Synthetic Data to Unlock Your Privacy-Sensitive Big Data Assets
Even the most sophisticated anonymization methods on the market fall short in the presence of big data, as they can only retain a small fraction of information. This calls for a fundamentally new approach!
Based on Mostly AI's Synthetic Data Engine, Mostly GENERATE allows you to simulate highly realistic & representative synthetic data at scale, by automatically learning patterns, structures and variations from your existing data. The software-solution leverages state-of-the-art generative deep neural networks with in-built privacy mechanism to build a mathematical model of your customers and their actions.
This model retains all the valuable statistical information while rendering the re-identification of any individual impossible. By drawing randomly from the model, a synthetic population of arbitrary size can be generated at any later point. This way you will get as-good-as-real, yet fully anonymous data at granular level, that can be freely processed, analyzed and shared further.
✩ is an easy-to-integrate software solution,
✩ runs on-premise or private cloud,
✩ scales to millions of customers, and
✩ retains unprecedented detail & accuracy!
See It in Action
Watch the creation of 2'000 realistic, yet synthetic baseball players (in fast-forward). Actual player records are provided with their year of birth, weight, height and 8 more attributes.
How It Works
The process consists of three basic steps:
1. The engine analyzes and preprocesses the existing data, that is provisioned.
2. The engine fits a high-capacity deep neural network architecture and persists it.
3. The engine utilizes the model to generate highly realistic synthetic data.