Regulatory, confidentiality, and security requirements result in data becoming increasingly restricted. Geographic and business line silos limit the effectiveness and efficiency of analytics organizations and preclude new business opportunities.
Prior to LeapYear, regulated institutions faced a number of challenges:
- Data from across geographies could not be analyzed, creating inefficiency and limiting the use of data for functions such as marketing, R&D, and compliance.
- Data scientists waited weeks or even months to obtain access to critical datasets
- Information could not be accessed or combined across business lines, which prevented the development of valuable products and revenue opportunities
LeapYear implements privacy-preserving reporting, analytics and machine learning enabling organizations to compute across data silos.
- Data never moves – Whether it’s a business unit that does not want information to leave its infrastructure, or a country with strict data residency laws, LeapYear ensures the data remain in place. However, insights from the data such as aggregates, statistics and models can be computed across organizational and regulatory boundaries.
- Mathematically proven privacy – It is a well-known fact that any information produced from a dataset, even if it’s aggregated and anonymized, can be used to reconstruct the underlying data. LeapYear provides mathematically proven privacy ensuring not only that the data will remain in place but that even derived insights cannot be used to compromise the data.
- Enterprise scale – LeapYear is built to drive efficiency for enterprises with complex and disparate infrastructure. The system has been deployed to overcome data silos across countries with the strictest privacy and secrecy laws, and in petabyte-scale IT environments ranging from air-gapped on-premises facilities to the public cloud
A top-10 bank has multiple business lines serving both retail and institutional clients. The data from these clients are highly valuable for its internal research function which provides reports to clients on financial markets. Prior to using LeapYear, the bank would either make client data entirely unavailable because of the privacy risk, or aggregate and anonymize the data before they were shared with the research group. This approach resulted in a loss of value — without access to certain datasets researchers could not pursue valuable reports for their clients, and anonymized or aggregated datasets did not have the granularity needed for many of their use cases. Moreover, the bank found that even this limited version of the dataset could be used to exploit sensitive information.
With LeapYear, the global bank enables its research group to generate new insights that would not have been possible given the limitations of anonymized and aggregated data. Finally, to ensure understanding, LeapYear worked with the bank to demonstrate to clients how the platform maintains the privacy of their most sensitive information through rigorous mathematically proven techniques.
Major Pharmaceutical Company
A global pharmaceutical company has operations across the US, Europe, and Asia. They are impacted by regulations, including GDPR and strict data residency laws from several countries. These restrictions limit their use of data science across both commercial and research & development functions. The pharmaceutical company has a central data science team — without LeapYear, this group would need weeks or months to obtain access to valuable data and certain cross-border datasets would be entirely inaccessible. With LeapYear, the pharmaceutical company was able to preserve data residency while still providing value for data science. This is because LeapYear demonstrated that the data science team could generate analytics and models without having access to the data itself. This streamlined investigations and meant teams would go through multi-week/month-long processes only when access to the underlying data was absolutely necessary. LeapYear enhanced both process efficiency and data security for the pharmaceutical company, and even created the opportunity for previously unattainable data science initiatives.