Synthetic data

Why You Need Synthetic Data for AI Adoption

Written by Roy Yogev

The age of Artificial Intelligence (AI) is here at last. AI applications underpin everything from marketing campaigns to compliance procedures to security features. They drive innovation in financial services, healthcare and transport. More than half of data and analytics leaders already use AI to inform their decision-making. By next year, three-quarters of enterprises are expected to embed it into their processes and technologies. 

It’s not a trend that’s going away any time soon. Governments the world over are investing serious money to expand the sector. The EU has poured $20 billion into supporting AI growth, while the U.S., UK, and China have all unveiled their own domestic schemes worth billions of dollars. 

AI is shaping nearly every industry and area of business, but many of the most promising opportunities are in the finance sector. Risk assessment, algorithmic trading, credit scoring, personalized offers… all these operations and applications can be enhanced with cutting-edge AI technology. Used right, it removes friction, improves accuracy, reduces delays and cuts costs. 

But there’s a problem: AI demands a lot of high quality, accurate, joined-up data. That data exists, of course – and most large organizations with decent data science capacity in-house possess the tools and skills to derive enormous value from it. The trouble is, you need to ensure this sensitive data stays completely confidential. That it can’t be traced back to any real people. 

Balancing the need for privacy with the need for data that’s comprehensive, nuanced and extensive enough to give a complete, accurate picture is a constant challenge. A whole raft of privacy-preserving technologies (PPTs) have tried to fix the issue, mostly by attempting to sever or obscure the link between data points and the original, real-life people they pertain to. 

Techniques range from crude anonymization steps (like simply removing the most obvious personally identifying data) through to far more sophisticated approaches, like differential privacy and homomorphic encryption. Meanwhile, other innovators are experimenting with ways of splitting up access to sensitive datasets between several stakeholders, so that none of them can retrieve all the data alone.

But simply anonymizing data, switching data points around, or adding in extra layers of disguise isn’t enough to thwart a committed hacker, determined to reverse-engineer the process or otherwise re-identify the real people underlying a dataset. Nor does breaking up datasets into pieces completely eliminate the risk of leaks and other breaches. 

Moreover, the more steps you take to protect your data, the less you’re at liberty to actually use it in creative, scalable ways that deliver genuine business value. At the end of the day, your AI-driven data analytics becomes more effective the more data sources you combine and the more contextual detail you add. The more work you do to build up a complete picture of a customer, the more potential there will be, inevitably, to match that picture to a real name that exists out in the world.

This is why more and more businesses seeking to overcome barriers to adopting AI-driven, intelligent data analytics are realizing that the best way to get around the problem is to decouple the data points from real people entirely. You can’t identify a person that doesn’t actually exist, no matter how statistically accurate and indistinguishable from reality their profile is.

This has led to an extremely exciting new development: synthetic data. With synthetic data, you create high-quality, scalable, shareable, entirely realistic datasets that aren’t linked to any real people. Your privacy risk isn’t mitigated; it’s eradicated. 


No wonder businesses keen to unlock lucrative opportunities created by AI in the coming years are putting synthetic data at the core of their data privacy strategy.


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