Why machine learning is key to your IoT success

By 2020, experts predict that the number of devices connected to the Internet of Things (IoT) will grow from 15 billion today to between 34 billion (BI Intelligence) and 50 billion (Cisco).

This network of connected devices is a huge contributor to the ever-increasing amount of data we create and collect, a volume which is currently doubling approximately every two years. By 2020, data from IoT devices will represent 10% of the world’s data, 4.4 trillion gigabytes (of a 44 trillion gigabyte total) a year.

We’re now creating so much data that it is impossible for humans to analyse it, and instead we must rely on computers and machine learning.

Using machine learning to create value

Machine learning refers to systems that can learn from the data they receive, rather than just following instructions. A simple example of this is your Amazon account: Amazon tracks your buying and browsing behaviour, and their system makes suggestions of products you might like; as your behaviour changes, so do the suggestions.

Other use cases include wearable devices that track your health and prompt you to visit the doctor if your health worsens; smart homes, cars, and offices that learn from your behaviour to manage your environment more effectively; and coordinated traffic lights that track traffic flow to increase the efficiency of our roads.

In each of these cases, a computer studies patterns and uses that data to improve efficiency or spot problems.

Data and scale are the critical success factors for any IoT rollout

Relatively speaking, building an IoT device is within reach of most businesses. You need a device with sensors and connectivity, but little onboard processing power (since normally most data manipulation is done remotely). You’ll need processing power at the backend to manipulate your data, but even small businesses can access scalable computing power through the cloud.

The real determinant of success won’t be the ‘what’, but the ‘how’. How will your systems use the data you collect to improve the user experience? How will they choose which data to keep and analyse, and what to ignore?

For your project to be a success, you need a large data set and a machine learning system that takes into account a huge variety of factors: different use cases, different environments, different seasonal changes, and much more. Your system must also use data from a variety of sources, both historical and real-time, and combine them to create actionable insights.

For example, in our previous example of traffic lights tracking traffic, the system must take into consideration the time of day, the season, the weather, and recent changes in traffic patterns; merging historic and current data to make useful decisions. This will be impossible without machine learning and a lot of data.

The first-to-market advantage

With so much to consider, some businesses might err on the side of caution, waiting before dipping their toe into the IoT pool. But in doing so, they could be ceding an advantage to their competitors.

Because the volume of data you have is key to creating value, businesses that enter the IoT market quicker will have a considerable advantage. If one business has a twelve-month lead on data collection, they will be able to introduce more advanced features first. It is conceivable that a business that gets ahead could lead the market for a sustained period if they keep using their advantage – other businesses may never catch up.

Are you considering developing an IoT product? Jemsys specialises in working with and analysing data from smart devices . We’d love to hear from you either in the comment box below, or through the contact form on our website.



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