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Data Mining is much more than extracting knowledge from data. This is a high performance strategy for different industries. Understand more in the article.

Data Mining is the process of exploiting large amounts of data from patterns identified. This large amount of data is stored and called Big Data. In addition to that, big data is one of the pillars of the 4.0 industry. So it makes data mining directly related to the revolution that happens in industries today.

Industries today have a constant data collection in all their processes. This happens because of sensors strategically located in industrial plants. And with that huge amount of information, we need to do what we call data mining to come up with valuable insights from it.

Therefore, it can be said that data mining is a process of techniques and tools that generates learning. Through the use of algorithms, it is possible to export data, and highlight patterns. And it is from these standards that discoveries happen.

To understand it all in more detail, in this article we will explain the concepts of Big Data and Data Mining. Added to that, you will understand a brief history of the term. Finally, we cover the stages of the data mining process and industrial applications today.

Introduction to the Big Data Concept

Big Data is an application that can store and handle a large volume of data from various sources. In everyday life, these sources are social media, websites, videos, and clicks in general. On the other hand, in industries, the various sensors in machines and processes are the source of the data.

And in the case of industries, the proportions of the data collected is huge. Think of continuous process factories that do not stop their production, for example. Every moment a command is executed by the machines, data is being collected. Storing all this really requires a powerful tool like Big Data.

Within industry 4.0, data becomes a great ally of industrial managers. After all, it is from raw data and data mining that it is possible to generate ideas and make more agile decisions.

Giving it a little bit more context, Big Data is actually a new term. It emerged in the year 2000 to meet the demand for data storage. In fact, it was during this period that the expansion of technology, the internet, and virtual environments took on big proportions. And since then the reach of technology has only increased, and as a result data capture as well.

In order to be used strategically by companies, there is a constant need for analysis and understanding of these data. The knowledge hidden in this information conglomerate is very large.

Data Mining Concept

In a nutshell, Data Mining is an analytical process aimed at exploiting large amounts of data. It is a technology used in various areas, always in search of standards.

In general, data mining tools are based on algorithms. These perform the construction of artificial intelligence blocks, neural networks, induction rules, and predicate logic.

As well as techniques exist, some steps are also used in data mining. The process begins with exploration and then proceeds to model building or pattern setting. With these done, it is then possible to validate or verify the information collected.

The great advantage of data mining is that the system itself looks for anomalies and possible relationships between data. This way, the search becomes much more effective and focused on problem-solving.

Thus, the results are more competitive business opportunities, discoveries or even innovations. This enables companies to achieve the growth they desire by using the data they have collected themselves.

The history of the term

Although data mining is a constantly evolving technology, its history is still recent. The term was introduced in the 1990s, but since then it has developed a lot.

Along with the concept of data mining, we must also describe the data warehouse. Unlike Big Data, where data is not structured for direct analysis, in data warehouse this structure exists. And it is from this structure that data mining systems can do their job.

Thus, the development of all these technologies has been happening constantly. And who benefits from this are the companies and industries that use them.

Key steps in Data Mining process

For a data mining process to happen positively and generate the expected insights, there are some steps that need to happen.

Problem Definition

Before starting the data mining process, you must first define the objectives to be achieved. Also, determine which data sources will be used for mining.

Data Cleansing

The second step of data mining is literally eliminating duplicate and redundant data. This is necessary because millions of data are collected daily in an industry.

Therefore, it is in the cleaning of the data that a selection is made of what will be used according to the previously defined objectives.

Pattern extraction

This is the third stage of data mining and also the actual execution of the different data mining techniques. From this extraction, it will be possible to identify patterns and find models for the company’s growth.


Just as when production reports are issued to managers, after the extraction of standards it is necessary to evaluate all that has been discovered.

Thus, by positively evaluating some pattern, it is possible that this knowledge will support decision making.

Applications nowadays

Since data mining is present in industry 4.0, there are several areas that use this tool. However, data mining is also quite present in people’s daily lives. Below we show several data mining applications.


To be increasingly assertive about promotional campaigns, retail companies use data mining. The main goal is to determine the behavioral patterns of their customers. With this, the entrepreneur can understand what works best, to then invest more money and effort in this type of campaign.


In Taiwan, a data mining process has been developed to identify health fraud and abuse. The purpose of this application is to use clinical data and cases to identify fraudulent patterns and behaviors.

Other health-related application of data mining takes place in the United States. In this case, the idea is to identify patterns that can trigger infections in hospitals.

Although widely used, large Brazilian companies use data mining as well. Some examples are: Caixa Econômica Federal, Banco do Brasil, Vale do Rio Doce, Petrobras, and Dataprev.


In finance, a common application of data mining is the creation of customer classification models. Thus, it is possible to identify patterns of people who usually make (or not) payments on time. Once done, the model is used for a preliminary assessment before granting credit to new customers.


In places where rainfall is scarce, data mining systems can be used to generate energy demand, forecasting models.

Thus, it is possible to make planning of consumption and distribution of electricity in different regions of the country.


Finally, an example of using data mining techniques in industries is determining sales patterns over a period of time.

Thus, managers can use this knowledge to plan production or look for ways to increase results in periods of low sales revenue.


After reading this article, we hope that the relationship of Big Data, Data Mining, and all of it in industry 4.0 has become clearer to you.

In fact, data is collected at all times from everything we do, whether it’s on our mobile phone, computer at work, driving on the streets or in production processes within industries.

It is therefore believed that it is possible to extract powerful insights from this giant gathering of information.

From using data mining techniques in your industry, you will see that several benefits are achieved. As we have shown in the applications, it is possible to predict market trends and consumer behavior.

Likewise, data mining contributes to crisis management as production environments are constantly being checked.

With this, more accurate analysis and a competitive differential are achieved by industries in the most diverse areas.

Do you use data mining to extract high-quality knowledge from your own production data? Share your experience in the comments section below.


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