Data Mining: what is it and what is its importance for companies?

If you are familiar with the corporate universe, you know that dealing with a ton of data is almost a routine.
You also know that this data is stored in digital databases, containing all types of information: personal, scientific, commercial, governmental, etc.

For this reason, it is fundamental to filter all information, identify what is relevant, and thus discover how to use it in business strategies. And that is where Data Mining comes in.

Want to know more about the concept and discover how it can boost your company's competitiveness? Then, keep reading and check out the article that the CodeBlog team has prepared for you!

What is Data Mining?

Translated as 'Mineração de Dados', Data Mining is an analytical process that explores large volumes of data to find relevant patterns or systematic relationships between variables and then validate them. It is like a detective, who finds the clues and validates all of them.

The process is divided into three stages: exploration, model building, and validation.

In this way, Data Mining tools work in data analysis, identifying problems or opportunities and diagnosing the entire behavior of a business.
With a complete set of information in hand, it is up to the user to decide how it will be used to promote competitive advantages.

In practice, Data Mining segments data, evaluates possible future scenarios, and finds answers to questions through sophisticated mathematical algorithms.
The concept, focused on large data sets, is defined by the automatic discovery of patterns, prediction of results, and creation of actionable information.

How does Data Mining work?

Data Mining is not as complex as it seems, mainly because data mining is built based on models, which are subdivided into several stages.

First, the user defines the problem to be solved and selects the tables they wish to analyze.

Next, the model training is started, where the algorithm is executed on the selected tables and generates a set of rules and patterns. (It is at this moment that the user chooses the dataset to be investigated).
Finally, the data query stage arrives, where the user uses the rules and patterns discovered by the algorithm to obtain valuable information.
Undoubtedly, this is a powerful ally for transforming data into valuable insights for your business.

The stages of Data Mining:

In the previous paragraph, you checked a practical example and visualized the stages of Data Mining in a generic way. To better understand what each of them is about, we brought more detailed explanations. See in the following topics:

Stage 1: problem definition

The first step to performing data mining is to define the problem that will be addressed. For this, it is necessary to understand which failures need to be solved or which processes must be optimized and, then, establish realistic goals.

In this stage, it is fundamental to outline clear objectives regarding the results that will be collected.

Stage 2: data exploration and integration:

The second stage is marked by statistical tools.
It is at this moment that analysts perform the collection, describe what was obtained, start the exploration of the initial data, and check the quality of the information, discarding what is considered 'irrelevant' for the business.

Stage 3: Data preparation and selection:

While stage 2 starts the exclusion of information that is not useful for the applied objectives, stage 3 is focused on the elimination of recurring and duplicate data.
In addition, it also prepares this data using numerous filtering methods, utilizing diverse combinations and filling empty values.

Stage 4: Data cleaning:

The true cleaning of the data occurs only in the fourth stage of the process, and is characterized by a more careful evaluation of the selected data.
In addition, this stage also performs data treatment, in case they present problems.

Cleaning is the procedure that ensures the data is, indeed, mineable. For this, it verifies erroneous additions of data in the system, conflicting, duplicate, and recurring information, to ensure the continuation of tasks and, above all, the generation of relevant knowledge for decision-making.

Stage 5: Mining:

In the fifth stage of the process, after filtering and treating all information, the data mining is, finally, concretized.
The process requires using a series of techniques that work on establishing patterns, trends, and general correlations within a common base.
Examples of this are artificial neural networks and decision trees - mathematical models and graphical representations that communicate to managers which patterns can be exploited by the business.
In this way, what was only an indication transforms into solid knowledge for the business, contributing to the development of more assertive commercial strategies.

Data Mining: why bet on it?

The application of Data Mining is an excellent ally in boosting business. After all, among its many applications, the tool allows companies to classify customers based on their purchasing potential, directing them to personalized offers, and to explore their sales databases, aiming for associations between products to create bundled offers.
In addition, it also enables the creation of anti-fraud mechanisms for credit card administrators, through payer classification models.
Last but not least, Data Mining also helps identify market trends, using data from social networks to understand user behavior.

Now that you already know the benefits of Data Mining, it is time to act!

How about taking advantage of all the power of this tool to create competitive advantages for your business?
Remember to properly manage the information obtained through data mining, so that it transforms into real value for your company.

Want to stay updated on the best practices and technological solutions?
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