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
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.
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.
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.
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
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
use cases to know more
Synthetic Data can help insurance companies to improve their underwritting, claims management and customer experience while unlocking the potential of data that have been hidden by data privacy regulations.
5 min read
Companies now have access to more employee data than ever before, and, as data collection becomes easier, it is more important than ever for HR to take position on ethical data use, privacy and security.
4 min read
Synthetic data has received considerable attention as a method of protecting patient privacy, diversifying datasets and augmenting clinical research, hence accelerating the application of Artificial Intelligence across different hospitals and institutions.
3 min read
Data anonymization is key to applying the best data-driven strategies in the banking industry. The correct implementation of this strategy based on synthetic data will allow extracting all the knowledge and insights from customer financial data.
5 min read
The banking industry faces a wide variety of challenges when it comes to anonymization and data privacy. Thanks to synthetic data, banks can tackle data challenges more effectively and unlock the full potential of their data assets.
3 min read
The techniques that have always been used to anonymize data do not have the guarantee of ensuring the total anonymity of the data. These are techniques that have flaws when it comes to ensuring data privacy and utility.
4 min read
Without any doubt, synthetic data is one of the key technologies in the present and future of data science and artificial intelligence, but what is synthetic data? How do they work? And why is it key for data-driven companies?
3 min read