With the development of AI, there are more opportunities to hear the keyword “data mining”. It is expected to be used for smooth management and sales, and I think some companies are considering introducing it. Although there are challenges in data mining, there are many parts that can be solved by introducing tools using AI. This article explains from the basic knowledge of data mining to the challenges in implementation.
What is data mining?
Meaning of data mining
Data mining is the search for useful information, patterns, and causal relationships from a large amount of data by making full use of data analysis techniques such as statistics and machine learning.
It is called this because it uses big data to mine useful information. Today, it has become an indispensable analytical tool for utilizing big data in marketing and sales.
History of data mining
The origin of data mining is “Knowledge Discovery in Databases”, a process that seeks to find knowledge from databases that appeared around 1989. After that, data mining continued to develop as the performance of computers improved and a large amount of data could be stored. In the 2000s, it became possible for ordinary households to always connect to the Internet, and the amount of data stored on the Internet increased at an accelerating rate. Currently, various companies, mainly IT companies, are developing and introducing data mining systems as a method for analyzing data.
Types of data mining and information that can be extracted
There are two types of data mining and four types of information that can be extracted.
Types of data mining
Data mining can be divided into two types: “knowledge discovery” and “hypothesis testing”.
“Knowledge discovery”: From the collected data, we automatically search for knowledge such as new patterns and rules that are useful to the company. The feature is that no hypothesis is prepared in advance. It is an effective means for big data, and machine learning is often used.
“Hypothesis verification”: We collect necessary data and analyze whether the hypothesis made in advance is correct according to the problem or event to be verified.
What can be extracted by data mining
The profits obtained by performing data mining can be organized into four categories: “data”, “information”, “knowledge”, and “wisdom”.
Data: Numerical values that have not been organized or classified, or unstructured character strings
Information: Those that have been organized or classified for “data”
Knowledge: Trends and knowledge that can be obtained from “information”
Wisdom: “Knowledge” The power of human judgment using
Challenges in data mining
There are no professional employees
The existence of data scientists who are familiar with data analysis is indispensable for data mining, which targets a huge amount of data and has specialized analysis methods. However, there may be times when your company does not have staff with such expertise. Even if we hire people, it is difficult to find human resources with specialized knowledge because the annual income is high and the absolute number is small.
Data utilization does not work
It’s a common story for data mining companies to have data but not use it well because of the sheer volume. .. If you can’t analyze it, you just collect data, and you don’t get the results you want with data mining, there is a possibility that data utilization itself will be hindered.
Data analysis is time consuming and costly
When you actually start data mining, you will spend a lot of time and effort on data acquisition and analysis. In some cases, the burden on the site will increase and labor costs will be extra.
Challenges when not doing data mining
In an era when big data utilization is being called for, it is essential to efficiently obtain information to advance business in an advantageous manner. Leveraging data mining can lead to the discovery of business tips and challenges that previously buried humans may not be aware of. From here, I will explain what happens when you do not do data mining and possible problems.
Know-how is not accumulated
Within the company, the know-how possessed by each employee is not shared, and there are tasks that are personalized. Unless we analyze text data that contains a lot of useful information such as daily business reports and work reports, know-how will not be accumulated and it may lead to overlooking issues and problems in internal operations. ..
Customer data cannot be analyzed
Understanding customer needs is important in marketing. By analyzing customer data, it is possible to develop products that encourage customers’ purchasing motivation and effectively promote them. Inadequate analysis may not be sufficient to address issues such as customer churn, lack of repeaters, and inability to increase customer satisfaction.
Sales are sluggish
If you can’t analyze customer data and purchasing data, you can’t come up with measures that will lead to sales. For example, if data mining can find products that can be sold at the same time, which was previously unknown, it is possible to promote migration within the store by bringing the sales floors of the products closer together or intentionally arranging them far away. By discounting only one of them and selling the other at the regular price without discounting, it may lead to an increase in sales. The optimal approach may not be possible due to the lack of analysis of purchasing information, which may affect sales.
Data mining issues can be solved by introducing AI tools
The advantages of introducing a data mining tool are as follows.
- Anyone can analyze, no specialized staff required
- There is a hint to find a problem from a huge amount of data
- Reduced time and effort spent on analysis
- Accumulation of business know-how is possible
- Detailed customer analysis and sales analysis are hints for improving business performance
Points for utilizing data mining tools
Currently, various tools for data mining have been released. There are three points to keep in mind when choosing a tool.
- Carefully select the data used for analysis
- Clarify the purpose of introducing the tool
- When installing for the first time, choose one that is easy to operate
Carefully select the data used for analysis
You don’t just have to have a lot of data. Data mining can be used even if the amount of data is small. If there is too much data, it will be difficult to extract only the necessary information, so it is important to carefully consider what information you want before selecting and reading the data.
Clarify the purpose of introducing the tool
It is necessary to clarify the “purpose” of what to do when introducing data mining. For example, if you want to improve the efficiency of your business and if you want to increase the purchase rate of products, the data you need and the tools you should choose will differ depending on your purpose.
When installing for the first time, choose one that is easy to operate
It is important to add the user interface (UI) -related parts such as data handling and expression method to the judgment criteria when choosing a tool. The point is that the operation is not too complicated so that anyone can check the extracted information.
Data mining issues are solved with TRYETING’s AI tool “UMWELT”!
There is great potential for utilizing data mining tools. With the no-code AI cloud “UMWELT” provided by TRYING, you can expect the introduction effect because you can use the AI engine that has already been proven. Since “UMWELT” is always equipped with more than 100 algorithms, we can build a highly accurate data mining system using AI in a short period of time. It will be a ready-to-use tool for companies that want to introduce it immediately.
Summary
In this article, I explained the challenges of data mining. By introducing a data mining tool, you can save the trouble of analyzing customer/sales data and realize smooth management/sales. Please use data mining for your business.