“Digital Natives” and the Shogi World Through the Lens of Sōta Fujii – At the Intersection of Tomorrow’s Mindsets and Digital Mastery

The term “digital native” is often used broadly to describe individuals who consider digital technologies as natural and integral to their everyday lives. Typically, it refers to those born in the 1990s and 2000s, who grew up surrounded by digital devices. As this cohort began to emerge actively in society, digital transformation—at both organizational and societal levels—has surged forward.

As of June 2023, when this article was written, the meaning of “digital native” is continuing to evolve and expand. In this piece, we explore how the image of a “digital native” has transformed with technological advances, and what it might look like in the future.

Understanding “Native”

Before diving into the term “digital native,” let’s clarify what “native” itself implies:

  • “Native” means innate or naturally acquired in English. It’s used in contexts like “native language” or “native speaker.”
  • Language acquisition differs significantly between childhood and adulthood. Most people exposed to a language from a young age become fluent—even in subtle nuances—while learning later in life generally demands more effort and rarely achieves native-level intuition.
  • This illustrates that the human brain has a “golden period” for learning, during which acquiring certain skills—including sensory finesse—is far easier.

Traditional Notion of Digital Native

In the 1990s and early 2000s, home PCs and the internet became common. Technology evolved to enable non‑technical users to handle digital data, and email and text‑based communication became routine.

From the late 2000s onward, the rise of video platforms, multimedia-capable social networks, and smartphones with built-in digital cameras led to explosive growth in digital communication.

Generations educated during that era became adept at using PCs and smartphones effortlessly, and quickly adopted new digital tools and services. As a result, they often demonstrate high proficiency in leveraging digital technologies to work more efficiently and effectively.

Digital Natives in the Age of AI

Historically, digital technologies have enhanced human behavior in terms of efficiency—recording, storing, transmitting information. But today, they are increasingly used to elevate human decision-making power. AI and advanced data analytics now provide insights in areas that are hard to articulate algorithmically—intuition-like judgments that humans struggle to define.

  • Machines outperform humans in pure computation and speed, making digitization of data aggregation and analysis a given.
  • Yet newer AI systems offer surprising insights, especially in complex, ambiguous scenarios—often where human intuition is hard to formalize.

In this era, digital natives may be characterized not just by their comfort with technology, but by their ability to embrace information generated by AI and data analysis as part of their decision-making process.

Although the human brain has limits in processing information, individuals who understand both human and AI strengths and weaknesses can leverage them to make more informed, strategic judgments.

A Case in Point: Shogi and the Evolution of Digital Technology

Shogi, the Japanese chess-like game, serves as a clear illustration of how human judgment and digital technology can intersect—and the evolving role of digital natives in mastering that integration.

Early Stage: Efficiency through Digitization

From the Edo period onward, shogi professionals studied under structured systems. But in the 1990s, a turning point came with youthful pros like Yoshiharu Habu, who revolutionized the field by leveraging computers for deep, data-driven preparation.

Previously, record-keeping of games (kifu) relied entirely on paper, making research inefficient. By digitizing kifu and managing them via floppy disks and other media, younger players gained a systemic advantage. These players, dubbed “PC shogi” by some veterans, showcased the power of digital tools.

Here, digital technology supported human efficiency by enhancing preparation and research.

The AI Epoch: Beyond Human Supremacy

By the mid-2000s, strong shogi AI had emerged. By the early 2010s, AI began performing competitively even against top professionals. The 2013 “Denō-sen” saw a professional team face AI in a formal match series—result: 1 win, 1 draw, 3 losses for the human team.

This milestone reaffirmed AI’s strengths but also revealed human advantages in certain areas. By 2017, a match between Meijin Taku Sato (a reigning top pro) and AI marked a symbolic moment—solidifying AI dominance on the public stage.

Since then, pros have used AI not just as competition, but as a sparring partner—referencing AI suggestions to expand their strategic thinking beyond conventional wisdom.

