Synthetic Data Solutions for Testing Challenges

This document is a case study that outlines the challenges faced by organizations when access to real data is restricted due to privacy concerns or regulations. It describes how traditional methods of creating test data are often time-consuming and prone to errors, leading to gaps in critical scenarios that are typically encountered in production environments. The case study highlights a partnership with Tonic.ai, which provides high-quality synthetic data generation. It details the post-generation validation process to ensure statistical accuracy and suitability for specific use cases. Furthermore, it presents a consultancy-led approach that includes a six-week evaluation of AWS Bedrock, ultimately determining its unsuitability for complex relational data. The document emphasizes the importance of expert-led data profiling to identify privacy risks and provides clear recommendations for implementing effective synthetic data solutions tailored to organizational needs.