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Analytics Of Things: Taking IoT to the Next Level

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Analytics of Things provides real-time data analysis for the Internet of Things, making the data more meaningful and valuable.

To date, a lot of initiatives have been taken on the internet of things (IoT). IoT is basically a whole lot of connected internet devices that pull data from different sources. But the question remains, how can this data add value without analysis? So we need to be more concerned about the analytics part before creating sensors that stream the data from the devices. Here is where the concept of analytics of things (AoT) comes in, which in simple terms, is analyzing the data collected from IoT devices.

What Is AoT?

The idea of AoT is basically that, as the modern devices connected to the internet produce magnanimous amounts of data, that data can only be used after proper analysis. The concept behind the analytics of things suggests that those devices which are smart enough to make decisions must be provided with useful information. This is also likely possible only after processing the data generated by them.

We can easily understand this concept with an example. A smart thermostat is a very common item nowadays, however many people don’t really understand how it works. These thermostats sense both the presence of people and the current temperature. Also, such “smart” thermostats keep track of the daily activity of people in that room. However, how is this data used? This data is carefully analyzed by the special embedded analytics of the thermostat, which provides it with useful information about switching off or on and controlling its temperature. This allows these devices to be useful and intelligent enough to save huge amounts of money, without even being connected to the internet.

Obviously, their use can be enhanced tenfold if they are also connected via the internet. One good use of it can be to monitor the temperature remotely and then change it. Through a Wi-Fi connection, you can turn the thermostat on or check the temperature from anywhere in the world.

How Is AoT Related to IoT?

People have employed various methods to collect as much data as possible. For this, they employ various kinds of sensors in “smart” devices, which collect data whenever these devices are used and are connected by the network called the internet of things (IoT). However, this data can be completely wasted if not analyzed and processed carefully in real time. This is only possible through AoT. (To learn more about real-time analytics, see Internet of Things (IoT) and Real-Time Analytics – A Marriage Made in Heaven.)

Analytics of things is crucial for the real-time use of the data collected by IoT devices. AoT helps in the quick analysis of the data gained through IoT devices and also in getting complete information from the data set. Another thing about AoT is that it can collect large amounts of IoT information in one place. This allows the data to be easily compared for analytical purposes.


How AoT Can Help in Analytics

AoT has already proven its worth in a variety of fields. It can be used for analysis and comparison between large amounts of data in real time. This will enable companies to analyze data quickly and get useful insights. Other such places where AoT can help include:

  • Futuristic self-driving cars can become a reality because of the use of AoT. The technologies which will be used to drive such cars are being tested extensively by both pioneers of the automobile industry and analytical organizations. These cars collect a lot of information coming from the sensors in the cars, and use quick AoT techniques for analyzing the data in real time and providing a safe journey for passengers.
  • Another place where AoT is helping analytics is the field of predictive maintenance. In this technique, data is collected from important devices like ATMs, computers and engines to get information about a breakdown before any actual damage occurs. This can predict and prevent mishaps, and in turn, save a lot of money.
  • Power infrastructures are now being upgraded gradually into smart grid systems. These systems are not only much more proficient, but they also help in saving power resources and money. Analytics is being used to get insights on the power lines, in order to protect them from damage and also in order to properly balance the power according to the requirements. The initial analysis is being made faster by the implementation of analytics of things, thus all the analysis is being done in real time. This will not only reduce power outages, but will also make electricity available at much cheaper rates in the future.
  • Nowadays, information about traffic conditions is becoming more accurate and hence, more dependable. Gone are the days when you had to tune in to the radio to learn about the latest traffic updates and plan your outing accordingly. Now, due to the advent of AoT, traffic updates are available in real time through numerous apps.

What Are the Challenges for AoT?

There are many challenges in the way of AoT. Some of these challenges include:

  • Speed of working – There is a large amount of data to be analyzed. The main problem is determining what to process and what not to. Also, the speed of transfer isn’t very high at all times, so that is a concern. A lot of filtering will be needed for the proper data to be transferred from the device. This can hamper the speed of working.
  • Privacy – Another concern is, how will the data be kept safe and secure from prying eyes? As sensors record all kinds of data, this could also include private information about a person.
  • One reliable standard – What should be the standard of communication? The proper standard will have to be decided upon, with each device having a different one. Every device must communicate with each other in a precise manner.
  • Complexity – Another major concern is about resolving the complexity of the data. The data is transferred from a large variety of sensors, thus there is a lot of diversity. So, a solution has to be determined that will tone down the complexity and keep the data simple and easy to process.

Some Practical Use Cases

Many companies are using AoT for various projects. For example, a company called Teradata, specializing in predictive analytics, is using analytics of things for predicting failures in important electronic devices like engines, computers or ATMs. (For more on predictive analytics, see How Contextual Integration Can Empower Predictive Analytics.)

Google is also using AoT for designing self-driving cars, which collect and process information in real time. Additionally, many personal fitness companies like Nike are using AoT to give fitness tips in real time based on the user’s schedule.

What’s in Store?

In the future, IoT and AoT will work together to create a more interconnected network of “smart” devices working together to enhance the quality of life for people all over the world. Newer analytics methods will mean faster rates of transfer and analysis. This will help everyone to lead more information-rich lives.


AoT is the newest technique in analytics, aiming at faster analysis in real time (or as close to real time as possible). AoT will help the IoT in order to create a faster and more intelligent network of devices, which will help users in all fields of life. Although AoT is at its nascent state and IoT is also not yet completely matured, the future is very promising. As we move into the future, with the advent of new technologies, devices, sensors, etc., AoT is going to have successful implementations in every sphere of business and personal life.


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Kaushik Pal
Technology writer
Kaushik Pal
Technology writer

Kaushik is a technical architect and software consultant with over 23 years of experience in software analysis, development, architecture, design, testing and training. He has an interest in new technologies and areas of innovation. He focuses on web architecture, web technologies, Java/J2EE, open source software, WebRTC, big data and semantic technologies. He has demonstrated expertise in requirements analysis, architectural design and implementation, technical use cases and software development. His experience has covered various industries such as insurance, banking, airlines, shipping, document management and product development, etc. He has worked on a wide range of technologies ranging from large scale (IBM…