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To the Edge of the IoT and Beyond

As organizations overcome challenges involving speed and technology challenges, they stand to reap rich rewards from the Internet of Things.
By Neeraj Chadha
Mar 06, 2017

The Internet of Things universally connects people, mobile devices and machines via networks. The implications of this sea change for business, government and all of society is only just beginning to show. IDC predicts that the IoT's installed base will be roughly 212 billion machines and devices worldwide by the end of 2020. This includes 30.1 billion installed connected (autonomous) things.

Organizations were the generators of the majority of their data before the IoT came along. This included files, presentations, spreadsheets and databases. They sent this self-generated data to a centralized repository, then stored it until they could analyze it. During the data warehouse era, organizations ran their operations and made decisions based only on this warehoused data.

Gaining intelligence from this centralized data could take days, weeks or even months. It could take even longer to make decisions based on these findings. In the age of the IoT, that's not fast enough, and it leaves out too many additional sources of valuable information.

There has been an explosion of unstructured data—big data—during the last half decade, It's coming from Google, Amazon, Facebook and Twitter. It also comes from mobile devices like smartphones or tablets, and from machines such as smart oil wells. The IoT generates massive amounts of big data every instant about how people are living, working and purchasing, and how machines and networks are operating. These days, organizations need critical unstructured data in addition to data they create. Based on this data, they can ask questions they weren't even capable of posing before, let alone answering.

Taking Data to the Edge
A completely new method of detecting patterns is now possible because of the IoT. It is called edge or "fog" computing. Fog computing takes place right where people are using mobile devices, and right where sensors are tracking and reporting performance and condition within industrial systems.

An example of an edge activity captured in the big-data torrent would be a sensor in an oil rig that checks for damage to critical valves. The sensor's signal can be tracked and analyzed using fog computing.

The caveat with big data is that it has a short half-life. Even if it takes only hours to get to a data center before it is analyzed, big-data risks becoming obsolete. If the rig's sensor reports a sudden change in pressure, the valve might fail before the rig operator knows there's a problem.

This is why it's necessary to analyze big data at the network edge—right where people, devices and machines are generating it, and right when a decision based on the intelligence from that data can make a difference. Storing data in a warehouse and waiting on decisions doesn't cut it anymore.

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