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External data sharing

Data sharing is getting increasingly difficult. Synthetic data sharing is not. Synthetic data sandboxes are the perfect solution for testing, POCs and cross-border AI and analytics projects.

Data sharing challenges

Data is getting increasingly difficult to work with. Enterprise data sharing has long been a difficult process. Endless bureaucracy and suboptimal data outcomes make the lives of engineers and data scientists difficult. Vendors and start-ups are asking for your data to work with. Research partners want access as well. Off-shore development teams rely heavily on data sharing for testing applications. For example, analytics projects need to span several organizations and countries. Cross border data sharing is getting increasingly difficult all over the world. With an increase in data privacy legislations and rulings, like Schrems II effectively prohibiting US-EU data sharing, such projects turn out to be impossible to pull off. An increasingly hostile cybersecurity environment further inhibits free data flows making organizations more reluctant than ever to share data.

The status quo in data sharing

Organizations, especially those handling troves of sensitive data, like financial institutions, banks and insurance companies have two options: either to not share data externally at all, or to heavily rely on legacy data anonymization approaches. These approaches are known for their poor privacy protection and often poor data utility as well. Even worse, less mature organizations take unacceptable levels of risk by relying on simple forms of de-identification or sharing production data.

Synthetic data for external data sharing

McKinsey estimates that privacy-safe data sharing could generate almost $3 trillion annual economic value. And synthetic data generators are the technology to make this a reality.

AI-generated synthetic data is modeled on original data. Synthetic datasets or databases function as anonymous, yet meaningful drop-in placements for production data. Synthetic data does not qualify as personal data. As a result, it is out of scope of privacy laws, like GDPR. What’s more, high quality synthetic data is statistically (almost) identical to the original dataset or database it was modeled on. As a result, synthetic data can be used for a variety of use cases such as application testing, data intensive POCs, cross-border analytics and AI/ML projects or to share with researchers and regulators. Synthetic data sandboxes are great data sharing tools, tried and tested in highly regulated environments from banking to insurance and healthcare. 

Case studies and guides

Ready to try synthetic data?

The best way to learn about synthetic data is to experiment with synthetic data generation. Try it for free or get in touch with our sales team for a demo.
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