What is Data Mining ?
Data mining is a process in which technology is used to find patterns, connections, correlations or anomalies in a large amount of data, allowing problems, hypotheses, and opportunities to be found more easily.
In the corporate world, data mining generates insights that result in competitive advantages for the company.
The processed data can motivate corrective actions or strategic changes, such as changes in the product portfolio and investment direction.
The process can also be applied in scientific research, in high-performance sports, in public administration, and in any other area in which it is possible to seek improvement from data analysis.
We can translate data mining as data mining, a name that makes an analogy to the activity of extracting minerals present in the soil.
It makes sense because, when exploring a silver mine, for example, the metal to be found is present in a tiny amount compared to the other types of rock present at the site.
That is, the silver is hidden.
Likewise, in big data, useful information is hidden in the middle of a sea of data.
And data mining is all about finding them.
To be able to process large amounts of information, artificial intelligence and machine learning algorithms are written.
The more sophisticated the algorithm, the more it is independent of a human user to build specific exploration models.
What is Data Mining for?
You’ve already learned that data mining uses raw data to generate some kind of value .
But what is the purpose, what kind of value can be generated, in short, what is data mining for?
One of the main purposes of technology is to serve as a basis for planning strategic actions.
Through data mining, a company can, for example, recognize patterns in the preferences of its target audience and create marketing campaigns based on the insights obtained.
Data mining can also indicate a correlation between different problems in a production line, making it possible to find a common cause that would be impossible to identify without algorithms.
In this example, it is a situation where the process results in greater efficiency and cost reduction, making the company more productive and competitive.
It is also possible to apply data mining techniques in the analysis of the competition and possible partners of the company.
These were just two examples, but the application possibilities are endless (later, we’ll talk about use in other areas besides these).
The important thing is to understand the premise: data analysis is one of the main ways to make a company evolve.
It’s just that you need to use technology to transform raw data into information that clarifies rather than confuses, and that’s what data mining is for.
How important is Data Mining?
Whether in commerce, industry, agriculture or the service sector, the reality of the current market is of a lot of competition and companies facing difficulties to adapt to constant changes.
We are talking about political, economic, technological and consumer behavior changes.
Everything is much more volatile than it used to be, which requires companies to have characteristics such as flexibility to adapt to different scenarios.
Data mining and other data collection and analysis processes are resources that help to face this context.
With data mining, decisions made by managers are more likely to succeed, and risks are minimized.
Problems are identified more quickly, which increases customer satisfaction.
Data mining makes it possible to find opportunities for improvement in different areas of the business and, therefore, is a very important tool for those who want to stand out from the competition.
If you’re still reluctant and don’t want to waste time or money building the necessary know-how to handle the technology, think that your competitors can treat it as a priority.
This means that they will have much more dynamic processes to identify threats and opportunities in their business, being able to reinvent themselves with agility to meet market demands.
But of course, mastering technology is not enough.
It is necessary to use strategic intelligence to interpret the insights generated and transform them into actions that add to competitiveness.
Never forget this: technological solutions for management must always go hand in hand with strategic thinking, as there is always a degree of intuition and subjectivity in human decisions that machines cannot reproduce.
How does Data Mining work?
To put the data mining process into practice in your company, there are some steps to be followed.
Let’s find out what they are now.
1. Strategic planning
The first step is to understand what the objective of the data mining process will be and how it is aligned with the strategic objectives of the company.
You can enter goals and indicators to be improved with the process, for example.
2. Data selection
The second step is the moment to think about which data, from which sources, will be important to use as inputs and obtain the expected results, according to the definition of the first step.
From there, the data that matters for the objectives are selected, allowing you to focus only on the really relevant information.
The selection and cleaning phase of this data is the most time-consuming of the entire process, but it guarantees a reliable result in the end.
3. Data Modeling
In the third phase, data mining techniques are applied, from which the selected data are processed by algorithms in order to identify patterns, correlations, problems, etc.
4. Evaluation of results
After data mining the selected data, it was time to evaluate the results, always considering the context of the objectives that were outlined at the beginning of the process.
What do the generated insights communicate and how can this information be useful according to the company’s interests?
5. Presentation and actions
In the case of professionals specialized in data science, statistics, and algorithms, the work ends with the presentation of the most important insights to the company’s directors.
The directors, on the other hand, receive the mission of taking what was presented and transforming it into actions to improve the organization.