Synthetic Data Utilization in Analytics

This case study discusses the concept of synthetic data and its application in analytics, particularly in addressing data scarcity and enhancing data privacy. It defines synthetic data as artificially generated data that mimics real-world data characteristics, created using algorithms such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). The document outlines the advantages of synthetic data, including improved data quality, scalability, and enhanced risk management capabilities. It emphasizes how synthetic data can facilitate compliance with privacy regulations like GDPR while allowing organizations to leverage data analytics effectively. The case study also highlights a collaboration with Deloitte, showcasing how synthetic data was used to synthesize a relational database containing over 8 million records. The document concludes with best practices for utilizing synthetic data responsibly, emphasizing the importance of selecting appropriate models for specific use cases.