Unlocking the power of synthetic data

Synthetic data is becoming increasingly prevalent in the world of data science. It is artificially generated data that does not match any individual record. While resembling real data, synthetic data ensures both business value while being compliant with privacy and GDPR regulations. This has made it a perfect solution for safely sharing privacy data, fostering innovation, and collaboration while reducing the risk of profile re-identification. Synthetic data has proven to be a powerful tool in cases where the collection of data is expensive and time-consuming, like fraud detection or insurance claims use cases, or at the start of new APPs, which will take some time to generate enough data to use in training Machine Learning models. It is also useful in cases where there are clear class imbalances, such as in bias and fairness challenges, like those found in credit risk scoring use cases.?

A typical machine learning project faces three major challenges: the lack of real-world data, the quality of the data, and the inability to release the data to train machine learning algorithms, without compromising on GDPR. Sharing sensitive datasets can take a significant amount of time, both externally and within the same organization. Unlike sensitive datasets, properly anonymized synthetic data does not require lengthy access requests, thus improving time to value.?Also, generating synthetic data that reflects core statistical properties of underlying real-world behavior is much cheaper compared to collecting or labeling large data sets. Hence, using synthetic data can enhance the quality of the data. It is the ability to have control over the output that gives synthetic data its potential to produce a more balanced, clean, and useful dataset for training machine learning models. If you lack adequate data, it is too expensive, or there is no consent to use it in a machine learning project, generating synthetic data can fill this gap. As such, Gartner has claimed that "by 2024, 60% of the data used for the development of AI and analytics projects will be synthetically generated." Additionally, Gartner has put Synthetic Data on the "Impact Radar for Edge AI," making it one of the top three high-profile technologies.

S4 Digital is setting itself apart from the competition by partnering with major global technology players in this emerging field. As a result, we are currently developing a comprehensive Data Science framework for a major app set to launch in 2023, which will enable our customers to leverage all data collected from day one. For more information, please reach out to?[email protected]