The Industrial Internet of Things (IIoT) is finally stepping out of the ‘Big Data’ hype where a ton of companies were collecting a ton of data without being able to leverage the majority of the data to generate definitive ROI.
With the advent of Edge Computing, companies involved in the IIoT realm are slowly but surely leveraging edge processing to ensure that the majority, if not all, of the data they collect generates actionable intelligence.
The year 2017 has proven to be the year of pilot projects and proof of concept implementations for IIoT. None of these implementations have proven to be more valuable then edge analytics.
The nature of edge computing is that it allows processing of data at the device/sensor end which eliminates the need for sending all data to the centralized cloud. This significantly improves processing speeds and effiency of the entire IIoT solution at hand. Equipped with the ability to process data near its source, IIoT solutions have started finding their way into industries which were so far only hypothetical IoT use cases.
As edge analytics sees wider adoption and signals a major shift in IIoT approaches it will likely become the key growth driver in the IIoT space. There is already evidence that this will likely be the case as some of the leading players in the IIoT space are partnering up to create ground breaking breakthroughs in the field. Perhaps the most impressive of these partnerships is Cisco and IBM coming together to bring IBM Watson capabilities to the Edge.
Since IIoT will effect all large scale industries in the world it should come as no surprise to anyone that global leaders in automotives, manufacturing, energy, and electronics are already utilizing edge analytics.
According to Grand View Research the global IIoT market is expected to reach USD 933.62 billion by 2025. While there will be several factors that will play a key role in making that market size a reality anything that improves the core IIoT solutions themselves will play a bigger role than others.
One of the big challenges with IIoT has so far been the sheer scale of implementations required. For example, while self driving cars are no longer futuristic, it is the effiency and accurary of the network that connects and feeds intelligence to these cars that will eventually determine how quickly self driving cars see wide scale adoption.
People do want their cars to drive themselves but more importantly they want their cars to drive themselves safely. Since the entire process requires feeding data collected by all cars on the network to the cloud, analysing and detailing trends like traffic, obstacle detection etc, and retrieving intelligence obtained from the analytics process to inform the vehicle as well as influence the user’s decision on how to proceed, it is imperative that the speed and quality of intelligence retrieved is top notch and ever improving.
This is why edge intelligence is superior to the current centralized cloud based processing. In the example of self driving cars edge analytics allows processing of data at its source allowing cars on the network to send and receive processed data directly. As the relevant Machine Learning solutions learn more and improve the network’s overall intelligence quotient edge processing enables their execution in real time.
In 2018 edge analytics seems poised to play a key role in IIoT’s growth.