User behavior data can be incredibly rich, but these insights are only available and actionable with personally identifiable information (PII) data around communication and engagement. Quite often, data scientists and business analysts are assigned projects that involve confidential or sensitive external data that makes them wary about the risks and consequences of how to handle the data in a safe way.

This session presents an architecture and approach for reducing the risks of customer analytics projects, enabling data scientists and analysts to proceed with confidence and produce insights while protecting their data. This session also provides the framework and context for architects involved in designing a secure architecture and the processes for projects involving regulated or sensitive data. This streamlined approach enables analytic projects to proceed more quickly and efficiently - with more confidence - while minimizing risk in a fundamental, repeatable manner.

Attendees will hear practical, real-world recommendations in five essential areas:

• Architecture overview for secure data transfer (vendor agnostic)
• Processes for private and public encryption key management, for example using the GNU Privacy Guard Linux utility
• Best practices with encrypted databases
• Options for user access controls to enable governed data blending and analytics from multiple data sources
• Critical components of operational reporting for compliance and audits