IoT analytics guide

#News(IoTStack) [ via IoTForIndiaGroup ]

Analytics for IoT has some unique requirements compared with analytics for other kinds of data. This includes data format, data richness, time sensitivity, where the data is stored and how long it is stored.

“The key analytics need is to close the gap between data generation in the physical world and the need for action either in the physical or digital world,” Hopkins says. “This inevitably means pushing some analytics logic to the edge—out of the cloud or data center. The problem is that servers and devices have far less compute power.” Some have battery or power limitations, and far less storage, than the analytics require, Hopkins says. Therefore, the analytics needs to be distributed. “Some things happen on devices, others on edge servers and gateways, others in central processing environments,” he says.

Building out real-time data capture, data governance, and availability of services are among the biggest challenges IT will face in creating an IoT analytics environment, Hopkins says

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