A Guide to Synthetic Data Generation 

A Solution to Augmenting High Quality Data and Preserving Privacy 

About the guide

In different sectors of society, organizations may generate huge volumes of data that are extremely complex and varied. These datasets are often stored in silos within organizations for various reasons, including but not limited to regulatory requirements and business needs. As a result, data sharing within different lines of business as well as outside of the organization (e.g. to the research community) is severely limited. It is therefore critical to investigate methods for synthesising datasets that follow the same properties of the real data while respecting the need for privacy of the parties involved.  

Our article serves as a guide to different types of synthetic data, synthetic data generation techniques, and how to choose the right technique. Complete the form to download your copy!  

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