Good data can be hard to find. And even when you can find it, costs or privacy concerns may prevent you from using it. Synthetic data can be the solution to these issues. Here’s an overview of what synthetic data is and a few examples of how various industries have benefited from it.
What Is Synthetic Data
Synthetic data is data that has been artificially generated by algorithms or simulations. Although it doesn’t come from the real world, it is a good enough reflection of real-world data to be as effective for training AI models.
But what is synthetic data being used for? Below you’ll find just a few of synthetic data’s applications.
The very necessary and important laws that protect the privacy and security of our data are also what make data sharing for business intelligence difficult for organizations. Often sharing data between departments is discouraged due to privacy concerns, or even when data is shared, information necessary for deriving insights is lost.
Synthetic data solves these problems. Since synthetic data is not based in the real world, it’s not subject to the same data privacy regulations. As a result, it allows businesses to share data freely across departments and more effectively derive insights that can give them a competitive edge in the marketplace.
Training models with real-world data is expensive in both cost and time. What’s more, when using video clips. you need to be careful not to violate the privacy of people in the data, or the copyright if it applies.
Synthetic datasets comprising clips of 3D models performing actions, on the other hand, easily side-steps these concerns. What’s more, models trained on synthetic data have proved to be more accurate than models trained on real-world data in certain cases during recent tests.
Recently we’ve seen increased use of machine learning for fraud and anomaly detection. Fraud affects a wide range of industries, including insurance, healthcare, eCommerce, and banking. As with all machine learning models, those used for fraud detection require training data. And like the examples above, synthetic data could have a significant impact on fraud detection by removing roadblocks like cost and privacy concerns and reducing the impact of bias.
There are many areas of healthcare where synthetic data can help solve the problems of protecting patients’ privacy, lack of high-quality data, insufficient datasets, and more. In a recent study, “Synthetic data in healthcare: a narrative review”, the researchers identified seven areas where synthetic data helps bridge the data gap: health IT development, public release of datasets, simulation and prediction research, linking data, education and training, hypothesis, methods, and algorithm testing.
Language and Chatbots
When training virtual assistants in new languages, it can be difficult to acquire enough data. That’s where synthetic data becomes useful. At Amazon, they create their synthetic data using two methods.
The first is to identify and gather “golden utterances” that can be used as templates from which more data can be created by recombining and using variations of the available phrases. The second method uses the utterances to extrapolate syntactic and semantic patterns that can be used to build new sentences to be added to the gathered data.
Synthetic data is poised to open up many new possibilities for sharing data and training models. To learn more about this topic and its application in machine learning, deep learning, and more, check out our upcoming conference, ODSC East 2023, where you will find these sessions on synthetic data: