Data collaboration is the process of combining datasets together to generate new value from data-driven insights. The datasets being combined can come from different organizations, or they can come from data silos internal to an organization.

A number of use cases are possible through data collaboration: fraud detection, advances in healthcare research, real-world data, cross-selling, churn analysis, etc. However, there are significant blockers in realizing the potential benefits of data collaboration. Some of these blockers are so severe that they can stymie potentially valuable collaborations. The blockers originate from a host of areas — fear of loss of IP (intellectual property), privacy regulations, data residency restrictions, and reputational risk (just to name a few).

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