The unlimited possibilities that data weaves

Thinking through what I want to do

I first learned about Meiji Gakuin University through the Off-Campus Program Compendium, an extracurricular activity I was involved in during high school. By participating in business contests, volunteer work, entrepreneurship programs, and other extracurricular activities, I realized I wanted to make those activities more accessible to high school students, regardless of their region or school, so I joined their operations team. The Off-Campus Program Compendium is a website that provides information on extracurricular activities for junior and senior high school students, and it was there that I learned the basics of HTML, CSS, and other skills that are necessary for creating web pages. I met a Meiji Gakuin graduate through those activities, which led to my visiting Professor Mitsutoshi Otake’s Department of Business Administration seminar, from which that student had graduated.

In high-school courses, there’s a strong sense of the teacher imparting knowledge on the students, but in college courses, there’s more of a sense of the instructor and the students informing each other. I became very interested in Meiji Gakuin because of the instructors I saw there, having eye-to-eye discussions of issues with their students and taking a caring approach to helping them.

After that, I started thinking through what I wanted to do. I have always had a strong interest in education. In addition to my own experience, I have been exposed to data showing that learning opportunities are closely linked to household income, and from my contacts with elementary, junior high, and other high-school students, I wanted to create systems that allow all children to choose a future based on their own criteria, regardless of their birth or environment. But I didn’t want to just yell about wanting to do that; I wanted to actually build mechanisms for such things. I strongly felt that economics has the potential to do this. But creating them would require learning the basics of such mechanisms (markets) through macroeconomics and other studies. With that in mind, I enrolled in the Department of Economics.

Learning is not a goal; it’s a method for answering questions

I resolved problems I encountered in my internship during my college classes and my instructors’ office hours. I did things like that every day after entering college. My internship, which I started at the same time as entering college, was at Learning for All, a certified nonprofit organization that conducts learning support activities for children. I started working there as a way of learning about the mechanisms of education, as mentioned above. There, I learned about everything from how to teach arithmetic involving negative numbers to the serious concerns of single-parent households. I wanted to know how we can identify various concerns and their contributing factors, as well as how we can resolve those problems and the questions that will lead to their resolution. That desire also provided motivation for studying in my courses.

I learned a great deal from Professor of Economics Takayuki Oishi during his office hours. When I took his Microeconomics course, which teaches the fundamentals of economics, he answered my questions in a friendly manner, and I increasingly sought his advice during office hours. He answered my questions about everything from my studies to advice on which seminar I should enter to worries about my future, often in long, carefully written messages he sent me through manaba (the University’s online learning support system). I also developed the good habit of questioning events, asking myself what Professor Oishi would think whenever I saw news reports about topics such as subsidies in support of elementary school closures due to the COVID-19 pandemic and other financial issues the government faces.

I think I am now able to better enjoy my daily studies because I’ve come to consider learning not as the end goal, but as a way of answering my questions. In my microeconomics course, for example, the depth of learning will vary greatly depending on whether the student has questions about markets, and if they really want to know the answer. Realizing that was one of my greatest discoveries in my first years at college. During my first two years of college, I had an internship that involved planning and designing exploration programs and classes for high school students in parallel with learning support activities for younger children, and that took up a lot of my time every day. Then, especially from spring semester of my third year, when the COVID-19 pandemic started settling down and we got more face-to-face classes, I entered the University first thing in the morning and studied until just before the gates closed (20:30 at the Yokohama campus, 22:00 at the Shirokane campus). It was a physically demanding schedule, but I was fascinated by the process of using what I’d learned in my university courses to solve questions and problems in the field of education that I’d acquired as an intern, and I spent two years truly absorbed in that process.

Finding data analysis to be incredibly interesting

In 2022 (my third year), I joined Naomi Kodama’s seminar in the Department of Economics. The main theme of that seminar is how to use data analysis to evaluate national and local policies. For example, say you have an “A implies B” hypothesis. Does that mean A is the only factor that causes B? Couldn’t there also be some other factor C? We used a statistics software package, Stata, to set up and analyze such hypotheses, allowing us to summarize our conclusions and give team presentations. In spring semester, we read papers on fiscal policies that affect an aging population and worked as a team to improve our proficiency in performing empirical analyses. Our current theme of studies (as of October 2022) is factors behind future insecurity among young people and measures to prevent them. Since the factors that create anxiety can have diverse backgrounds, we must proceed with a step-by-step analysis. The first step is to analyze attributes such as educational attainment (high school, college), gender (male, female), and employment status (full-time, part-time). From the results of that analysis, in the second stage, we utilize concepts from frameworks such as logic trees and MECE to dig deeper into factors that, for example, lead a person to become a full-time or part-time employee. I consider it important to have specific questions or themes you want to learn about or solve, and to take an active attitude toward their solution. Here, too, my experience with learning support activities for children was useful.