Sōta Fujii: A Prototypical Modern “Digital Native”

Ranked the youngest professional shogi grandmaster in history, Sōta Fujii (born 2002) is widely known for integrating AI into his daily study routine. While many professionals now work with AI, Fujii is uniquely positioned: his formative years overlapped with powerful AI tools already existing, enabling him to absorb AI-generated insights as intuitive judgment.

Rather than memorizing AI-recommended moves, Fujii appears to internalize them—thus empowering his ability to make accurate decisions in novel situations with the same strategic finesse.

羽生善治九段インタビュー “七冠”挑戦…藤井聡太竜王への思い AI時代のプロ棋士とは

Resistance to Rapid Tech Change

Even in shogi—a domain where outcomes are clear and meritocratic—adoption of new tech has not been frictionless. Similar psychological dynamics emerge in business with AI adoption:

Others reach a pragmatic acceptance: once a technology proves superior, collaboration—not competition—becomes the norm.

Experts may feel threatened, believing computers cannot match their in-depth knowledge.

Some fear that AI may render human expertise obsolete, making objective evaluation of technology difficult.

Redefining “Digital Native” for Today and Tomorrow

Modern digital technologies are not merely tools for efficiency—they increasingly inform complex decisions. As such, new digital natives:

  • Understand AI is fallible but useful.
  • Leverage digital tools flexibly to augment human judgment.
  • Remain adaptable and continuously restructure mental frameworks as technology evolves.

The future digital native is not defined by early exposure to digital devices alone, but by the mindset of embracing—and co-evolving with—rapid technological advancement.

Data Analysis with Python

In recent years, it has become much easier to collect and analyze large amounts of data using computers. Methods of utilizing data—such as artificial intelligence (AI)—are rapidly evolving. Today, making business decisions based on thorough data analysis is becoming the norm.

This article provides an overview of Python, one of the most commonly used programming languages for data analysis. We explore why Python is frequently used for data analysis and how it can be applied.

What is Python?

Python is a programming language used to write instructions for computers to execute. While Python itself does not “analyze” data, it provides a foundation for implementing data analysis workflows envisioned by the user.

By writing code following Python’s syntax and structure, users can access a wide range of data analysis tools and techniques.

Why Use Python for Data Analysis?

Here are some key reasons why Python is especially suitable for data analysis:

Rich Library Ecosystem

Python offers a wide variety of libraries created by developers around the world to support tasks like data cleaning, analysis, and visualization. These libraries eliminate the need to manually write complex algorithms, allowing even beginners to perform advanced analyses with minimal code.

python library 一覧

Covers the Entire Data Workflow

Data analysis involves many stages: data collection, cleaning, transformation, analysis, and reporting. Python supports every part of this process. From small datasets to massive data pipelines, Python provides tools to manage the entire workflow.

Beginner-Friendly

Compared to many other programming languages, Python is relatively easy to learn. With its clean syntax and the availability of pre-built libraries, even those new to programming can perform powerful data operations with just a few lines of code.

Abundant Online Resources

Due to Python’s popularity in data science and AI, you can find countless tutorials, troubleshooting guides, and community forums online. This makes it easier for learners to overcome obstacles like error messages or coding bugs.

Pythonエラーメッセージ

Free to Use

Python is open-source and completely free. It can be downloaded and installed from the web with ease, making it ideal for companies or individuals looking to explore data analysis without upfront costs.

Points to Keep in Mind When Using Python

Although Python is a powerful tool for data analysis, understanding its limitations is crucial to using it effectively.

📊 Visualization Requires Extra Effort

While Python supports graph and chart generation, setting up visualizations often requires manually specifying details in code. This can be a hurdle for those used to drag-and-drop interfaces.

🧠 Skill Gaps Among Users

Unlike spreadsheet tools, Python requires a certain level of programming knowledge. If multiple team members work on the same project, communication and knowledge sharing are essential to ensure consistency.

Python vs BI Tools vs Spreadsheets: When to Use What?

