What is data mining?
Posted: Thu Dec 05, 2024 10:28 am
Data mining, data mining or data prospecting are terms that you have probably heard on some occasion. In today's article we will resolve frequently asked questions and explain in a clear and convincing way what data mining is or what data mining means .
What is data mining?
First of all, we must say that this is not a new term. It is not something we have been using since the technological revolution, but it was with the digital transformation that we realized the need, the potential and the incredible value of data .
The data accumulated by companies or businesses, institutions or even any individual, has become raw material that, once well processed, becomes valuable knowledge for the design of strategies and pursuit of objectives in the field of extracted data.
Data mining and statistics, are they the same thing?
Although it is true that in a first approximation to the term we could find a certain parallelism with statistics, data mining works with a large volume of data and with a different typology. In addition, it pursues a final objective, the extraction of knowledge .
For example, statistics tell us that 16% of women are born blonde, but if we establish a set of rules based on background and other specific characteristics we could predict whether the future baby will be a blonde girl.
In any case, we have to grant statistical science the role of “mother” of data mining, but the big difference is that gambling data malaysia phone number mining does not produce data, but rather new knowledge extracted and generated by the tool in an assisted or automatic manner.
Where does data mining come from?
Out of necessity. The growth of digital databases and the emergence of various sources of data creation has generated the need to learn to interpret them, to know how to study them, in order to predict the future, understand the present and analyze the past.
This system, which could previously be done more manually, is now impossible with the exponential growth of data and it is necessary to use powerful tools.
This is why data mining becomes the method for solving the problem that involves the analysis of current databases.
Let's take an example. A fast food franchise wants to open a new establishment and is considering different geographic areas. One of the things this company will do is analyze its database looking for the profile of its best customers and cross-reference them with different sociodemographic data, establishing characteristic patterns and determining where a greater number of these "good customers" are located.
The data warehouse or the data warehouse
When data quantities were not as large as they are now, analysis and reporting could be done using conventional languages such as SQL. But once its growth took off, technology made it possible to build a new architecture called a data warehouse . Its first appearances date back to the late 1980s.
Today, big data and advanced technology have changed the capabilities of these warehouses and have reduced costs, improved performance and reliability, and ultimately, given more value to data.
Applications of data mining
We could say without fear of being wrong that distribution or advertising companies were the first to apply data mining, but in reality today it is used in any area or sector, for example:
The stages of data mining
There is a wide variety of data mining systems and each of them focuses on the typical characteristics of the data they mine or the technique they use.
Although data mining is the main process for obtaining knowledge, the development is broader and is described in several phases:
1-Data preparation . Sources are determined, data is transformed and standardized, corrected and stored.
2-Data mining . Data is classified and grouped and the method to be used is decided.
3-Evaluation . This is wheData mining, data mining or data prospecting are terms that you have probably heard on some occasion. In today's article we will resolve frequently asked questions and explain in a clear and convincing way what data mining is or what data mining means .
What is data mining?
First of all, we must say that this is not a new term. It is not something we have been using since the technological revolution, but it was with the digital transformation that we realized the need, the potential and the incredible value of data .
The data accumulated by companies or businesses, institutions or even any individual, has become raw material that, once well processed, becomes valuable knowledge for the design of strategies and pursuit of objectives in the field of extracted data.
Data mining and statistics, are they the same thing?
Although it is true that in a first approximation to the term we could find a certain parallelism with statistics, data mining works with a large volume of data and with a different typology. In addition, it pursues a final objective, the extraction of knowledge .
For example, statistics tell us that 16% of women are born blonde, but if we establish a set of rules based on background and other specific characteristics we could predict whether the future baby will be a blonde girl.
In any case, we have to grant statistical science the role of “mother” of data mining, but the big difference is that data mining does not produce data, but rather new knowledge extracted and generated by the tool in an assisted or automatic manner.
Where does data mining come from?
Out of necessity. The growth of digital databases and the emergence of various sources of data creation has generated the need to learn to interpret them, to know how to study them, in order to predict the future, understand the present and analyze the past.
This system, which could previously be done more manually, is now impossible with the exponential growth of data and it is necessary to use powerful tools.
This is why data mining becomes the method for solving the problem that involves the analysis of current databases.
Let's take an example. A fast food franchise wants to open a new establishment and is considering different geographic areas. One of the things this company will do is analyze its database looking for the profile of its best customers and cross-reference them with different sociodemographic data, establishing characteristic patterns and determining where a greater number of these "good customers" are located.
The data warehouse or the data warehouse
When data quantities were not as large as they are now, analysis and reporting could be done using conventional languages such as SQL. But once its growth took off, technology made it possible to build a new architecture called a data warehouse . Its first appearances date back to the late 1980s.
Today, big data and advanced technology have changed the capabilities of these warehouses and have reduced costs, improved performance and reliability, and ultimately, given more value to data.
