The Intersection of Generative AI and Data Profiling: Emerging Techniques for Automated and Explainable Insights

Abstract

Integrating generative AI into data profiling marks a transformative leap in how organizations manage, analyze, and derive insights from complex datasets. This review explores cutting-edge advancements, focusing on generative AI's role in data synthesis, anomaly detection, and pattern recognition. It highlights the importance of explainable AI to ensure trust and transparency in decision-making. Technical challenges such as scalability, privacy concerns, and computational costs are discussed alongside opportunities in real-time data integration, multi-modal profiling, and healthcare, finance, and IoT applications. Furthermore, the paper outlines key research directions, including hybrid models that combine traditional methods with generative AI, privacy-preserving solutions, and domain-specific applications. These advancements are critical for addressing modern data profiling challenges while adhering to ethical and regulatory standards. Emphasizing interdisciplinary collaboration, this paper calls for partnerships between researchers, industry experts, and policymakers to ensure the development of scalable, efficient, and secure generative AI-driven profiling techniques for diverse industries.