More and more people across organizations are expected to work with data and to do so safely without breaking or leaking anything. Synthetic data generation is a solution that allows citizen data scientists and auto ML users to quickly and safely create and use business-critical data assets.
Letting go of production data is a hard sell for data scientists and engineers privileged enough to have unrestricted access to their companies' most valuable data assets. Old habits are hard to change, but that doesn't mean they shouldn't. More and more companies are creating synthetic data repositories, where curated synthetic data assets replace privacy-sensitive, messy, and biased production data access. Benefits go beyond democratizing data access, and even those with privileged data access are building synthetic data generators into their work flows.
The Future of Machine Learning Is Synthetic
For building machine learning models, synthetic data is better than real data. The best synthetic data generators, like MOSTLY AI's no-code synthetic data platform, offer high-quality, 100% GDPR-compliant synthetic data based on real data samples. And privacy is only one of the reasons why data scientists, analysts, and engineers embrace this new technology. According to analysts, 60% of data used in AI and analytics will be synthetic by 2024. And that is because the synthesization process can improve the original data in ways that are beneficial for machine learning models. From simple data augmentation to upsampling minority groups and filling out missing data points to simulating hypothetical scenarios, data synthesization is a creative process in itself.
How Does Synthetic Data Make Machine Learning Better?
Next-generation synthetic data generators are an example of how AI can help to build itself. Models trained on synthetic data perform on par or better if augmented via synthesization. Originally a privacy-enhancing technology, synthetic data generators retain correlations and distributions of the original data while generating brand-new data points that have no 1:1 relationship to the original data points. Intelligence is elevated to the population level, while sensitive information is no longer present on the data subject level. Traditional anonymization tools like data masking, aggregation, and randomization destroy the utility of the data. Machine learning models trained on masked data might miss out on granular level details invisible to the human eye.
A synthetic data generator is your best friend if you have heavily imbalanced data sets. You can easily generate new synthetic data to upsample minority class instances. You can also undersample the majority class. The result is improved machine learning performance on top of secured privacy.