A glimpse of the future
Chou explains: “The most technology we have built so far was for the Internet of People (IoP). Whether it was an e-commerce, ERP or search program, it was built to serve people – and to accumulate specific types of data that we could analyze later. But people are not things. Things produce large amounts of data directly and can give us information when we ask for it. So why should we think that the technology we built for people’s internet would work for the Internet of Things? “
the future of IoT provides an opportunity to connect all kinds of different devices, collect many different types of data and learn from it without having to sort it all first. In the future of IT, we may learn from things like wind turbines, scissor lifts or blood analyzers before we even know what we are looking for or trying to achieve.
Data on the edge – the starting point
There is a continuum of points in IoT where data can be generated, collected, aggregated, analyzed and stored. While these points vary with each situation, the “edge” is where it all starts.
“Everything that generates data outside a data center and is connected to the Internet is on the edge,” explains Oliver Schabenberger, Group Executive Vice President and Technology Manager at SAS. “It includes appliances, machines, cars, streetlights, smart home appliances, turbines, locomotives, pets and healthcare equipment.”
A certain amount of intelligence and computing power can be placed in edge devices with today’s technology. But data cannot yet be fully analyzed because most edge devices do not have enough computing and storage resources to perform machine learning and advanced edge analysis. As a result, many IoT applications observe data at the edge and then move them to the cloud for analysis.
In the future of IT, Kumar Balasubramanian, CEO of Internet of Things Solutions at Intel, says, “Industries that stand to gain the most are those who are able to extract the right business insight at the right time and place – or cloud – based on factors such as cost and latency of the underlying business problem. “
So how do you decide what’s driving at the edge versus what’s driving at the cloud? You have to decide based on the situation.
Here is an example. If a smart car senses a driver is having a stroke, you can’t wait for the data to go to the cloud to be analyzed, then wait for a signal to come back to the edge unit to correct the right action . The cloud is too far away to process the data and respond in a timely manner. As Schabenberger puts it, data has an expiration date – you can’t afford to devalue the data until you apply analytics.
There are other problems with sending raw data to the cloud over the Internet. Think about personal information, security and legal consequences. Anyone planning the future of IoT needs to weigh those considerations.