What is synthetic data?
Synthetic data is smarter than mock data and better than real data. It's like modelling clay for AI training, allowing you to upsample rare categories and improve the performance of models. It's also like a tap for high quality test data, providing on-demand, production-like data for testing teams. You can also use it to share customer data in a privacy safe way across and outside your organization.
How to generate synthetic data?
You need to remember a few basic principles when prepping your data for synthesization. To protect your data subjects' privacy, you need to set up a subject table containing user IDs. Your time series data needs to be in another table, clearly referring to the subject table. For best results, follow our guide for synthetic data generation and learn the best tips and tricks by getting hands-on with MOSTLY AI's state-of-the-art synthetic data generator.
Why synthetic data?
You can create, use, share and discard synthetic data at will. It is as good as production data and capable of improving data quality. AI-generated synthetic data is an advanced privacy enhancing technology (PET), ready to unlock data value across a a huge range of use cases.
The synthetic data guide
Our definitive synthetic data guide includes everything you need to know about AI-generated synthetic data with real-life case studies. Understand why synthetic data is truly anonymous and why it's a revolutionary data management tool.
The Synthetic Data Dictionary
Synthetic data is an emerging privacy-enhancing technology. It's a new field with new tools and terms coming from data science, machine learning development and related fields. The Synthetic Data Dictionary collects the most important terms and definitions that can be important when you work with synthetic data generation.
Fair ethical AI
Historical data is like a mirror of the world we live in. This means that it's full of bias, discrimination, and injustice. AI systems trained on raw historical data will pick these bad patterns up and amplify them at scale. MOSTLY AI's team has been at the forefront of fairness research since day one. We've been working on methods to remove these embedded biases via synthetization. Fair synthetic data generation introduces constraints based on a mathematical definition of fairness.