Sensor data analytics in manufacturing: the ‘why’, the ‘when’ and the ‘how’

We bet any electronic that you pick right now will have a sensor in it. And not just electronics, you will find sensors in buildings and trees even. There is a good reason for their presence though. Users gain value from the data that comes from their readings. 

In this article we are going to tell what sensors really are and the basics of sensor data. But before you do that why don’t you take a look at these two articles about four types of data analytics and best software outsourcing destination in 2022.

‘Why’ Analyze Sensor Data?

The why of analyzing sensor data depends on what your enterprise does with it. Most companies analyze the data to optimize, monitor and or design products. 


The process of monitoring keeps a certain process in pre-supposition. You make a fault model that defines parameters of failure. Then you analyze the sensor Data Analytics, identify a faulty pattern according to the model you created and correct them.  Monitoring lets you enjoy good quality assurance and predictive maintenance. 

The sensor data that you collect will warn your maintenance team about a faulty pattern. This will give them a chance to predict the error in the malfunctioning equipment, make necessary preparations for it and avoid unnecessary expenses.


Optimizing the manufacturing process means you fine tune every production step to maximize yield, improve quality and stabilize the output. For example a tire manufacturer can use the sensor data to optimize the temperature of the steam they use to mold tires thus managing to cut on expenses and improving quality.  

Design of Product

If the manufacturers take the results of the sensor analytics into account, they will be able to design better products. Smartphone manufacturers use the data from sensors in their products to identify the popular and non-popular features and then come up with designs that are better than the previous ones. 

‘When’ Analyze Sensor Data

When you analyze your sensor data, it must be based on the specific task that you want to complete. Doing it once will not be enough, when you see the benefits that it brings, you will probably want to do it again. There are two models that tell you when to analyze sensor data: Ad hoc and real time. 

Ad Hoc

In this model, you only look into the sensor data when there is demand for it. It is usually done by data analysts or scientists. 

Real Time

Just the mention of real time gives off the sense of quickness. That’s exactly what this model does, it provides you analysis results without break. The definition of real time is different for different companies. For some it is 35 millisecond of gathering and analyzing data while for others it is 30 minutes. Choosing the right time interval for real time is one problem that many face in sensor data analysis. 

‘How’ to Analyze Sensor Data

How you analyze your sensor data also depends on what your needs, context and tasks are. However, big analytics recommend an architecture that works for almost everyone. The starting point of this architecture is sensor, from here on the data gets transmitted through gateways and is filtered. 

The data then moves to big data lake and is forwarded to the warehouse. Data analysis happens in the data segment. The last and probably the smartest form of data analysis is machine learning. 

Final Words

The point of our discussion is that you can analyze your sensor data both on demand and regular intervals. You can use machine learning, big data lakes, warehouse and algorithms to do that. Sensor data analysis will help you to optimize, monitor and create better products. 

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