IQ of Things: Making processes smarter
To tap the full potential of the Internet of Things, Artificial Intelligence and Machine Learning must be a part of it. Only then can there be a complete, interdependent, distributed ecosystem which will have transformative value gleaned from the flood of data generated
New-age technologies like the Internet of Things are redefining the way businesses across the globe generate and consume data. Successful businesses thrive on the ability to convert data into insights.
In fact, their capacity to continuously analyse data and deploy insights to improve customer experience or to drive new operational efficiencies has now become a core business process to deliver competitive advantage.
However, the true potential of Internet of Things can only be unlocked when it comes together with Artificial Intelligence and Machine Learning. Together, Machine Learning and Artificial Intelligence have the ability to drive transformative value from the flood of data generated by Internet of Things devices.
Combining the three will become one complete, interdependent distributed ecosystem. This intelligent ecosystem is called IQT, which stands for the IQ of Things. In other words, IQT is the combination of Internet of Things and practical application of Artificial Intelligence. The concept of IQT is all about elevating the IQ of all the connected devices, thereby making the technological ecosystem around us smarter.
According to International Data Corporation, India’s Internet of Things market is going to reach $34 billion by 2021. As Internet of Things continues to take shape and bring more and more data sources online, one of the biggest challenges is to understand the data generated by all connected things in real time and to act on it. This requires layers of intelligence across the Internet of Things continuum. Hence, to gain the most from Internet of Things, organisations should distribute analytics into a three-layered structure, flowing from the edge, through to the core and to the cloud.
For instance, suppose the edge to core to cloud is a sporting event like football — The Edge: Athletes represent the edge. Using talent, coaching and instincts, athletes must quickly make decisions and respond during a game.
Therefore, the edge is where things first get connected, is the first layer of intelligence in IQT.
For example, self-driving cars, robots in factories etc. The sensors attached to all these connected devices generate the data from the operating environment which is sent it to edge compute systems for processing and analysing.
The Core: The core represents the coaches on the field. Coaches make calls from their oversight of the field of play and build their athletes intelligence through plays, tactics and drills.
Core enables the higher order of intelligence and more complex decision making in real time. As the number of sensors grows exponentially and the sheer volume of data becomes too great to handle at the edge level, the core aids in extending the enterprise compute capabilities from the data centre, thereby increasing analytical capabilities to generate valuable insights.
The Cloud: In the cloud, where the deep learning takes place, envision the athletes and coaches are learning by watching films and diagramming plays on whiteboards to analyse their own performance and those of their competitors.
Cloud is where the most sophisticated high-volume data storage, processing, and analytics occurs for realising longer-term business insights. Once the data becomes insurmountable for the core to handle in real time, the core transmits the data to the cloud. The cloud utilises deep learning to glean valuable and actionable insights that drive analytical models at the edge and augment machine learning in the core. This process results in lower cost of artificial intelligence.
This three layered process results in faster adoption and more practical implementations that excel in both training and inference to ultimately “train” the edge, as coaches train their athletes to perform and execute better in each successful opportunity.
Therefore, the full potential of IoT can only be unlocked with the benefit of a complete, interdependent ecosystem, in which connected things, artificial intelligence and machine learning all come together to make things smarter.
As devices at the edge become smarter, organisations can leverage the rising “IQ of Things” to transform businesses and industry.
(The writer is Director, Digital Cities & Mega Projects, Government Segment, Dell EMC Commercial)
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