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Data mining and machine learning are often confused, but their essence is different. However, both are essential for improving company-wide productivity through DX conversion.
Therefore, this time we have summarized the commonalities and differences between data mining and machine learning. We will also introduce specific usage examples, so please consider whether it can be used in conjunction with your company’s business content.
What are data mining and machine learning?
Data mining and machine learning are both digital techniques that utilize data. Although they have similar elements, they are strictly different in essence. Here, we will review the overview of data mining and machine learning, and explain their essence and differences.
Data mining | Technology to extract useful information
Data mining refers to technology for extracting useful information from huge amounts of unexamined data such as big data. Extracting information through data mining is not just about classifying data. We analyze the relationships between data and even make future predictions based on that data.
The future prediction referred to here is to predict the probability of occurrence, such as “This product will sell at this rate to this customer group in the future” based on “buyer data.” The essence of data mining is the ability to obtain highly accurate knowledge based on such data.
Machine learning | Technology that gives computers automatic learning ability
When you think of technology that gives computers the ability to learn automatically, artificial intelligence (AI) may immediately come to mind. However, strictly speaking, machine learning is positioned as one of the technologies used to build AI.
Unlike data mining or AI, machine learning does not require the creator to reveal surprising knowledge. It is a technology that is good at repeating what has been learned manually. The essence of machine learning is this kind of “automation,” in which something once taught is accurately repeated over and over again.
What data mining and machine learning have in common
Data mining and machine learning have something in common, as both are digital techniques that can be used to process data. Let’s dig into these commonalities and deepen your knowledge of data mining and machine learning.
Analyze large amounts of data
Both data mining and machine learning can analyze large amounts of data. Both apply built-in algorithms to perform analysis.
Machine learning techniques can be incorporated into data mining and vice versa. Although both are good at analysis, they have different essences, so by combining them, you can aim to improve accuracy and processing speed.
Use quantitative analysis methods
Both data mining and machine learning use quantitative methods for analysis. Therefore, the advantage is that highly accurate analysis results can be obtained under certain conditions.
However, it is not possible to analyze qualitative data that is difficult to translate into numerical values. If you need to analyze qualitative data, you will need to use “text mining” technology, which has evolved significantly in recent years.
Differences between data mining and machine learning
Please check the table below for the differences between data mining and machine learning used in work.
Data analyzed through data mining is primarily used for business strategy. Therefore, data mining tends to be used only as a means of marketing.
Machine learning is used to automate tasks that were previously done by humans. Automation can be expected to reduce human costs and improve customer convenience.
Examples of data mining usage
Data mining is often used for “analysis of customer data”, “maintenance of production equipment”, and “analysis of medical data”. Here, let’s take a look at specific use cases for each and understand what kind of benefits can be gained by introducing data mining.
Analysis of customer data
Analysis of customer data through data mining is used in a wide range of fields, including the sales and service industries. Analyze customer behavior patterns and characteristics (basket analysis), such as what kind of customers have purchased what products and services.
It is also possible to perform “cluster analysis” to classify multiple customer data, and “logistic analysis” to predict sales volume from data such as sales products and sales revenue.
If you formulate a business strategy based on the content of these analyses, you will be able to conduct accurate advertising, promotions, and purchasing, which will also lead to increased sales. It can also be used to select good customers.
Maintenance of production equipment
Data mining technology for production equipment is mainly used in the manufacturing industry. By analyzing basic data on production equipment, frequency of use, age, and other information, it is possible to visually check the aging of equipment.
This will make it clear when maintenance is required, which will help prevent accidents and other risks. Additionally, knowing the maximum operating time suitable for the equipment will improve work efficiency.
Analysis of medical data
Data mining technology is also beginning to be used in the medical field. A typical example is data mining, which enables quantitative understanding of disease trends by analyzing past clinical data.
This is a typical example of combining data mining with statistical methods, but at present there is still little data to base it on, and it is still in the process of development. However, if more hospitals adopt data mining in the future, more reliable analysis will become possible.
Examples of using AI technology using machine learning
Machine learning is often used for “chatbots,” “image/speech recognition,” “future prediction,” etc. Here we will explain the use cases of each and what benefits can be gained by introducing machine learning.
chatbot
Chatbot is a technology in which robots automatically respond to inquiries from customers via chat. There are two types of chatbots: AI-equipped types that improve answer accuracy through self-learning, and non-AI-equipped types that respond based on existing data, but in recent years, AI-equipped types have become mainstream.
As a result, chatbots of various types are increasing, not only chatbots that respond to simple inquiries, but also ones that can identify products that are suitable for customers, have conversations, and consult with customers about their concerns. It can be used not only for the purpose of reducing personnel costs, but also for a variety of purposes, such as developing new services and cultivating new customers.
Image/sound recognition
Facial recognition engines and AI assistants that have become rapidly popular over the past few years use AI that utilizes machine learning. Another memorable use case is the appearance of “electronic declaration gates” at six major domestic airports that allow customs procedures to be completed using just facial recognition.
Image and voice recognition are of course useful for such customer services, but they are also useful in general business operations. For example, meetings and phone calls can be read using a speech recognition tool and converted into text.
By automating the huge amount of work that used to be done manually, human costs can be reduced.
future prediction
Prediction of the future is a specialty of data mining, but even machine learning can make future predictions by training algorithms that specialize in time series. Specific examples include stock price forecasts and sales volume forecasts. AI that incorporates various technologies, including machine learning, can make even more accurate predictions of the future.
Machine learning can also be used as a means of data mining.
Since data mining and machine learning have similar properties, machine learning is sometimes used as a data mining method. In some cases, AI with machine learning is incorporated, and by entrusting AI with processes that would otherwise have to be done manually in basic data mining, it is possible to further reduce human costs. In addition, disadvantages such as human error and subjectivity can be alleviated.
In today’s world where consumer behavior is diverse, the use of big data and DX are necessary in every industry. As a stepping stone for future investment, it is essential to first introduce tools that enable machine learning and data mining.
summary
Although data mining and machine learning are different in nature, they are characterized by their high affinity. In order to survive in the future, we will need to utilize both in our management strategy.
