Over the last years, many customers have had to deal with cyber attacks targeted at intellectual property. We have entered a new phase in which criminals unleash destructive attacks, rendering IT environments unusable and effectively halting business. Consumerization of IT and the cloud add inherent tension and security implications to this new reality. How can one deal with data that moves in and out of your datacenters, to unmanaged devices and cloud services not directly under your control? How can you create a cost efficient architecture and plan for prevention, detection, and recovery in a time where cloud and consumerization of IT are high on every CxO's agenda? The presenters of this session have been on the Targeted Attack frontline with enterprise customers over the last years. They explain the threats of targeted attacks and share an architecture model using a data classification model, as well as their field experiences and services you can leverage to deal with prevention, detection and recovery.
Anyone who can type commands into R but that is not the same as actually 'doing' statistics for analytics. They may even misuse those methods, and it's an entirely different thing to really understand what's happening. Knowledge is what really drives each phase of your analysis, and create effective models for the business to use in order to create actionable insights. It can be difficult to see when someone is building faulty statistical models, especially when their intentions are good, and their results look pretty! Results are important, and it's down to you to create models that are sound and robust. In this session, we will look at modeling techniques in Analytics using R, using our boozy day at the Guinness factory as a backdrop to understanding why statistical learning is important for analytics today.