The major technology trend of the past decade has been mass migration to the cloud. The economies of scale and breadth of online services have meant that organizations of all sizes have adopted cloud services for a variety of IT functions, to such an extent that modern approaches to building and running applications are now described as “cloud native.”
But, for businesses that want to stay ahead in the data race, centralizing everything inside massive cloud data centers is becoming limiting. The arrival of 5G networks and a boom in connected devices as part of the Industrial Internet of Things (IIoT) will produce vast quantities of real-time data—all of which will need to be rapidly analyzed to inform timely business decisions. In a world of emerging technologies and powerful new analytics models, speed is as critical as accuracy—and in this world, the cloud is going to fall short.
According to Gartner [1], while only about 10 percent of enterprise-generated data is created and processed outside a traditional data center or cloud, this figure is expected to soar to 75 percent by 2025. Santhosh Rao, Gartner’s Senior Research Director, concludes organizations are therefore going to have to consider a decentralized approach: “As the volume and velocity of data increases, so too does the inefficiency of streaming all this information to a cloud or data center for processing.”
This means making a potentially game-changing shift: away from the cloud towards edge computing.
Oliver Schabenberger, Executive Vice President and Chief Technology Officer at analytics firm SAS, argues the edge should be the starting point for enterprise organizations. This is because everything generating data outside of a data center and connected to the Internet is at the edge. “That includes appliances, machines, automobiles, streetlights, smart devices at home, locomotives, pets, and healthcare equipment,” he says.
For data scientists, shifting intelligence to the point of collection opens up a new world of possibilities. For starters, it presents the opportunity to finally realize the potential of IIoT and use connected devices to collect lots of different data types and learn from it without having to sort it first. This allows data scientists to capture insights from things like wind turbines or doors or streetlights, without knowing what they are looking for.
But more immediately it raises the tantalizing prospect of three major benefits: faster response, greater scalability due to processing being distributed around the network, and cost savings by minimizing the bandwidth used. All of this adds up to being able to push new boundaries in analytics and do more, faster.
Click here to read the full article from HP.