Applications of data mining
We could say without fear of being wrong that distribution or advertising companies were the first to apply data mining, but in reality today it is used in any area or sector, for example:
-The bank
-Medical insurance
-Education
-Policy
-Medicine
-Science…
The stages of data mining
There is a wide variety of data mining systems and each of them focuses on the typical characteristics of the data they mine or the technique they use.
Although data mining is the main process for obtaining knowledge, the development is broader and is described in several phases:
1-Data preparation . Sources are determined, data is transformed and standardized, corrected and stored.
2-Data mining . Data is classified and grouped and the method to be used is decided.
3-Evaluation . This is where experts analyze the patterns.
4-Diffusion . New knowledge acquired is applied.
In many cases, different objectives may appear. For example, an e-commerce company can use data mining in its business to locate the best customers, how to retain them and keep them happy, but it could also have the objective of building a second predictive model that helps it locate customers who will soon stop being customers.
We have focused on data mining because it is the most important stage of KDD (knowledge extraction) but we want you to be clear that it is part of a broader process.
Finally, it must be taken into account that the evaluation and interpretation phase of the patterns that the algorithms give us is not at all simple. Different criteria are considered and it is necessary to put them in the context of the predictive model and take into account the associated cost of the errors that we could make.
Sometimes, predictions are not a reflection of the real world because we may have omitted some parameters that affect it, which is why it is vitally important to have well-trained professionals in the department. In our Master in Business Intelligence and Data Management we train professionals to fill the positions that large companies demand.
Take a step forward and predict a better future for yourself by enrolling in one of the most sought-after courses.
4-Diffusion . New knowledge acquired is applied.
In many cases, different objectives may appear. For example, an e-commerce company can use data mining in its business to locate the best customers, how to retain them and keep them happy, but it could also have the objective of building a second predictive model that helps it locate customers who will soon stop being customers.
We have focused on data mining because it is the most important stage of KDD (knowledge extraction) but we want you to be clear that it is part of a broader process.
Finally, it must be taken into account that the evaluation and interpretation phase of the patterns that the algorithms give us is not at all simple. Different criteria are considered and it is necessary to put them in the context of the predictive model and take into account the associated cost of the errors that we could make.
Sometimes, predictions are not a reflection of the real world because we may have omitted some parameters that affect it, which is why it is vitally important to have well-trained professionals in the department. In our Master in Business Intelligence and Data Management we train professionals to fill the positions that large companies demand.
Take a step forward and predict a better future for yourself by enrolling in one of the most sought-after courses.
What is data mining?
First of all, we must say that this is not a new term. It is not something we have been using since the technological revolution, but it was with the digital transformation that we realized the need, the potential and the incredible value of data .
The data accumulated by companies or businesses, institutions or even any individual, has become raw material that, once well processed, becomes valuable knowledge for the design of strategies and pursuit of objectives in the field of extracted data.
Data mining and statistics, are they the same thing?
Although it is true that in a first approximation to the term we could find a certain parallelism with statistics, data mining works with a large volume of data and with a different typology. In addition, it pursues a final objective, the extraction of knowledge .
For example, statistics tell us that 16% of women are born blonde, but if we establish a set of rules based on background and other specific characteristics we could predict whether the future baby will be a blonde girl.
In any case, we have to grant statistical science the role of “mother” of data mining, but the big difference is that gambling data malaysia phone number mining does not produce data, but rather new knowledge extracted and generated by the tool in an assisted or automatic manner.
Where does data mining come from?
Out of necessity. The growth of digital databases and the emergence of various sources of data creation has generated the need to learn to interpret them, to know how to study them, in order to predict the future, understand the present and analyze the past.
This system, which could previously be done more manually, is now impossible with the exponential growth of data and it is necessary to use powerful tools.
This is why data mining becomes the method for solving the problem that involves the analysis of current databases.
Let's take an example. A fast food franchise wants to open a new establishment and is considering different geographic areas. One of the things this company will do is analyze its database looking for the profile of its best customers and cross-reference them with different sociodemographic data, establishing characteristic patterns and determining where a greater number of these "good customers" are located.
The data warehouse or the data warehouse
When data quantities were not as large as they are now, analysis and reporting could be done using conventional languages such as SQL. But once its growth took off, technology made it possible to build a new architecture called a data warehouse . Its first appearances date back to the late 1980s.
Today, big data and advanced technology have changed the capabilities of these warehouses and have reduced costs, improved performance and reliability, and ultimately, given more value to data.
Applications of data mining
We could say without fear of being wrong that distribution or advertising companies were the first to apply data mining, but in reality today it is used in any area or sector, for example:
The stages of data mining
There is a wide variety of data mining systems and each of them focuses on the typical characteristics of the data they mine or the technique they use.
