IoT analytics guide: What to expect from Internet of Things data | Virtual Reality

The growth of the of Things () is having a big impact on lots of areas within enterprise IT, and is one of them.

Companies are gathering huge volumes of information from all kinds of connected of objects, such as data about how consumers are using certain products, the performance of corporate assets, and the environmental conditions in which systems operate. By applying advanced analytics to these incoming streams of data, organizations can gain new insights that can help them make more informed decisions about which actions to take. And with companies placing IoT sensors on more and more objects, the volumes of incoming data will continue to grow.

“Sensor-based computing is a core trend in digital transformation,” says Maureen Fleming, an analyst at research firm IDC. “Operational intelligence using condition-based monitoring assures organizations about the health of sensor-attached devices, machines and systems. Depending on the use case, applying machine learning [ML] to sensor data is aimed at predicting probability of outages, propensity to buy, health problems, etc.”

Applying ML to sensor data in combination with data from enterprise applications can also fundamentally change how an organization works, by predicting problems with meeting service-level agreements on services for customers or logistics problems within a supply chain, Fleming says.

IoT “is driving the blending of the digital and physical worlds,” says Brian Hopkins, vice president and principal analyst at Forrester Research. “Almost all businesses want real-time data from the physical world to take the next step in their quest for insights that deliver competitive advantage.”

Forrester sees three primary scenarios for gaining insight through analytics. One is insight about the smart connected products themselves. Another is insight about how connected things work efficiently together, which can help companies improve processes that involve physical assets. And the third is insight about things and people that come from the IoT data of business partners such as suppliers.

IoT necessitates new infrastructure

For many enterprises, the existing data analytics infrastructure will not adequately handle the expected increases in volume generated by the IoT, however. They will need to alter their IT environments to make them more “IoT-ready.”

“IoT is creating an unprecedented amount of data in the enterprise in terms of both volume and velocity,” says Mark Hung, research vice president at research firm Gartner. “In order to extract value out of this data, the enterprise’s data analytics architecture needs to be revamped.”

For enterprises to act on IoT data in a timely manner, streaming or real-time analytics is often required, Hung says. The need to incorporate new analytics methods such as streaming analytics and new infrastructure such as edge gateways places new architectural requirements on the existing IT infrastructure, he says.

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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