Why is synthetic data
essential for your
data-driven projects ?
With synthetic data, you can reduce the time needed to get reliable and safe test data, which can be used to fuel innovation. Therefore, improve your time-to-market and reduce development costs.
Synthetic data mimics the structure and properties of real data, but is created from scratch. No link can be made with foundation data, ensuring compliance with GDPR and reducing the risk of data breaches.
By 2030, synthetic data will overshadow real data
according to Gartner
Data is one of the most valuable resources for companies. Getting the right data is one of the most important but challenging part of data projects.
Synthetic data is making the creation of reliable, fast and safe data more accessible. By democratising access to data, it empowers a new generation of AI innovation and opens new industry possibilities.
Tackle the challenges of test data management
Up to 45% of development/testing time is spent waiting for data or generating it by hand : over 70% of test data is still created manually.
Up to 85% of data is still profiled manually
Over 55% of companies are not fully compliant with data privacy policies due to access to production data by non- production teams.
Synthetic data addresses these issues. It makes the generation of realistic and safe test data easy. It accelerates data-driven projects and ensures full compliance with policies such as GDPR.
Tailor your test data to
your needs
As reliable as real data, synthetic data can be defined, made and shared in minutes. It could be the only solution when real data is rare or even not existing for new use cases.
CloudTDMS allows the creation of big data based on a small data sample by understanding its structure and paterns.
Safe, anonymous data
avoid data breaches
Synthetic data is the only data you can safely share. No link can be made between synthetic and real data.
Using synthetic data reduces the risk of data breaches and is fully compliant with GDPR.
The power of data, accessible to regulated sectors
In sectors such as health, telecom, banking or insurance, a large amount of sensitive data is involved. To avoid data breaches and GDPR violation, it cannot be used for testing without proper processing.
Legacy anonymisation techniques such as masking are a way of creating usable test data. But this solution takes time and it is hard to ensure that no personal information can be obtained from it.
Synthetic data is the best alternative, enabling easier and safer testing in critical sectors and unlocking new innovation possibilities.