Although data mining is the main process for obtaining knowledge, the development is broader and is described in several phases:
1-Data preparation . Sources are determined, data is transformed and standardized, corrected and stored.
2-Data mining . Data is classified and grouped and the method to be used is decided.
3-Evaluation . This is wheData mining, data mining or data prospecting are terms that you have probably heard on some occasion. In today's article we will resolve frequently asked questions and explain in a clear and convincing way what data mining is or what data mining means .
What is data mining?
First of all, we must say that this is not a new term. It is not something we have been using since the technological revolution, but it was with the digital transformation that we realized the need, the potential and the incredible value of data .
The data accumulated by companies or businesses, institutions or even any individual, has become raw material that, once well processed, becomes valuable knowledge for the design of strategies and pursuit of objectives in the field of extracted data.
Data mining and statistics, are they the same thing?
Although it is true that in a first approximation to the term we could find a certain parallelism with statistics, data mining works with a large volume of data and with a different typology. In addition, it pursues a final objective, the extraction of knowledge .
For example, statistics tell us that 16% of women are born blonde, but if we establish a set of rules based on background and other specific characteristics we could predict whether the future baby will be a blonde girl.
In any case, we have to grant statistical science the role of “mother” of data mining, but the big difference is that data mining does not produce data, but rather new knowledge extracted and generated by the tool in an assisted or automatic manner.
Where does data mining come from?
Out of necessity. The growth of digital databases and the emergence of various sources of data creation has generated the need to learn to interpret them, to know how to study them, in order to predict the future, understand the present and analyze the past.
This system, which could previously be done more manually, is now impossible with the exponential growth of data and it is necessary to use powerful tools.
This is why data mining becomes the method for solving the problem that involves the analysis of current databases.
Let's take an example. A fast food franchise wants to open a new establishment and is considering different geographic areas. One of the things this company will do is analyze its database looking for the profile of its best customers and cross-reference them with different sociodemographic data, establishing characteristic patterns and determining where a greater number of these "good customers" are located.
The data warehouse or the data warehouse
When data quantities were not as large as they are now, analysis and reporting could be done using conventional languages such as SQL. But once its growth took off, technology made it possible to build a new architecture called a data warehouse . Its first appearances date back to the late 1980s.
Today, big data and advanced technology have changed the capabilities of these warehouses and have reduced costs, improved performance and reliability, and ultimately, given more value to data.
Applications of data mining
We could say without fear of being wrong that distribution or advertising companies were the first to apply data mining, but in reality today it is used in any area or sector, for example:
-The bank
-Medical insurance
-Education
-Policy
-Medicine
-Science…
The stages of data mining
There is a wide variety of data mining systems and each of them focuses on the typical characteristics of the data they mine or the technique they use.
Although data mining is the main process for obtaining knowledge, the development is broader and is described in several phases:
1-Data preparation . Sources are determined, data is transformed and standardized, corrected and stored.
2-Data mining . Data is classified and grouped and the method to be used is decided.
3-Evaluation . This is where experts analyze the patterns.
4-Diffusion . New knowledge acquired is applied.
In many cases, different objectives may appear. For example, an e-commerce company can use data mining in its business to locate the best customers, how to retain them and keep them happy, but it could also have the objective of building a second predictive model that helps it locate customers who will soon stop being customers.
We have focused on data mining because it is the most important stage of KDD (knowledge extraction) but we want you to be clear that it is part of a broader process.
Finally, it must be taken into account that the evaluation and interpretation phase of the patterns that the algorithms give us is not at all simple. Different criteria are considered and it is necessary to put them in the context of the predictive model and take into account the associated cost of the errors that we could make.
Sometimes, predictions are not a reflection of the real world because we may have omitted some parameters that affect it, which is why it is vitally important to have well-trained professionals in the department. In our Master in Business Intelligence and Data Management we train professionals to fill the positions that large companies demand.
Take a step forward and predict a better future for yourself by enrolling in one of the most sought-after courses.
4-Diffusion . New knowledge acquired is applied.
In many cases, different objectives may appear. For example, an e-commerce company can use data mining in its business to locate the best customers, how to retain them and keep them happy, but it could also have the objective of building a second predictive model that helps it locate customers who will soon stop being customers.
We have focused on data mining because it is the most important stage of KDD (knowledge extraction) but we want you to be clear that it is part of a broader process.
Finally, it must be taken into account that the evaluation and interpretation phase of the patterns that the algorithms give us is not at all simple. Different criteria are considered and it is necessary to put them in the context of the predictive model and take into account the associated cost of the errors that we could make.
Sometimes, predictions are not a reflection of the real world because we may have omitted some parameters that affect it, which is why it is vitally important to have well-trained professionals in the department. In our Master in Business Intelligence and Data Management we train professionals to fill the positions that large companies demand.
Take a step forward and predict a better future for yourself by enrolling in one of the most sought-after courses.