Winning a data analysis contest

In September 2022, I teamed up with a high-school friend to compete in a data business creation contest, the Digital Innovators Grand Prix. This contest, which is sponsored by the Data Management Creation Lab and Consortium at Keio University, is hosted under the concepts “Evolving the world for the better,” “Harnessing the power of digital technologies,” and “Collaboration between corporations, academia, communities, government agencies, generations, and perspectives.” Participants compete to extract new knowledge and create value by utilizing vast amounts of data. I first learned about the contest when my friend invited me to participate, an invitation I accepted because I wanted to try applying the data analysis skills I had learned in my seminar in a situation outside of school, and because I wanted to take on a new challenge. The theme of the contest was “Wisdom for surviving in an era of 100-year lifespans: Disease prevention and happy lives.”

We took on the novel challenge of creating a business plan based on the use of prescription data for 150,000 lifestyle disease patients. In our analysis, we first focused on the age of disease onset by gender. We took this approach because when we analyzed employment issues by gender at the University, we saw different trends for men and women. As we performed analyses by gender and age, we found a large uptick in people who develop lifestyle-related diseases in their forties and fifties, but we also noticed a surge in younger age groups, those in their teens and twenties. That seemed strange to us, so we conducted interviews with elementary school teachers, school lunch cooks, and others who work with children daily to learn why that might be the case. We thought that while data can reveal facts, qualitative approaches are also important to transform understanding into conviction. The conclusion that our interviews led us to was that children have a low awareness of health issues. When children suffer from lifestyle-related diseases, they may need to restrict their diet and behavior.

As I wrote above, I want to create a system that allows all children to choose a future based on their own criteria, regardless of their birth or environment. This educational theme I had taken on also helped, making me even more enthusiastic about each step, from analysis to thinking through our business plan. Later, however, I had an experience that made me even more enthusiastic: from August 28 to September 14, I went to Vancouver, Canada for an exchange study program. While my flight was crossing the Pacific, during our meal a woman seated diagonally in front of me had a seizure and fainted. The cabin was in a turmoil, with panicking passengers and calls for a doctor. Thankfully a doctor from Taiwan was able to handle the situation, but I was forced to admit that while I was gaining experience through education that would help people far away, I was unable to help someone right in front of me. Having this truth forced upon me made me feel a sense of helplessness. But even so, that experience made me realize that by utilizing medical data, even someone like myself might be able to help people closer to me. I returned to Japan one week before the deadline for submitting our business plan. I spent sleepless nights trying to come up with one.

「第15回データビジネス創造コンテスト」当日の発表資料

Our team was one of approximately ninety contest applicants, but we were selected as one of the eleven who would go on to the actual competition, and we ended up winning the Excellence Award (equivalent to a second-place finish). When they announced the winners, I couldn’t believe we had received an award. My self-evaluation of the accuracy of our analysis and preparation of our slides left me with the impression that our team was less thorough than some of the others, but we received particular praise for focusing on young people in their teens and twenties. Also mentioned were that earlier interventions for the prevention of lifestyle disease are more effective, and that we could expect growth of the teens-to-twenties market. Teens are also the primary target for the educational activities I’ve been involved in, so I felt as if those efforts too were being praised.

Most gratifying of all was after the competition, when we received compliments from those who had come to watch and their ideas for realizing our proposal in the future. I was particularly happy when one person with experience in child placement support told us, “Problems related to children are deep-rooted, so we’re very happy to see you focusing on these issues, and we look forward to seeing what you’ll achieve in the future.” Those words made me keenly aware of the possibilities, and even the necessity, of realizing a better society through medical data.

The unlimited possibilities that data weaves

Before learning about data analysis, I tended to focus more on qualitative factors, such as comments from those in the field. Qualitative results are important, too, because they contain feelings. Quantitative results obtained through data analysis are furthermore an embodiment of their thoughts and enthusiasm. But if we can handle data and put a system in place, it may allow us to reach out to and help people we otherwise may never have been able to interact with. I am convinced that data has unlimited possibilities. I am currently planning to enter graduate school to further explore those possibilities. I hope to continue pursuing my studies in the field of education, which I have been involved in, but I also value encounters in areas that are new to me, like business contests. The keyword for my studies will be data. I will continue to value a sense of excitement as I forge my future.