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How to do data mining? Also explains the benefits that can be obtained by using AI tools

Table of contents

  • What is data mining?
  • Purpose of data mining
  • Main methods of data mining
  • Main analytical methods used in practice
  • Steps to perform data mining
  • Benefits of proper data mining
  • It is recommended to introduce AI tools for data mining.
  • summary

In today’s era of advanced information technology and the flow of large amounts of data, data collection and analysis hold the key to business success. However, handling huge amounts of data is not easy. This is why “data mining” is attracting attention.

Data mining is a technology that finds useful information and new knowledge from vast amounts of data, but many people do not know how to do data mining. This time, we will explain the concept and specific methods of data mining.

What is data mining?

Data mining is a method of mining meaningful information from the huge amount of data called big data that cannot be handled by conventional methods. In recent years, with the spread of networks and improvements in computer performance, an environment has been created in which companies and individuals can easily process big data.

Clustering and regression analysis are used as data analysis methods, and models are created using statistics and machine learning (AI). By conducting data mining, you can expect to discover new relationships and obtain hints.

Purpose of data mining

The purpose of data mining is “knowledge discovery” and “hypothesis verification.” Knowledge discovery refers to analyzing vast amounts of data to obtain knowledge such as certain laws, relationships, patterns, and trends, and utilize it for future predictions.

On the other hand, in hypothesis verification, the necessary data is collected and analyzed in accordance with the established hypothesis. Let’s take a closer look.

discover new knowledge

Statistical analysis is performed after forming a hypothesis before performing it. Select the appropriate analysis method based on your hypothesis and the data you will use. However, the purpose of data mining is to discover new knowledge, so it does not formulate hypotheses.

Use the collected data as a starting point to discover new knowledge, useful patterns, rules, and relationships. It is common to use AI, which can perform high-level calculations and discover minute features, to discover relationships that cannot be found by humans and to classify data in new ways.

Aiming to verify hypotheses and solve problems

Data mining involves collecting and analyzing the data necessary to solve the problem that you want to verify, based on a hypothesis that you have established before execution. Expertise in statistics is essential to formulate a hypothesis, but it can be supplemented to some extent by using AI tools.

You can also look for effective ways to solve business problems. In modern business, the use of big data is pervasive, and various analyzes are performed based on the accumulated data. However, there are many companies that are having trouble utilizing and analyzing data, and are aiming to resolve these issues by introducing data mining.

Main methods of data mining

There are a wide variety of data mining techniques. Two typical methods are “statistical analysis” and “machine learning” using AI. Statistical analysis is suitable if the purpose is to prove a hypothesis, and machine learning is suitable if the purpose is to make new discoveries. However, it is important to note that the methods you should use will differ depending on the problem you want to solve with data mining.

statistical analysis

Statistical analysis is a method of analyzing data using statistics and probability theory. It is mainly used for testing hypotheses. For example, we formulate a hypothesis that “drinks sell more when the temperature is high,” and use the derived statistical data to analyze the relationship between the two and verify whether the hypothesis is correct.

Results can be automatically derived using AI tools, but it is necessary to formulate a hypothesis and select the analysis method to obtain the results yourself. Furthermore, from the perspective of forming a hypothesis, statistical analysis is sometimes considered to be different from data mining.

machine learning

Machine learning is a method in which AI searches for relationships in data while learning, without forming hypotheses. By using AI instead of humans, it is possible to discover characteristics and trends that humans tend to overlook. However, even if a relationship is derived from the data, it also has the disadvantage of not being able to clarify the reason. Human judgment is required to determine the causes of certain characteristics or trends in data.

Main analytical methods used in practice

There are many different data mining analysis techniques. Typical examples include “clustering,” “regression analysis,” “logistic regression analysis,” “decision tree analysis,” “market basket analysis,” and “neural network.”

Two of the most commonly used are “clustering” and “logistic regression analysis.” When performing analysis, you will not be able to derive meaningful results unless you use different methods depending on the purpose. Here, we will explain these two analysis methods.


Clustering is an analysis method that classifies data into groups (clusters) based on similarities and devises marketing and other approaches based on the results. It is a type of unsupervised learning in AI machine learning that learns similarities in data from situations where there is no answer and divides it into clusters.

There are two types of clustering procedures: “hierarchical cluster analysis” and “non-hierarchical cluster analysis.” Hierarchical cluster analysis creates a dendrogram from similar combinations and performs more detailed clustering. Non-hierarchical cluster analysis does not create a hierarchical structure, but collects objects with similar properties from a mixed collection and divides them into clusters. Non-hierarchical cluster analysis is commonly used in big data analysis.

Logistic regression analysis

Logistic regression analysis is a statistical method for predicting the association between multiple factors and binary values. It is used to analyze the probability of occurrence of events such as natural disasters. A binary value is a value that has only two possible answers, such as “Yes” or “No.”

In the field of marketing, it is possible to analyze accumulated customer data and product data to predict reactions when sending DMs and products that are likely to sell. In business, it is used when you want to improve customer responses to management measures.

Steps to perform data mining

Data mining is performed in three steps: “data collection,” “data processing,” and “data analysis.” Data mining requires data to be used for analysis. First, collect data that matches the analysis content. The collected data is then processed into a format suitable for analysis. Processing is the process of making the information necessary for analysis easier to view. Once data processing is complete, data analysis begins.

1. Collect data

In order to improve the accuracy of data mining, it is important to accumulate large-scale data. However, just having big data is not enough. In order to perform data mining efficiently, it is necessary to collect data appropriately according to the purpose. It is essential to determine the purpose of data collection and prepare data that matches that purpose.

