An ongoing challenge for organizations of all sizes has centered around how to accelerate analytics projects and extract analytical value without compromising privacy. Data scientists struggle to get access to sensitive data for analytics rapidly, and using a one-size-fits-all de-identification technique leaves them with data that is insufficient for complex use cases.

The Big Data paradigm assumes that more is better: more data, collected from more sources means that we can make better predictions. This holds true, but leaves out the imperative that organizations have to protect the privacy of the consumers in the data. Technology has created many ways to collect and analyze information, and it holds the key to protecting the data that is the lifeblood of innovation.