Generative AI for Data Analytics & Synthetic Data

Abstract

Generative Artificial Intelligence (GAI) has emerged as a transformative force in data analytics and synthetic data generation, providing advanced capabilities for predictive modeling, scenario simulation, and privacypreserving data augmentation. By leveraging techniques such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformer-based models, generative AI enables organizations to synthesize high-fidelity datasets that closely resemble real-world data while mitigating privacy concerns. This paper presents a comprehensive review of generative AI applications in data analytics, methodologies for synthetic data generation, key challenges, and future directions. The study highlights the growing significance of generative AI in enhancing decision-making processes, improving data accessibility, and supporting advanced analytics in sectors such as healthcare, finance, and smart cities.

KEYWORDS: Generative AI, Synthetic Data, Data Analytics, GANs, Variational Autoencoders, Transformer Models, Privacy-Preserving Data, Predictive Analytics