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 ithout containing any identifiable information, ensuring business value without compromising customer's privacy.

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.

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.

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.

HOW DOES IT WORK?

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

Step 1
Connect to your data sources

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.

Step 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.

Step 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.

Step 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

USE 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

What is Synthetic Data and Why is it so important?

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?

The Benefits of Synthetic Data

The applications and benefits that synthetic data can bring in the development of data-centric strategies for companies are huge and growing in relevance for data science teams. .

Anonymization & Data Sharing

Throughout this article, we will work with a Kaggle dataset, making a brief demonstration of how synthetic data is generated and how it preserves the statistical properties of the original data.

Why do traditional data anonymization methods no longer work?

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.

Synthetic Data use cases in Financial Industry

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.

The Value of Synthetic Data in Financial Services

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.