In figuring out how to manage cloud analytics, how do you weight geographical location -- and managing/selecting cloud/datacenter providers accordingly? (for instance, where "edge analytics" are crucial)
Re: "Collecting everything"... How much data = too much? And with the capabilities we have today, how can enterprises scale their efforts efficiently so that it becomes more about data accessibility on demand rather than about static data collection/hoarding? (whether via IoT and/or other tools)
Q: What are the obstacles you see before we finally reach the age of the SON (self-organized network)? We have virtualization and scale-on-demand. How much further do we have to go before the network is able to predict, direct traffic, and scale accordingly all by itself without human input?
I remember that. That sparks another related question: What provenance checks do you recommend in your alert/automation systems to ensure that "fake news" doesn't cause automatic chaos -- without overbudening human analysts with too many alerts?
QUESTION: In cybersecurity, research demonstrates that the majority of security alerts get ignored. How can we leverage ML, automation, and other real-time analytics to help reduce the live-alert burden -- whether in cybersecurity or on other areas?
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