Synthetic data may be artificial, but it has real value. What is synthetic data and what is it used for?

Synthetic data is artificially generated information. Although it has many uses, synthetic data is particularly valuable as a way to preserve privacy in sensitive data, and as a way to model rare “black swan” events.

For example, Buzz Solutions, a GoPoint portfolio company, uses synthetic images alongside existing proprietary data to help train its cutting-edge AI tools which can detect faults in critical infrastructure. “Obviously we cannot light an electricity substation on fire,” says Buzz Solutions Co-Founder Vikram Chaudhry “so we created a 3D model and trained our AI based on this synthetic data.”

This approach has worked well, with Buzz Solutions’ AI performing with significantly greater accuracy than any other tested model in an evaluation run by the New York Power Authority. But, Vik explains, it’s a rookie mistake to think you can easily train a model with only synthetic data. “What is most important is that we have an unrivaled set of real-world data, and our synthetic data is built on top of that stack.” 

When even “real” data depends on careful interpretation and analysis, a business that believes they can rely on synthetic data alone is likely fooling themselves. At the same time, today’s wave of synthetic data companies need to find a way to stand out amidst an increasingly competitive field where image and data creation may soon be commoditized.

There’s no question that demand for synthetic data will rapidly rise in many industries. As AI technology improves, foundation models proliferate, and compute cost continues to decline, synthetic data will be used almost everywhere. However, widespread adoption will be partially a function of easy-to-use interface layers and workflow solutions which make synthetic data–as well as its creation and analysis–accessible. 

Just as the internet only became practically usable for millions after the advent of modern web browsers and graphical user interfaces, many synthetic data tools and consumer-facing use cases may only see widespread adoption once an easy to use workflow solution is implemented. 

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