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November 17, 2023

Realizing a Recycling-Oriented Society through a Data Mining Approach

In today's world, where vast amounts of data are exchanged, data mining, which has been the focus of much attention, is an indispensable method for identifying trends and analyzing relevance and tendencies from collected data.
 Such a data mining approach is also expected to contribute to the goal of achieving a "sustainable society."
 In this issue, we will explain the objectives of data mining, the scenarios in which it can be used, and NTT's efforts in this area.

What is the purpose of data mining?

Image: What is the purpose of data mining?

【Purpose of Data Mining】

The purpose of data mining is to utilize information while collecting and analyzing vast amounts of data. Information obtained through data mining can be used not only to provide hints for future predictions, but also to calculate the probability of a particular event occurring.
 Because of the large volume of data handled by data mining, it can be a great ally in business if it is used well. In fact, in the business world, data mining is not only used to forecast sales and identify potential risks, but also to formulate hypotheses and observe market trends.

【Specific Methods of Data Mining】

Data mining involves collecting data, preparing it, storing it appropriately, and analyzing it.
Here's an example of a specific method:

(1) Collecting data

The first step in starting data mining is to collect the data to be handled. At this point, it is important not to exclude any data because of differences in numerical values or because the data is old.
 In data mining, the more data you handle, the more credible your analysis will be. In other words, it is important to focus on the volume of data to handle.
 Also, during the data collection phase, avoid processing or editing data unnecessarily.
 It is important to collect a wide variety of data in an unprocessed state.

(2) Data cleansing.

Data cleansing is the process of preparing the collected data.
 Simply collecting data is not enough to utilize the data for analysis, so data cleansing is necessary to make the data easier to utilize.
 The main method of data cleansing is to set data cleansing rules in advance and delete or modify data accordingly. It is also recommended to use tools that automatically organize large volumes of data nowadays.

(3) Storing data

Once the data has been prepared for easy analysis through data cleansing, it is time to store it.
 While it is common to store original data and data for analysis separately, data extraction time can be reduced by utilizing a "data warehouse" that can store data to be used for analysis.
 Furthermore, even if the amount of data is huge, the advantage is that it can be stored without problems due to the large storage capacity.

(4) Analyze the data

Once the data storage process is complete, analysis can begin.
 Data mining is performed using analysis methods that match your company's objectives.
 The purpose of data mining is not just to stockpile data, but to use the data to its fullest extent.
 Try to use the analysis results obtained from data mining for marketing and sales, et cetera, as appropriate.

Scenarios in which data mining can be used

Image: Scenarios in which data mining can be used

Data mining can be used in any situation where data is handled, with no restrictions on where or in what fields it can be used.
 For example, in the case of automobile insurance, it is possible to design appropriate insurance services based on data such as region and mileage. In customer management, it is possible to analyze past problems and complaints to improve service. In sales, it will be possible to investigate customer demand and provide products at the appropriate time.

The following cases are examples of data mining applications.

(1) Financial Industry

In the financial industry, data mining enables loan screening, insurance, and credit card fraud prevention. It makes it possible to decide whether to provide loans based on personal data or to prevent life insurance policy cancellations using canceled data. It also helps to detect unauthorized use of credit cards based on data such as the amount of money spent, information on stores where credit cards are used, and dates of use.

(2) Manufacturing Industry

Data mining is also having a significant impact on the manufacturing industry.
 In recent years, the advancement of digitization has increased the complexity and sophistication of products, placing a heavy burden on manufacturing sites. However, data mining has enabled the timely detection of malfunctions and maintenance of manufacturing equipment. It has also made it possible to predict power usage in manufacturing and environmental forecasting.

(3) Education Industry

Data mining and the education industry are a promising combination.
 By providing children with digital devices, it will become easier to collect data, which will be used to improve education and support learning. The data is expected to be used to provide learning support suited to each student based on learning data, and to reduce the burden on teachers through automated grading, and lead to curriculum development based on collected data.

What data mining can solve

Image: What data mining can solve

Data mining deals with large amounts of data and can lead to solutions to problems faced by companies and workplaces. Specifically, we will explain what issues can be solved.

【We have data, but are not utilizing it】

Data mining can pre-process and group vast amounts of data, facilitating its utilization.
 Since analysts and specialized staff are not needed and anyone can analyze the data, it is a great advantage for companies that are facing issues such as, "We don't have specialized staff, so we can't actively utilize the data." In addition, by creating analysis scenarios for each site or department, it will be easier to utilize data more efficiently.

【The time and effort required for analysis is burdensome】

One of the issues that can be solved by data mining is the time and effort required for analysis.
 Data mining does not require you to spend time acquiring and analyzing data. Data to be checked can be narrowed down to specific criteria and can be templated. Templated data can then be distributed at the desired date and time by configuring the distribution settings, allowing for progress in data analysis without spending much time and effort.

