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Using Machine Learning to Analyze Customer Usage Data for IoT Devices

Published Date
Author Cadence PCB Solutions

                                   

 

What is the Internet of Things?

The Internet of Things (IoT) is a tech buzzword—but what does that actually mean?

 

The definition is simple: the Internet of Things is the concept of connecting any device with an on/off switch to the Internet, and to each other. The amount of devices that have Wi-Fi capabilities, like smartphones, watches, even home entertainment systems, are growing rapidly. It’s also cheaper and easier to access this technology, helping it become more widespread. All of these factors have created a perfect storm for IoT devices to become ubiquitous in our everyday lives.

 

It’s probably not hard to think of a few examples of these kinds of devices - watches, phones, devices like Alexa or Google Home might come to mind. But this technology is only going to expand, becoming more widespread.  In the near future, your car might have access to your calendar and plot an efficient route to a meeting, or text the person you’re meeting if you're running late. Your alarm clock might wake both you and your coffee maker up, so a fresh cup of joe will be waiting for you by the time you walk to the kitchen. When you begin to think of the possibilities, it’s not a surprise that Gartner has predicted that 65 percent of enterprises will adopt IoT products by 2020. IoT technology is convenient for customers and represents great potential for collecting usage data. But this widespread use and collection of data raises more questions. How do you best organize and analyze data from IoT?

 

Introduction to Machine Learning

 

This is where machine learning comes in. Machine learning is the science of getting computers to learn and act like humans. The eventual goal is for computers to improve their learning autonomously by feeding them data to analyze and learn from. Machine learning can take a few forms:

  1. Guided learning (where the desired outcome is known)
  2. Unguided learning (the data is not known beforehand)
  3. Reinforcement learning (where the learning is a result of interaction between a model and environment). You may have heard about Google using machine learning to cut costs at one of their data centers.

 

So how does this tie into data organization, management, and analysis? If data is not utilized, it’s useless. And if there’s too much data,  can’t be organized, and analyzed — it’s useless or Machine learning can assist with this. By combining the diverse collection of data from IoT devices with the powerful predictive analysis of machine learning, you can leverage data into valuable insights.

 

Examples of Machine Learning and IoT Devices

Using algorithms from machine learning can help make IoT data better suited for processing and analysis. Deploying these algorithms properly can help with organizing and tagging data. With machine learning, companies can quickly evaluate data and check if it meets certain compliance requirements — useful in highly regulated and financial services.

 

Machine learning can analyze data as well. For example, an algorithm can parse data coming back from complex machinery. Using machine learning technology, an algorithm can be generated that can predict and alert businesses about when systems might break down or need maintenance. This predictive capability can help businesses save time and money.

 

Another way predictive machine learning can be integrated is through high tech farming. Sensors in farm equipment collect data about soil quality, weather, and plants, which could then be used to make predictions about crop yield, or fertilizer usage for the coming season.

 

Using machine learning to analyze customer usage data can also have promising effects on marketing research and analysis. Companies with IoT devices can track how often these devices are being used, and then compare that usage to how see how it corresponds to how much product is being sold.

 

In these examples, IoT devices are generating a lot of data, which can be combined to form new insights for businesses. Machine learning is useful if you have a goal, but don’t have all the variables that might help you make a decision — tell the algorithm your desired outcome, and it can isolate relevant variables in the data. If you are trying to solve a problem and already have an abundance of relevant data, machine learning may be a good bet — machine learning models need at least a thousand data points to be helpful.

 

Conclusion

IoT and machine learning are both in relatively early stages, and these examples are only a taste of what the possibilities of merging these technologies represent. Smart algorithms can be used to collect data on user experience, which can then help improve experience or predict market behavior. Companies using IoT data should have a good understanding of what kind of data they have and what kind of outcomes they want to make the best use of machine learning for IoT analysis.