Synthetic Data enables data driven innovation in many different ways
Real data does not contain enough data or all possible conditions and events needed to train ML models. BlueGen.ai generates training data that meets every condition and event variation, making ML models much more accurate.
Generate privacy-compliant data for sharing and collaboration .
Organizations lose collaboration opportunities and innovation power because data security and privacy regulations prevent them from exchanging data with other organizations and departments. BlueGen.ai solves this security and privacy problem by generating anonymized data so organizations like government agencies can exchange valuable data and collaborate successfully.
Synthetic data covers all conditions, events, and edge values, enabling bridging seasonal and statistical gaps in datasets and proper boundary value testing. For performance testing it provides all the data you need, even when this is limited in the production data
Security and compliance risks often limit the adoption of cloud services. By moving the synthetic counterparts of some datasets to the cloud, organizations can take advantage of the wide array of services previously not allowed.
Because of the power and possibilities of synthetic data, it is quite possible that a market will arise for commercial synthetic data generation.
Prevent data losses due to data retention policies.
Data that may only be stored for a limited period can be permanently preserved by creating a synthetic snapshot.
Synthetic data can reveal statistical patterns and trends that would otherwise go unnoticed because synthetic data covers all possible conditions and events. And synthetic data helps test all relevant scenarios when evaluating trial products.