Dedomena was built for data driven companies

Dedomena Synthetic Data Generation tool is able to replicate the statistical, informational and predictive components of real world data without containing any identifiable information, ensuring business value without compromising customer's privacy.

High-quality

Compliant

Seamless integration

Use case flexibility

High-quality data

Besides preserving the statistical properties of the original data, our methods preserve the data quality and structure, ensuring high-quality data for purposes such as training ML models.

Compliant

Synthetic data is compliant with the most strictest data protection laws. Individual´s privacy and protection against re-identification attacks are guaranteed through mathematical methods.

Seamless integration

Seamlessly integrate synthetic data into your processes and environments. We support your company’s cloud and on-premises infrastructure such as AWS, GCP, Azure, MS SQL, Oracle or PostgreSQL, amongst others.

Use case flexibility

Generate structured and no structured synthetic data on-demand through a user interface or rest-API. Synthesize entire databases or subsets of your original data.

How does it work?

Dedomena is the platform that helps companies develop scalable AI solutions by putting data at the heart of their strategy

1

Select data to protect

We provide an user interface and/or API for companies to easily create synthetic data projects and integrate synthetic data into existing data pipelines and processes.

2

Configure your synthesization job

Our platform analyzes your data and recommends the optimal run configuration. Optionally, you can replace column names, data types as well as other dataset and run configurations, allowing you to generate clean and useful data.

3

Train the model that generates synthetic data

Our algorithm learns your data's patterns, statistical distributions, correlations, and time dependencies. The resulting model will then be used to generate synthetic copies of your data.

4

Work with confidence, data quality assured.

Now synthetic data is generated and ready to use. Additionally, Dedomena generates a QA report evaluating the utility and privacy of the newly generated data.

Benefits

Generating data that looks real sounds like a fantastic playground for your business.

Be faster

Reducing time-to-data and time-to-market from months to days. Up to 50X shorter time-to-data.

Improve customer understanding

Accessing fully anonymous synthetic behavioural data. 90% more data for your customer data analytics projects.

Increase ML accuracy

Work with larger volumes of synthetic data that retains structure, patterns and value. Improve ML performance by 20-40%.

Eliminate privacy risks

Minimizing the need of processing real customer data. $3.5 million is the average cost to remediate a data breach.

Reduce costs

Say goodbye to data compliance bureaucracy and endlerr processes. Reduce data provisionning costs by 75%.

Boost collaboration

Share synthetic versions of your customer data. Reduce up to 80% on data delivery time and costs

Uses cases

Developing successful data-centric initiatives requires access to large amounts of high-quality and secure data.

Improved Machine Learning

In AI and ML development, synthetic data is better than real data. Synthetic data can also be augmented and create records to fix biases.

Data Monetization

Take your data monetization strategy further by selling packages of synthetic data to third parties.

Data Retention

Even though the original data is no longer in the custody of the entity, there is no limit on how long or for what purpose the synthetically generated data can be used.

Testing & Development

Synthetic data empower engineers to create and test software applications in shorter development cycles, making products come to life before launching.

Vendor evaluation and Hackathons

Oursource innovation, design, development and testing of data-intensive applications eliminating the lag in the process.

Data Sharing

Synthetic data function as production data but anonymous, so that it can be used and shared with partners and providers for PoCs, software testing and advanced analytics projects.

Related articles

Check out some of our explanatory articles or cross-industry
use cases to know more

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