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LeapYear, the Most Compelling Data Privatization Company for R&D
In today’s highly complex Biopharmaceutical Research and Development Environment, Data is the common entity that drives Ideation, Simulation, Design, Synthesis, Analysis, and further Integrated Decision making. These highly complex data-driven organizations are challenged by not only the types of data (RCT, RWE, Omics) but the origin of data and the right to use this data (consent) in various scenarios, further complicated by geographical barriers or rules. For years organizations and groups have relied on data anonymization to prevent data origin or any potential personal details from being known. Simply put, this does not meet today's security criteria. LeapYear (www.LeapYear.io) has developed algorithms that not only ensure data privacy but also enable model quality data for use in machine learning.
The combination of regulatory frameworks (GDPR) and public perception makes ensuring proper use/handling of this data a strategic priority.  However, leadership must balance the governance costs (time, restricting access, duplication of effort, removing info) with the validity/usefulness/timeliness of the data.
We will discuss several high-value use cases that are enabled by implementing a new data exchange/sharing/access model based on LeapYear:
Working with sensitive data assets like RWE and Genomic data across teams and geographic boundaries.  Key outcome: enabling more teams to have more access to data, faster, and with full fidelity
Third-party data acquisition - helping teams both deal with the data they already acquire (in a more valuable, more private way) and helping them get access to more data (e.g. from healthcare providers) that may be impossible to get access to without a mechanism like differential privacy.  
Crossing lines of business - R&D teams can be broken into specialty areas, and therefore hard to share data across lines. But this data could be used to drive initiatives across lines.


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