In today’s world, data is a crucial component of many businesses, helping them make better decisions and stay ahead of their competition. However, with data comes a risk – privacy and security. This is where synthetic data comes in, offering the same predictive power as original data, without any of the privacy concerns.

Synthetic data is artificial data generated by an AI algorithm that has been trained on a real data set. It reproduces the statistical properties and patterns of the original dataset, without any of the original data ever being reconstructed from either the algorithm or the synthetic data. This makes it ideal for businesses that need to access data quickly, securely, and cost-effectively.

One of the most significant benefits of synthetic data is that it eliminates the risk of exposing critical data and compromising the privacy and security of companies and customers. With techniques like encryption, anonymization, and advanced privacy-preserving, companies can protect the original data and the information in that data that could be traced back to an individual. Synthetic data replaces the original data, making it impossible to compromise.

Another advantage of synthetic data is its ability to eliminate roadblocks in privacy and security protocols that often make it difficult and time-consuming to get and use data. This means businesses can get access to their data quickly and start generating value from it without worrying about privacy and security issues.

The scale is also a by-product of security and speed. Secure and faster access to data allows companies to expand the amount of data they can analyze and the types and numbers of problems they can solve. Synthetic data sets from third parties make it much easier and cheaper for companies to supplement their data with additional data from many other sources, so they can learn more about the problem they’re trying to solve and get more accurate answers—without the worry of compromising anyone’s privacy.

Synthetic data also helps companies that are limited by the data they own. Big companies’ current modeling efforts tend to be quite narrow because they’re limited to just the data they own. Companies can, of course, purchase third-party data in its “original” form, but it’s often prohibitively expensive and comes with related privacy concerns. Synthetic data makes it much easier for companies to supplement their own data with additional data from many other sources, so they can learn more about the problem they’re trying to solve and get more accurate answers—without the worry of compromising anyone’s privacy.

However, generating synthetic data requires more than just plugging in an AI tool to analyze your data sets. It requires people with advanced knowledge of AI and specialized skills, as well as specific, sophisticated frameworks that enable a company to validate that it created what it set out to create. This means businesses need to invest in their AI expertise and infrastructure to ensure they can generate synthetic data properly and securely.

In conclusion, synthetic data is a game-changer for businesses that need access to data quickly, securely, and cost-effectively. Its benefits are compelling and significant, including eliminating the risk of exposing critical data and compromising the privacy and security of companies and customers. It also allows businesses to supplement their own data with additional data from many other sources, learn more about the problem they’re trying to solve, and get more accurate answers. However, generating synthetic data requires specialized skills and frameworks, which means businesses need to invest in their AI expertise and infrastructure to realize its benefits fully.