【There is little know-how based on actual results】

Data mining can solve this problem.
 In business, it is necessary to promote business based on actual results and by repeating the Plan-Do-Check-Act cycle. With data mining, it is possible to predict and measure effectiveness based on more reliable data, rather than on uncertain factors such as intuition and experience, making it easier to predict more accurate results.

How data mining can be used for a sustainable society

Data mining can be utilized in the "circular society" that we are striving for around the world.
 Let's take a look at how collecting and analyzing vast amounts of data relates to a recycling-oriented society.

【What is a "circular society? 】

A recycling-oriented society is one in which limited resources are used sustainably and waste is minimized.
 The cycles of mass production and mass consumption, as well as mass disposal, which are considered problematic today, not only place a heavy burden on the environment, but are also believed to cause pollution and stagnate economic growth. Commonly known initiatives include resource reuse, recycling, and conservation of non-renewable resources such as energy and water.
 Data mining is considered to be an important method for moving toward a recycling-oriented society.

【How data mining can be used for a sustainable society】

Data mining can be used to help realize a recycling-oriented society.  For example, based on the results of data mining, it is possible to find ways to improve the efficiency of resource use and reduce waste.
 Another option would be to link this to the formulation of waste management strategies to reduce waste, or to incorporate data mining into the development of new business models to create a recycling-oriented business model.
 Furthermore, it will be easier to launch a business related to product reuse and recycling based on customer demand and purchase history.
 Depending on your ideas, data mining can be utilized in a recycling-oriented society by relating it to your company's industry and the services you provide.

NTT's efforts to realize a sustainable society

In May 2022, NTT East Group opened the NTTe-City Labo, a field for experiencing the realization of a recycling-oriented society. We are working to realize a recycling-oriented society for the region in various fields such as smart agriculture, drones, e-sports, and digital art.

NTTe-City Labo has three characteristics.

・The first is "Reality"

The Labo is equipped with the latest technology, including state-of-the-art agricultural greenhouses and urban biogas plants, and other equipment and materials used in actual industries. Visitors can experience the actual products and materials that cannot be conveyed through materials, videos, or online, with all five senses.

・The second is "Sympathy"

NTT East Group's efforts to solve social issues have been developed in cooperation with local residents and partner companies. This section introduces the real-life examples of NTT East Group employees who worked together with local residents to solve issues, rather than just having theoretical discussions.

・The third is "Co-Creation"

The Co-Creation Center can be used as a base for distributing information on various initiatives and the latest technologies related to a recycling-oriented society that links communities and regions, and communities and companies. We aim to solve regional issues and create businesses together with local communities and companies.

As of October 2022, 14 facilities are available. Here we introduce some exhibits that utilize data analysis.

Predicting Harvest Yield with AI - Making the Supply Chain Smart

Image: Predicting Harvest Yield with AI - Making the Supply Chain Smart

The exhibit features a system that uses AI to predict the future yield of crops based on environmental data from the field and video data captured by cameras.

Remote Farming Cockpit - Data Driven Agriculture in Practice

Image: Remote Farming Cockpit - Data Driven Agriculture in Practice

The exhibit features a base that provides cultivation guidance to local farmers remotely by utilizing high-definition, real-time images of crops and data such as temperature, humidity, and CO2.

Physical Condition Management and Heat Stroke Prevention - Achieving Business DX through Health Management

Image: Physical Condition Management and Heat Stroke Prevention - Achieving Business DX through Health Management

The sustainable corporate health management, improvement of work style, and business DX by real-time data collection and analysis of vital data are exhibited with actual equipment.

Sleep Tech - World Peace through Sleep

Image: Sleep Tech - World Peace through Sleep

The smart sleep solution that combines data analysis technology cultivated through Japan's leading information solutions with medical science is exhibited, along with actual equipment. About 150 cases have been exhibited at NTTe-City Labo, with a total of 1200 visitors from municipalities and companies as of October 10, 2022.
 Some visitors commented that they had been unsure of what exactly to do in the various fields of regional recycling-based society and smart cities, but now that they have seen actual examples and initiatives, they understand what issues they need to address in their own communities.

We hope you will stop by the NTT e-City Labo, where you can experience data mining and recycling-oriented society initiatives.

At the NTT R&D Forum 2023, we will also be exhibiting future forecasting technologies that focus on natural capital and abundance, to realize inclusive sustainability.

NTT R&D Forum 2023 other window

NTT's departments and research institutes involved in these efforts

NTT e-City Labo (NTT Central Training Center) other window
1-44 Irima-cho, Chofu-shi, Tokyo 182-0004
Odakyu Line
From Shinjuku Station to Seijo Gakuen Mae Station: 15 minutes
Odakyu Bus from Seijo Gakuen Mae Station: 10 minutes
Keio Line
From Shinjuku Station to Tsutsujigaoka Station: 17 minutes
Odakyu Bus from Tsutsujigaoka Station: approx. 10 minutes

If you have any questions or would like to visit the site, please contact an NTT sales representative.