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.
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.
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.
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.
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.
Dedomena is the platform that helps companies develop scalable AI solutions by putting data at the heart of their strategy
1
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
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
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
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.
Generating data that looks real sounds like a fantastic playground for your business.
Reducing time-to-data and time-to-market from months to days. Up to 50X shorter time-to-data.
Accessing fully anonymous synthetic behavioural data. 90% more data for your customer data analytics projects.
Work with larger volumes of synthetic data that retains structure, patterns and value. Improve ML performance by 20-40%.
Minimizing the need of processing real customer data. $3.5 million is the average cost to remediate a data breach.
Say goodbye to data compliance bureaucracy and endlerr processes. Reduce data provisionning costs by 75%.
Share synthetic versions of your customer data. Reduce up to 80% on data delivery time and costs
Embracing Innovation: What You Need to Know About Synthetic Data Trends in 2025
Developing successful data-centric initiatives requires access to large amounts of high-quality and secure data.
In AI and ML development, synthetic data is better than real data. Synthetic data can also be augmented and create records to fix biases.
Take your data monetization strategy further by selling packages of synthetic data to third parties.
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.
Synthetic data empower engineers to create and test software applications in shorter development cycles, making products come to life before launching.
Oursource innovation, design, development and testing of data-intensive applications eliminating the lag in the process.
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.
Check out some of our explanatory articles or
cross-industry
use cases to know more
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