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Synthetic data: all you need to know

What is synthetic data? Why should you synthesize your data assets? How to generate synthetic data? What is fair synthetic data? All your questions answered.  

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.

How to start your synthetic data journey?

Gartner recommends to start your synthetic data journey with tabular data and to include synthetic data in your overall data strategy. We at MOSTLY AI can help you every step of the way!
If you would like to explore what benefits synthetic data can bring to your organization, consider the following steps:

Identify your data challenges

Assess situations in which data restrictions, privacy regulations or other governance requirements have impacted your business activities.

Define your use case

Based on this assessment, describe how access to highly representative and completely anonymous data could solve this data challenge.

Partner with experts

Get in touch with a trusted synthetic data specialist and advisor like MOSTLY AI to shape your synthetic data solution

Build your roadmap

Together with your trusted synthetic data advisors, develop the best commercial and procedural set up for the implementation of synthetic data generation in your business.
Are you ready to start your synthetic data adventures?