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Analytics of things in the context of IoT

Analytics of things

Analytics of things – Next to IoT

Overview

The idea of the Internet of Things (IoT) is revolutionary and in the future, it is expected to find its way into everyone’s lives. Everyone, sooner or later, is going to be impacted by the IoT. Different surveys or studies project a future with a huge number of interconnected devices. For example, the ABI Research stated in 2014 that by 2020, there will be “41 billion active wireless connected devices”. The Gartner Research stated in 2015 that ““4.9 billion connected things in use in 2015 … and will reach 20.8 billion by 2020.” However, the data generated by the interconnected devices has been outpacing our ability to analyze and extract meaningful data out of it. As long as our analytical abilities are unable to keep pace with the speed of data generation, IoT will not be able to fulfill its promise. This is why Analytics of Things (AoT) is important in the context of IoT and can be said to be the next level of IoT. The sooner we reach there, the better it will be for us.








The potential of IoT and the fulfillment challenges

According to Simona Jankowski, Senior Equity Research Analyst, Global Investment Research, Goldman Sachs, the IoT represents the third wave of the Internet which follows the mobile revolution and the Internet itself. Obviously, IoT can transform our lives like never before. Though the IoT has already made some advances in certain domains, its potential largely remains untapped. To keep the definition of potential simpler and straightforward, let us consider just the monetary value of the impact IoT can have on different industries.


According to a study conducted by McKinsey, the consulting giant, the monetary value of the potential of IoT can be between $3.9 trillion to $11.1 trillion a year by 2025.Industry-wise/domain-wise, the monetary values of IoT potential can be as given below:

  • Factories: 1.2-3.7
  • Health and wellness: 0.2-1.6
  • Cities: 0.9-1.7
  • Retail: 0.4-1.2
  • Vehicles and transportation: 0.2-0.7
  • Homes: 0.2-0.3
  • Offices: 0.1-0.2

All figures are in trillion dollars and these figures are expected to be reached by 2025.

  • According to McKinsey, the global consulting giant, if the potential of IoT is converted into monetary values, by 2025, the value would be $4 trillion to $11 millions.
  • IoT can potentially replace the search feature, which is veritable symbol of Internet because people will have information delivered even before it is searched for. Millions of connected devices will collect information about the tastes, preferences, behavioral traits of people and it is as if the machines will preempt the requirements.
  • Detailed and real-time patient health information will result in improved medical care.
  • Consumer electronics industry will offer intelligent electronic appliances.

However, to tap the potential of IoT, a number of challenges need to be faced. Perhaps the primary challenge is to extract meaningful and actionable data from the large volumes of information the interconnected devices will generate. Data is offered in complex formats such as video, structured, unstructured, graphics and text. To be useful, data needs to be obtained and presented in a meaningful format such as analytics. So, while the capabilities of IoT are harnessed, it is equally important to find ways to obtain usable information from the large volumes of data.

What is AOT?

AOT is the term used to describe the analytics generated from the data generated by the interconnected devices or IoT. AOT makes the data generated by the interconnected devices meaningful and usable. It is a difficult task to distinguish between redundant and useful data from the huge volumes of data generated by the interconnected devices mainly because of the volume and the complexity in data. But without this distinction, IoT or big data in itself are unlikely to provide much value.








AOT is treated as the answer to the problem of finding insights from the huge volumes of data generated by the interconnected devices. It is even treated as the next level of IoT. While IoT is about connecting all sorts of devices through Internet Protocols (IP), AOT is about generating analytics from the data generated by such devices. While IoT is touted as the third wave by many, it in itself cannot bring about the desired impact without the analytics or usable data that AoT can generate. So, both AoT and IoT need to be treated as an integrated solution or a package.

You may also like to read The Analytics of Things & the challenges?

What is the potential of AoT?

IoT cannot fulfill its potential without AoT so in a way, it can be said that AoT is actually helping fulfill the potential of IoT. There are too many areas to be listed here that are potentially impacted by AoT but some important areas have been described.

  • Predictive maintenance can get a fillip with AoT. Analytics can help maintenance and troubleshooting of machines, engines or computers by providing predictive analysis on the health of the machines.
  • The self-driving cars that are being tested by a number of automotive companies and Google can actually get off the ground with accurate, wide ranging analytics.
  • The health or fitness devices can be massively upgraded with the help of the analytics that will be generated from the health data collected from different devices associated with human body.
  • AoT can help provide traffic information on a real-time basis by analyzing huge volumes of data generated by sensors attached with vehicles and traffic points.

AoT Use Cases

There can be several use cases for AoT. In fact, the same number of use cases applies for AoT as that in the case of IoT. An industry can implement one or more use cases with AoT depending on their requirements. For example, the health and fitness industry can have the following types of use cases:

  • AoT can be used to provide real-time information about patients’ health status such as blood pressure, blood glucose levels, heart rates, pulse rates and sodium levels at different frequencies depending on the severity. For critical patients, the frequency might be every 15 minutes.
  • AoT can be used to gather data from wearable fitness devices and provide alerts and tips. For example, a device can tell a jogger to slow down if the heart beat exceeds certain number of beats.
  • AoT can provide analytics that can help scientists find reasons for diseases peculiar to certain regions. For example, dietary and lifestyle data about inhabitants of the Indian subcontinent can provide vital clues on why they are relatively more prone to cardiac diseases.
  • AoT can help in critical health research such as cancer-related or HIV-related drugs by providing critical analytics to the researchers on a historical basis.

Conclusion

While the AoT seems like a great idea, there are several challenges on the way to its fruition. For example, it will take a highly intelligent program to filter out all redundant data from tons of data and produce accurate analytics. Accurate analytics will be critical in certain domains such as health and traffic management. While AoT seems to be making slow advances, it seems that it will be a while before it finds wider acceptance. The use cases, to start with, need to be defined well.

You may also like to read The Analytics of Things & the challenges?

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