2. Organize the data you have

The collected data contains “noise”. If the data contains noise, it cannot be directly submitted for analysis. Once data collection is complete, “data cleansing” is required to process and organize the data. By removing outliers and missing values ​​and reducing data variation, smoother analysis becomes possible.

In addition, when organizing data, you should unify the data format to make it easier to analyze, and perform a process called “normalization” to prevent data duplication.

3. Analyze the characteristics of each group

Organize the data into groups, analyze the characteristics of each group, and investigate what kind of properties they have. What is used here is the aforementioned “clustering” and “logistic regression analysis.” These methods are used to analyze data, find rules in the data, and classify data into groups.

4. Examine the relationship between multiple data

Analyze the large amount of collected data from various perspectives and explore and extract correlations between data. In the field of marketing, products that appear to have a weak relationship but are actually often purchased at the same time, products that appear to be related but are rarely purchased at the same time, and the types of products that grouped customers purchase. You can discover whether or not there are any.

In data mining, it is possible to obtain new relationships and knowledge by determining the relationships between data and the frequency of occurrence of events through analysis.

5. Verify the effectiveness

Validation is the process of evaluating the effectiveness of data mining on real data. Before starting operation, it is essential to understand the characteristics and verify the effectiveness of data mining.

After conducting an analysis using data mining, we identify the factors from the analysis results. The collected data is then compared with the analysis results obtained through data mining to verify the effectiveness of data mining and evaluate its accuracy. In addition, reliability can be evaluated by verifying whether it functions in the same way using different collected data.

Benefits of proper data mining

When used appropriately, data mining can help solve business problems and improve business performance. By analyzing data using various methods, it is possible to discover patterns, trends, and relationships between data that were previously overlooked.

Predict future market trends based on analysis results from data mining and take measures that will lead to solving business issues.

Organize your data appropriately

We do not know what meaning the collected data has in its raw form. The important thing here is data organization.

By determining conditions and organizing data, data can be visualized and its meaning can be seen. It is not uncommon for the actual amount of data collected to exceed 1 million units. By organizing it properly, it becomes usable.

Data-driven demand forecasting

Data mining allows you to predict outcomes based on the relationships between data and events. To use a marketing analogy, by analyzing product data, customer data, etc., it is possible to predict when a certain product is likely to sell and which products are likely to become popular.

For example, if umbrellas sell well on rainy days, you can predict that sales will increase during the rainy season. Furthermore, if there are product A and product B that often sell together, it is possible to predict that products similar to product A that are scheduled to be released will sell together with product B. Based on data like this, highly accurate demand forecasting can be achieved.

Discover relationships between multiple data

Data mining allows you to discover new relationships in data that you were not aware of before. Finding relationships in data can be useful in formulating business strategies.

In the field of marketing, importance is placed on relationships such as “product A and product B are likely to be purchased at the same time.” If we can discover this law, we can devise strategies such as displaying product A and product B side by side.

Leads to solving your own problems

When vast amounts of data are simply listed, it is difficult to find solutions to business problems. By using data mining, you can perform detailed analyzes tailored to your needs, such as classifying data and predicting results. If used correctly, it will lead to discoveries that will lead to problem-solving.

For example, in marketing, the total sales amount for each product category and the sales amount of similar products derived through data mining can provide hints for solving problems such as increasing sales.

It is recommended to introduce AI tools for data mining.

Data mining is also possible in Excel. However, it is not suitable for full-scale implementation because it handles a huge amount of data and requires specialized knowledge.

Therefore, we recommend using AI tools. AI tools are equipped with a wealth of functions useful for data mining, including data collection, calculation, and visualization of analysis results. If you can make data mining more efficient, you will have more time to focus on utilizing the analysis results, and you can expect to improve your business performance.

Analyze large amounts of data efficiently

The advantage of introducing AI tools is that large amounts of data can be collected, centrally managed, and analyzed efficiently. This requires multifaceted data, but using AI tools can greatly reduce that effort. You can also efficiently analyze the collected data, saving you time and effort.

Can be analyzed from various angles

It is rare to obtain useful results with one-time data mining. Therefore, it is necessary to examine data from various angles and perform repeated analyses. Examples include changing the analysis method or processing the data.

Trial and error is essential for data mining. However, repetitive analysis can also be easily performed with the introduction of AI tools.

Activate the PDCA cycle

Using AI tools, data mining can be performed even by non-experts. By introducing data mining, it becomes possible to analyze results that previously relied on intuition and empirical rules, and it becomes possible to perform quantitative evaluations based on performance. Being able to predict what will be implemented will activate the PDCA cycle and lead to faster decision-making.

Reduce effort and cost

The amount of data handled in data mining can be enormous. Therefore, it is recommended that you perform analytical tests on partial data, and then perform data mining analysis on large-scale data if it is determined to be effective.

As long as the path to analysis is established, AI can be used to process large-scale data during times when resources are relatively free, significantly reducing effort and costs.

No specialized knowledge required

Data analysis requires difficult expertise. By introducing an AI tool, you can perform data mining on a screen designed to be intuitively operated even without this knowledge. As long as you learn the meaning of analysis and how to use AI tools, there is no problem. You can speed up your business by allowing on-site staff to do the work without having to send it to a specialist.


Data mining is carried out in many business settings, but the results can vary greatly depending on the method used. What is important is how quickly you can resolve business issues with less effort and time. If AI tools can be utilized according to the purpose, it will be possible to produce highly accurate analysis results.



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