Other than Python, data analysis in business settings is often done using BI (Business Intelligence) tools or spreadsheets. Here’s how to decide which is right for you:

✅ Use Python When:

  • You want to start serious data analysis using existing business data.
  • You plan to build custom AI models using your data.
  • You need to perform non-standard, flexible, or experimental analysis.

Python allows for highly customized analysis, particularly useful when standard tools cannot handle the complexity or uniqueness of your data.

📊 Use BI Tools When:

  • Your data is already well-structured and regularly updated.
  • You’re working with business metrics like sales, finance, or marketing KPIs.
  • You need to automate dashboards and visual reports for stakeholders.

BI tools are great for visualizing structured numerical data in dashboards with minimal coding.

🧾 Use Spreadsheets When:

  • Your dataset is small and manageable.
  • You lack technical personnel with Python or BI experience.
  • You need quick and simple calculations with no need for automation or scalability.

However, spreadsheets fall short for large-scale or complex data operations.

Choosing the Right Resources for Data Analysis with Python

Python is an excellent choice for handling complex and large-scale data tasks. However, for businesses new to data analysis, hiring or training talent who understand both your business context and Python programming is a major challenge.

Simply knowing how to code isn’t enough. To gain true business insights, analysts need to understand the story your data tells.

Our company provides support tailored to your business, helping you use your data effectively—even if you are still in the early stages or unsure how to begin. Please don’t hesitate to reach out for a consultation.

Feature of the contents

The Importance of Small “Differences”

Recently, services containing the term “360” such as Salesforce Customer 360, Adobe 360 Degree Customer View, and SAS Customer Intelligence 360 have become increasingly common.

The goal behind “XXX 360” solutions is understood as follows: Unlike during Japan’s period of high economic growth—when products sold explosively and population and buyers increased steadily—today’s market does not grow in such a simple, upward manner. Therefore, the aim is to get to know customers better so they will continue to use one’s products and services over the long term. However, on the other hand, human behavior and thought processes are not so simple as to be fully captured just by recorded data.

A 2018 survey by Groupon on impulsive buying attitudes (*1) yielded interesting results:

  • One in two people make impulse purchases at least once a month.
  • About 60% of people regret their impulse purchases.

From this, we can hypothesize that even if you know your customers well, stimulating purchase and retention motivation may still be difficult.

In other words, what businesses must focus on going forward is to “trigger impulse buying and maintain ongoing communication so that customers do not regret their purchases.”

What is key to encouraging impulse buying and sustaining ongoing communication? We believe the answer lies in content.

Many people have probably experienced making an impulse purchase triggered by a video seen on the street or moved emotionally by a TV commercial.

When customers choose between similar products or services, content acts as the means to create the small “difference” that makes your brand stand out.

Encounters with Customers through Content

For example, when asked, “We want to play videos in a popup store opening next week,” how might you respond?

  • Gather videos from an internal server and create a looped video playlist
  • Upload videos to YouTube and create a playlist
  • Save videos on a USB and hand it over

These on-demand responses are common. If your company uses a Digital Asset Management (DAM) system, video preparation might be smoother. However, from the viewpoint of content that creates a small “difference,” several questions arise:

  • Are the videos compelling enough to attract customers?
  • Do they stimulate purchase motivation?
  • Are they videos that customers barely notice whether they play or not?

Customers visiting stores differ by day of the week and time of day. Even the same customer’s mindset may change depending on weather or season.

If daily life isn’t just a repetition of the same things, then videos that emotionally resonate and cause people to stop and watch will also vary. Providing content that reaches these subtle emotional shifts can make brand-customer encounters much more memorable and special.

In 2018, Chanel raised the challenge of how to enhance customer engagement at popup stores (temporary retail locations). Existing popup stores had become somewhat templated spaces, lacking compelling information, content, or attractions. When building a completely new content platform while leveraging existing assets, the following points were emphasized:

  • Support for regionally appropriate content distribution
  • Support for flexible content changes on-site
  • Support for interactive initiatives

Regarding region-specific content distribution, a digital wall was developed to retrieve and display content via API using metadata tags assigned when content is stored. To enable easy content selection on-site, a management interface accessible via smartphone or tablet was created, allowing staff to change distribution content by setting keywords or selecting images. By decoupling the presentation layer (digital wall) from the content management layer and communicating via APIs, flexible content use that was previously impossible became achievable.

