Everyone is hungry for data, but most organizations rigorously control who can see what data; preventing free, self-service access, keeping it safely locked behind ironclad barriers. The risks associated with exposing sensitive client and customer data to unauthorised parties demands this control. In the past, security constraints were driven by the threat of regulatory fines and brand destruction, but today there is even more at stake as company directors are ultimately held responsible, even risking jail time.
Not surprisingly, this fuels into the development of a strong risk-averse culture where it takes weeks, or even months to secure data approvals, leaving ideas unrealised and innovation delayed. Competitive advantage and new revenues that are sacrificed at the cost of mitigating possible risks. Operational agility demands ready access to sensitive data for blue sky innovation and rapid experimentation. So why is it constantly thwarted with teams sitting idle lacking access to sensitive data?
Complex approval processes and rigid data sharing policies exist due to a myriad of regulatory controls like GDPR and HIPPA. Further data movement constraints are found in nearly every country, all different in their restrictive covenants and driven by different agendas. Compound this with the ever present threat of data breaches and data leaks, and we have sufficient reason for ever stronger defensive positioning when it comes to accessing and collaborating with sensitive data.
There’s also the problem with data itself, usually disappointing in quality and shape, and often lacking in sufficient volume.
It’s well understood that the more data you feed a model the more accurate it becomes, but how do you solve this extreme data drought given the risks and restrictions that exist?
The team at Synthesized has a deep understanding of all of these data challenges. It’s why we created the Synthesized DataOps Platform - to solve the problem of sharing and collaborating with sensitive data. Synthesized leverages sophisticated AI power to intelligently create artificial replicas of original data as measured across two dimensions: utility and privacy.
And we believe we do this better than anyone else.
Our Machine Learning experts have perfected how Synthesized creates accurate, new data points using a sophisticated AI model built from a deep understanding and analysis of the original data. The performance of new Synthesized datasets can be proven using a range of common and advanced statistical metrics to demonstrate its utility closely matches the original data. We can use Machine Learning models to further demonstrate the accuracy and performance of Synthesized data compared to the original.
Data masking and data anonymisation are insufficient means of ensuring data privacy, as they can be easily attacked by modern techniques and they drastically reduce data quality. Unlike these poor techniques, our unique Synthesized approach learns the complex statistical relationships in the data to automatically Synthesize new intelligent samples at any volume.
Synthesized data points are entirely new and did not previously exist, but fully preserve the quality and performance of the original set. The same cannot be said of other approaches.
In addition to preserving data quality, we have tested our approach using a variety of attacks, including statistical linkage attacks, and with many different datasets to ensure privacy is fully preserved and no confidentiality is broken.
Data access and sharing has never been easier, faster, or more secure. Synthesized Data Clean Rooms are secure, isolated environments created by the platform to streamline data collaboration with Synthesized data between internal teams and with external parties. They are tightly integrated with your enterprise logging and monitoring tools, providing a full audit of all data access and data movement.
