FAQ . .. ...

BlueGen.ai Synthetic Data FAQ

General.

When our self-learning system has trained itself to create synthetic data, it only needs to run periodically to keep up with the changing characteristics of your real data. To save time, you can do this incrementally and over different time intervals, depending on your use case.
Deep learning uses artificial neural networks to discover patterns in large data sets to make predictions. We train our neural networks specifically to create high-quality synthetic data. Similar to the ChatGPT neural network trained to understand and write text.
BlueGen.ai uses generative deep learning technologies in an autonomous system that generates high-quality synthetic data. Because BlueGen.ai is based on federated learning principles, your real data is safe from the outside world, as our solution only uses the structure and characteristics of your real data to generate the synthetic data.
1. Connect the solution to your existing data set. 2. Configure the requirements for the synthetic data. 3. Review the quality of your newly created data. 4. Generate your synthetic data on demand.
An intrinsic property of synthetic data is that personally identifiable information isn’t inherited from the real dataset. So specific individual records from the real world aren’t available.
Synthetic data is artificially generated data that looks and behaves exactly like real data.
The most common use cases for synthetic data are sharing privacy-safe data, training machine learning models, developing & testing new software, and analyzing big data sets. With assured privacy and integrity, full control over bias, and the ability to create more and scenario-specific data.
Synthetic data overcome your real data’s privacy, security, and utility constraints and enable faster, cheaper, and broader access to statistical insights.
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Data Quality.

BlueGen.ai guarantees the privacy of synthetic data by: Preventing linkage attacks by creating new data instead of (pseudo)anonymizing existing data. Running its own privacy attacks and nearest neighbor analysis on the synthetic data. Applying differential privacy during the training process of our system.
You compare the standard evaluation of the synthetic data with your real data to assess the quality. If the synthetic and the real data give the same forecasting, classification, and regression analysis results, the quality is right for your use case.
When our system has learned to generate your synthetic data, it automatically creates a comprehensible evaluation report with graphs and performance indicators on standard statistics such as distributions, percentiles, distances, correlation, precision, and recall.
Data quality is a multi-dimensional measure that includes (but isn’t limited to) relevance, diversity, consistency, accuracy, robustness, utility and privacy. The weighting between the individual aspects depends on the use case for the synthetic data.

Technical.

Part of the solution is always where the real data set resides, but where you generate the synthetic data is up to you: in the cloud, on-premise, or combined.
Yes, we offer several APIs to integrate with your IT environment.

Once the system is trained, BlueGen.ai can generate thousands of synthetic data rows per second.

Depending on the available computing power and amount of real data, the training process can take from an hour up to a day in the most complex cases.
No, there isn’t a fixed maximum amount of processable real data. However, the training time will increase with larger and more complex data sets.
BlueGen.ai needs at least a thousand rows of data to train itself properly. And the more columns there are in a dataset, the more rows are required to learn all its statistics, correlations, and relationships.
Yes, if the data in your data lake is structured so that BlueGen.ai can do its work efficiently.
BlueGen.ai supports all kinds of single table and time series data, such as in database systems like SQL, Oracle, and MongoDB.

General.

When our self-learning system has trained itself to create synthetic data, it only needs to run periodically to keep up with the changing characteristics of your real data. To save time, you can do this incrementally and over different time intervals, depending on your use case.
Deep learning uses artificial neural networks to discover patterns in large data sets to make predictions. We train our neural networks specifically to create high-quality synthetic data. Similar to the ChatGPT neural network trained to understand and write text.
BlueGen.ai uses generative deep learning technologies in an autonomous system that generates high-quality synthetic data. Because BlueGen.ai is based on federated learning principles, your real data is safe from the outside world, as our solution only uses the structure and characteristics of your real data to generate the synthetic data.
1. Connect the solution to your existing data set. 2. Configure the requirements for the synthetic data. 3. Review the quality of your newly created data. 4. Generate your synthetic data on demand.
An intrinsic property of synthetic data is that personally identifiable information isn’t inherited from the real dataset. So specific individual records from the real world aren’t available.
Synthetic data is artificially generated data that looks and behaves exactly like real data.
The most common use cases for synthetic data are sharing privacy-safe data, training machine learning models, developing & testing new software, and analyzing big data sets. With assured privacy and integrity, full control over bias, and the ability to create more and scenario-specific data.
Synthetic data overcome your real data’s privacy, security, and utility constraints and enable faster, cheaper, and broader access to statistical insights.
Load more

Data Quality.

BlueGen.ai guarantees the privacy of synthetic data by: Preventing linkage attacks by creating new data instead of (pseudo)anonymizing existing data. Running its own privacy attacks and nearest neighbor analysis on the synthetic data. Applying differential privacy during the training process of our system.
You compare the standard evaluation of the synthetic data with your real data to assess the quality. If the synthetic and the real data give the same forecasting, classification, and regression analysis results, the quality is right for your use case.
When our system has learned to generate your synthetic data, it automatically creates a comprehensible evaluation report with graphs and performance indicators on standard statistics such as distributions, percentiles, distances, correlation, precision, and recall.
Data quality is a multi-dimensional measure that includes (but isn’t limited to) relevance, diversity, consistency, accuracy, robustness, utility and privacy. The weighting between the individual aspects depends on the use case for the synthetic data.

Technical.

Part of the solution is always where the real data set resides, but where you generate the synthetic data is up to you: in the cloud, on-premise, or combined.
Yes, we offer several APIs to integrate with your IT environment.

Once the system is trained, BlueGen.ai can generate thousands of synthetic data rows per second.

Depending on the available computing power and amount of real data, the training process can take from an hour up to a day in the most complex cases.
No, there isn’t a fixed maximum amount of processable real data. However, the training time will increase with larger and more complex data sets.
BlueGen.ai needs at least a thousand rows of data to train itself properly. And the more columns there are in a dataset, the more rows are required to learn all its statistics, correlations, and relationships.
Yes, if the data in your data lake is structured so that BlueGen.ai can do its work efficiently.
BlueGen.ai supports all kinds of single table and time series data, such as in database systems like SQL, Oracle, and MongoDB.

General.

BlueGen.ai needs at least a thousand rows of data to train itself properly. And the more columns there are in a dataset, the more rows are required to learn all its statistics, correlations, and relationships.

BlueGen.ai needs at least a thousand rows of data to train itself properly. And the more columns there are in a dataset, the more rows are required to learn all its statistics, correlations, and relationships.

Data Quality.

BlueGen.ai needs at least a thousand rows of data to train itself properly. And the more columns there are in a dataset, the more rows are required to learn all its statistics, correlations, and relationships.

BlueGen.ai needs at least a thousand rows of data to train itself properly. And the more columns there are in a dataset, the more rows are required to learn all its statistics, correlations, and relationships.

Technical.

BlueGen.ai needs at least a thousand rows of data to train itself properly. And the more columns there are in a dataset, the more rows are required to learn all its statistics, correlations, and relationships.

BlueGen.ai needs at least a thousand rows of data to train itself properly. And the more columns there are in a dataset, the more rows are required to learn all its statistics, correlations, and relationships.