Synthetic Data Hub
Make data privacy a competitive advantage. Use synthetic data Hub to unlock and accelerate your data operations.
What is Synthetic Data?
Synthetic data is non-reversible, artificially created data that replicates the statistical characteristics and correlations of real-world, raw data.
Utilizing both discrete and non-discrete variables of interest, synthetic data does not contain identifiable information because it uses a statistical approach to create a brand new data set.
While it’s possible to identify an individual with anonymized data or de-identified data by inferring characteristics, cross-referencing data similarities, or reversing the data approach, Drillo’s Synthetic Data Hub is the only anonymization method that fully prevents re-identification.
Synthetic data functionality is a feature of the Synthetic Data Hub Platform. Connect with our team to see how it works.
MAXIMIZE YOUR DATA UTILITY
Because IRB approvals are no longer needed, your data can be leveraged instantly while teams save time and resources. Mediators, external IT teams, and data experts can now focus resources on other tasks rather than pulling data.
PROTECTED PATIENT PRIVACY
With the patient privacy protected, healthcare professionals are given public access to data for clinical and scientific research, operational evaluation, process refinement, and improving patient outcomes.
EMPOWER YOUR TEAMS
Encourage innovation by empowering your teams—administrators, researchers, clinicians, and operational staff—to access the patient data they need in order to implement real change in their departments and across the organization.
Is Synthetic Data always better than real data?
That really depends on what you are trying to do. Synthetic datasets are great when you want your application to work anywhere, anytime and in unknown situations. But there are times where an AI is being run from very similar camera viewpoints. For example, within the same type of environment, or when the camera position stays the same. Here it might be better just to collect real data and benefit from the biases it contains (yes, biases can be a good thing, too!). In a way, we call this process 'overfitting'.
How does Synthetic Data help with AI bias?
Another problem that many AI applications faces is "bias". Because training data is often collected from specific regions in the world, it's highly biased. This imbalance in data can cause all kinds of wrong behavior in a neural network; behavior that is hard to explain or justify because often we don't understand why AI models do what they do. No matter how well you collect your data in the real world, the distribution will always be biased on some level.