Synthetic Data: A Guide to Responsible Adoption

In this paper, we demystify synthetic data and provide recommendations on when, where, how, and who to trust for responsible, safe, and value-adding implementation. 

When using Generative AI to create synthetic data, remember that this technology is not magic – it is math. It may appear magical when used correctly, but that’s only when it combines the best of human and artificial intelligence: when experienced researchers combine proprietary analytics frameworks, select the right AI/model for the specific task at hand, inject fresh, purposeful consumer data from real people, apply prompt engineering from domain experts, tap into fine-tuned data science algorithms, and leverage norms databases and data assets. 

Simply put, the quality and reliability of synthetic data is entirely dependent on the real human data used to create and update it, as well as the expertise of the people behind it all. To that end, be wary of providers that lack the proper controls, reputation, expertise, or validation, and can thusly wreak havoc on brands and businesses. 

Download our point of view to gain a true understanding of the current landscape and what the future may hold. We hope it will help you form an objective opinion of synthetic data, demonstrate both its potential and its risks, and refine the questions you need to ask yourself and your partners before you start considering it.