To imagine that physical objects could be connected to the Internet and that they could communicate with each other at the same time seemed ludicrous even a decade ago to most people. However, Internet of Things (IoT) is real, and it is here. This system of connected devices connected to the Internet via sensors and churn out a large amount of data. IoT-enabled devices are no longer mysterious. They are used in the health sector, smart homes, vehicles, gadgets, and just about everywhere else.
It shouldn’t come to us as a surprise that all these devices result in large volumes of data, adding to what is often called the Big Data. While this data may seem insurmountable, and sometimes even redundant, artificial intelligence and data analytics can empower users, manufacturers, and businesses to venture into unchartered territories.
In this article, let us take a look at how artificial intelligence and next-level data analytics are helping to unlock the potential of IoT.
What’s the problem with IoT today?
While most devices manufactured today have some level of IoT enabled in them, there is a lacuna of sorts, when it comes to the actual utilization of the technology. This is because, at its simplest, IoT connects devices using sensors and Internet, but there is no way to really understand the data that is produced or to enhance features that already exist. While IoT has helped millions of people to communicate with their cars, homes, machines in factories, etc right from the smartphones, a lot of data that is churned out by these IoT-enabled devices is simply ignored.
Artificial intelligence and advanced data analytics can help to put IoT data into perspective. Whether it is data from medical devices or from IoT-enabled cars, analyzing this deluge of data is humanely impossible. Next-level data analytics use predictive analysis, advanced statistics, and other methods to provide insights from both structured and unstructured data. Most importantly, ignoring all this valuable data can prove to be disastrous to the society as a whole.
How can data analytics and artificial intelligence take IoT to the next level?
Data Analytics derives meaningful conclusions by examining data sets of various sizes. Conclusions can be used to identify patterns, trends or even predict certain outcomes. These conclusions help businesses to improve their products and services, come up with better business strategies, and make effective decisions. In addition, deriving useful conclusions from datasets will help businesses to drive revenue and profits, and gain a competitive edge. Datasets that can be used from different sensors include:
There are different types of data analytics that can be used with IoT-enabled devices. Some of those are:
There are a number of ways businesses can take IoT to the next level by using Data Analytics. Data insights can be used for the purpose of marketing and product usage analysis. The insights can help not only consumers but also businesses. Video and audio analytics, social analytics, etc. are opening doors to analyzing emotions and behaviors of people. This can be particularly useful to avert emergencies in crowded places or to improve products.
Turbo-charging IoT with Artificial Intelligence
A Gartner study predicts that more than 80% of IoT projects will involve an AI element, and that is a whopping 70% increase from today’s situation. Most organizations are looking at machine learning and deep learning to unlock the potential of IoT. Machine learning identifies trends and patterns within data sets, and also pinpoints anomalies if any.
Machine learning makes identification of patterns more accurate as it does not depend only on numbers, but also on other aspects. Speech and facial recognition, emotion and behavior analysis, and predictive maintenance are all aspects of AI that will help take IoT to the next level. Imagine being able to predict the time for servicing a gadget based on an individual’s frowning patterns? This can happen. Artificial Intelligence is also being used in risk management, and avert disasters and emergencies from occurring.
Machine learning enables a software “agent” to identify patterns in a dataset and use those patterns to learn how to adjust the way it further analyses data. The best example would be movie recommendations on Netflix or playlists on Spotify and Apple Music. Machine Learning can identify usage patterns in IoT and help manufacturers to improve customer experience. On the other hand, businesses can benefit from the competitive edge, improved products and services, and enhanced revenue-making potential.
Need for implementing AI and Data Analytics quickly
As you can see, you cannot remove artificial intelligence or data analytics from IoT. IoT without these two important technologies will simply enable devices to communicate with other and there is no room for improvement of products and services or opportunities to identifies usage patterns and trends. Both Artificial intelligence and Machine Learning take IoT to the next level and help customers and businesses to unlock experiences and opportunities they never knew existed. The future of IoT certainly vested in the implementation of Artificial Intelligence and Data Analytics. Sooner these two technologies are implemented, the better it is for IoT success.