In which areas is Data Mining applicable?


As we have already discussed here, data mining is a technology that can be used in different contexts.

Below, we present some examples of data mining applications.

Business management

In administration, there are several areas where data mining can improve an organization’s bottom line.

We have already mentioned some examples throughout the text, such as the decision-making about the product portfolio.

The technology can also be used in hiring policies, marketing, sales, and customer relationships, areas we’ll talk about in other topics.


Public administration bodies responsible for the safety of the population can benefit immensely from data mining to make their actions to combat and prevent crime more efficient.

By analyzing data about events (place, date, and time when they occurred, for example), it is possible to find patterns and act in a timely manner to reduce the number of events.

Data mining can also help with private security, allowing you to allocate protection resources intelligently.


In healthcare, there are several specific areas where data mining can be applied.

For example, finding patterns of correlations between symptoms, diseases, patient characteristics, etc.

These data allow researchers to raise hypotheses for their studies.

In addition to scientific research (see more in the next topic), the data can help hospitals adopt the structure according to the needs of each season.

Scientific research

For scientific research to result in important discoveries, it is necessary to collect, classify, select and analyze tons of data.

Data mining makes it easy to monitor variables and discovers hypotheses to solve complex problems.

Which can result in advances in the fight and prevention of diseases, in the development of sustainable materials in the laboratory, and in other lines of scientific studies.

Sales and Marketing

Data mining clustering and classification techniques are methods that help discover factors that influence consumers’ purchasing decisions.

This information results in targeted marketing actions that are much more likely to draw attention and engage the target audience.

Likewise, sellers and sales channels, armed with this information, can significantly increase their conversion rates.


Data mining can be used by supervisory bodies and banking institutions to detect possible fraud and financial crimes.

Banks and insurance companies can use the data to calculate risks in granting credit and also in investments.

In addition, the financial sectors of companies also find in data mining an important resource to improve control tools.


The energy supply sector for the population and for industries is known for instability, as several problems in generation and transmission are common.

Processing data through artificial intelligence makes it possible to add efficiency to the sector, identifying patterns and anticipating problems.

If it does not result in the elimination of interruptions in the energy supply, data mining at least reduces the incidence of occurrences and speeds up the resolution of setbacks.


Frauds and hacker attacks have become increasingly common, unfortunately.

Among the agents looking for ways to increase protection against this type of problem are telecommunications companies.

With data mining, they can analyze the entire immensity of data they have, find patterns in attacks and fraud, and, from there, think of preventive actions.


The largest online stores have been using data mining for a long time. Algorithms identify user buying patterns.

That’s why, when viewing a product on an e-commerce site, the site displays recommendations for related items.

This is a pretty basic usage.

With more advanced modeling, it is possible to plan promotions and advertising actions to boost sales.


CRM stands for Customer Relationship Management.

The most modern CRM methods are those that use technology to offer the most personalized service possible.

With data mining, professionals in the field save hundreds of hours analyzing customer data, as the algorithm automatically generates the most relevant information.


The largest agribusiness producers work with small profit margins when selling the commodities they produce.

This is because the production (inputs and pesticides) and logistics costs are very high.

In such tight calculations, any opportunity to reduce costs and increase productivity makes a big difference, and data mining is one of the best ways to find those opportunities.

Types of techniques used

As we mentioned before, it’s time to talk about some of the main techniques and knowledge used in the data mining process.

Check out!


If you are not very fond of Exact Sciences and Mathematics, working with data mining algorithms is probably not a good professional option.

Data mining involves a lot of knowledge related to statistics, with calculations applied to discover patterns and build predictive models.


It is a technique that is also known as data clustering.

This is the process of identifying similar and dissimilar data to each other.

This segmentation is fundamental for the selection of data groups and subsequent generation of insights.


Visualization techniques are used early in the data mining process as a first step in discovering hidden patterns in a large group of data.

Decision tree

It is a predictive model that, as the name implies, forms a design that resembles a tree.

Model branches function as classification methods.

The decision tree is a technique that allows easy interpretation of data and shows the path to be followed to achieve a certain objective.

Association rules

This is a technique that helps the user to find associations between two or more items.

By defining relationships between database variables , it is possible to discover hidden patterns.

Neural networks

Neural networks are most often used in the early stages of the data mining process.

In this context, they serve to model relationships between the data entering and leaving the mining process.


Classification is a technique that helps to obtain important information about data and metadata.

It is closely related to the clustering technique and uses the decision tree or neural network.

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