Brad Shimmin, an analyst with Current Analysis, said it's extremely important to be able to trust the data and the analytics the crew chief is seeing. That makes it important for IBM to reliably fill in any blanks that occur.
"Given some of the technologies IBM has been building, like Watson for predictive analytics, I can see them being able to really understand the data set," he added. "They can look at the data and be able to derive and predict [any missing] data. They can say, 'This is what we should be seeing.' Think about how invaluable it would be to have real-time streaming data off of these systems to apply predictive analytic algorithms to counteract potential problems with the network or the uplink."
That, according to Shimmin, will be the big proof point for this system.
"I don't think it tests the cloud itself," said Shimmin. "What it will point out is how difficult it is to have the scalability and the throughputs, no latency, no jitteryness, no lag for highly mobile sources of information. It will show us how difficult it is to pull this off the network that sits between the Internet of Things and the cloud itself."
That's important to companies that want to stream data from jet engines in flight, from ambulance companies and from shipping companies.
"It points out the difficulties of moving large amounts of analytical data from point of origin to point of insight," he added. "There's so much data to get to the cloud."
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