Figure 1: ポップアップストアに設置されたデジタルウォール
Figure 1: Digital Wall Installed in Popup Store

To increase customer interaction, an initiative called “Read My Lips” combined a mobile app and facial recognition technology. This system extracts lip shapes via facial recognition, performs personality analysis based on lip shape, and recommends suitable lip products accordingly. Such interactive technology created an environment more acceptable than one-way staff suggestions for customers unsure about which lip product to choose. It was effective in enhancing customer engagement and attracting attention in-store.

Read My Lipsのモバイルアプリと店内に設置されたディスプレイ
Figure 2: “Read My Lips” Mobile App and In-store Display

“Read My Lips” also uses a decoupled architecture between the presentation layer (mobile app and display) and content management layer, enabling flexible content use while leveraging existing content.

Expanding Support Services with Content

When your hands are full—cleaning, cooking, or repairing equipment—you may want to look up information or operate other devices. Possible options include:

  • Freeing your hands to perform the task
  • Using non-hand methods to perform the task

From a technology perspective, voice-interactive smart speakers like Google Home and Amazon Alexa, and AR/MR headsets like Microsoft HoloLens or Magic Leap that operate via eye or body movements, are increasing. However, brands still struggle to provide adequate content across various devices. One reason is that existing FAQ systems or knowledge portals were designed assuming content is displayed on screens, making adaptation to other devices difficult.

In 2019, German cleaning equipment manufacturer Kärcher launched two Alexa Skills for “Kärcher Smart Home”:

Kärcher Info: answers questions about how to select and use cleaning devices

Kärcher: controls sprinklers and other devices via voice commands

Figure 3: Kärcher Smart Home

Kärcher initially planned to extend their existing FAQ system but faced challenges:

  • Expected questions were too long to be effectively used as conversational data
  • Answers were too lengthy, making it hard to provide concise responses

They redesigned the FAQ from scratch, building a platform capable of multilingual and multi-device content delivery. The redesigned FAQ is stored multilingual in the content platform and communicates with the presentation layer via API, enabling rapid release of skills for multiple languages. The content can also be accessed via a Smart Home app, allowing seamless support for device operation and settings not only by voice but also through the app interface.

Whether impulse or purposeful buying, maximizing post-purchase support experience is critical to keeping customers valuing your services. Being able to control devices and ask usage questions by voice may seem like a small “difference” for brands with extensive web help pages. However, providing content suited to diverse customer needs creates better experiences. Addressing the diversification of needs demands advancing from conventional screen-based content management to more flexible management.

The Value Delivered by Headless CMS

Cases like Chanel and Kärcher demonstrate that traditional web-centric content management struggles to keep up with technological changes and providing compelling content.

When thinking about “continuously delivering content in a manner suited to the times,” utilizing Headless CMS is one solution.

Headless CMS decouples the presentation layer from the content management layer, with communication via APIs. This architecture allows content delivery independent of UI, as in Kärcher’s case. Content management via APIs also enables secure and flexible integration with existing systems.

The Potential of Headless CMS “Contentful”

In recent years, “Contentful,” a German-born Headless CMS, has gained attention among Western brands. Unlike CMS designed to create pages, Contentful is built as an API-centric content platform.

For example, when displaying images in an app, it is desirable not just to fetch stored images but to obtain images resized, cropped, or modified to suit the display environment. Contentful provides APIs to support these needs, allowing apps to retrieve images in the desired format simply by calling APIs. Various other APIs support flexible content use as a platform.

Figure 4: Conceptual Image of Contentful Usage

While making the most of what you have is important, investing “time for ingenuity” to create small “differences” using forward-looking technology will likely lead to increased brand value.

Reference *1: Survey on Impulse Buying Attitudes, Groupon Japan, 2018


Contributor:Hidenobu Sakata