How Machine Learning helps you better understand your company
Machine learning is a popular and a very hyped media topic these days.
Most people probably already know that machine learning is used to make predictions.
But did you also know that machine learning makes sense even when you don't need those predictions? No, then it is high time to find out why. Of course only if you want to enjoy the benefits of a real and objective advantage.
You are probably already familiar with the principle behind the machine learning process:
You observe events and collect data about them. And now you would like to use this historical data to make predictions about future events that are similar.
Well, this is a classic case that is solved with the help of machine learning.
But what if you know from experience what will happen. So why develop a machine learning process?
The rarely mentioned super power
The use of machine learning offers another - sometimes little-known super bonus: it enables you to find answers to the question "Why does this happen?"
Right, machine learning gives you a better understanding of why events occur and it doesn't do it somehow subjectively, but as rationally and objectively as possible, namely data-driven.
Imagine that you had some objectively and reliable insights with which you could better justify, evaluate and ultimately compare decisions. By applying Machine Learning such an unbeatable added value becomes possible.
A real world example for this super power
Want to have a small example? With pleasure. Let's take the topic of predictive maintenance as an example.
However, since machine learning is a general technology, you can also use this method in any other industrial fields, such as eCommerce, Marketing, Cyber Security, … add any industry you like here.
Now, back to the example: Imagine you are operating a machine and you are measuring hundreds of sensor values.
This can be for example vibration, temperature, energy consumption, volume, and many many more.
If you want to know whether a machine is about to break down or needs a routine check earlier than usual, you can use a machine learning approach that based on historical sensor data will generate a warning, e.g. "Machine XY should be checked because the deviation from the norm is too significant and it behaves more like if there is a defect".
This example is a typical example for the application of machine learning in the industrial sector.
This means that the operator can react preventively and maintain machines before a defect occurs and thus prevent high costs and downtimes - a real advantage.
But, what is perhaps even more interesting: With this machine learning method, you can now also wonderfully analyze which measured values caused the algorithm to predict a possible impending defect.
Imagine you have several machines and the algorithm regularly triggers the alarm earlier than you would expect for a particular model series. Or you are a manufacturer of machines and want to understand why this series causes so many problems.
Sure, you will examine the machine closely, but where do you start?
Start by asking you Machine Learning model for help
Actually you should start by asking your machine learning algorithm. Why?
Well, you know that it has often correctly predicted that a machine has or will have a problem.
And now, the second major strength of machine learning comes into play: If you analyze your machine learning model correctly, you will quickly find out which sensor values the algorithm has identified as the source of the problem.
But it gets even better. Not only some of the hundreds of measured values are identified, but also the combination of certain values can be identified.
This mean that with this information, you may be able to directly identify what the cause of problem is without even having been required to physically examine the machine - and that analysis has been performed 100% objectively by applying a data-driven approach.
Thanks to the historical data, the algorithm has created a model for a correct and incorrect functionality.
Or imagine another scenario: You first examine the machine manually, then you find a probable problem and you would like to get an objectively analyzed confirmation by the algorithm.
In this case, machine learning too offers its excellent analysis options.
And these analysis are not only available for predictive maintenance. Whenever machine learning can be applied, this form of analysis can be carried out, since this functionality is always part of a machine learning process.
Regardless of whether it is Industry 4.0, IoT, marketing, security, process optimization and now please add here 1000 examples for any industries that collects data. Virtually every industry will benefits from these deep insights.
Overwhelming positive conclusion
So, you see, the unknown super-power number 2 of machine learning is a true all-rounder, which allows deep insights into the functionality of your company and its products. Unfortunately, this feature often doesn't get the attention it deserves.
Conclusion: Even if you think you do not need any predictions as such, it still makes sense to use machine learning because it will help you better understand your processes and can will help you make better decisions by applying a data-driven approach.
If you want to know more about this topic, feel free to contact me at any time. I am offering you a short and free consultation, where I answer your individual questions about machine learning. Just write me a message right now.
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Thank you for reading.
Dr. Thomas Vanck
About the author:
Dr. Thomas Vanck is an expert for Machine Learning and Data Analysis. Since years, he supports companies in applying their data for bigger success. He is looking forward to hear your questions about your planned or ongoing data projects. Feel free to write him a message.