how deep learning techniques enhance data quality by identifying and correcting inconsistencies, detecting anomalies, and filling missing values using predictive modeling. It also examines AI-driven methods for ensuring data integrity, such as adversarial learning for fraud detection, blockchain integration for data immutability, and AI-enhanced access control mechanisms. Additionally, we discuss the role of AI in real-time data validation, deduplication, and error correction, highlighting case studies across industries such as finance, healthcare, and supply chain management. By leveraging deep learning models for automated data cleaning, validation, and security, organizations can achieve higher accuracy, reliability, and trust in their data-driven processes. This paper provides insights into the evolving role of AI in data governance, emphasizing the need for scalable and interpretable deep learning approaches to maintain robust data quality and integrity.

Keywords: Artificial Intelligence, blockchain, Data, deep learning