8 Reasons People Say “Don’t Become a Data Analyst

Have you heard people say “don’t become a data analyst” and found yourself hesitating about a career change?

In fact, a 2024 survey by the Japan Data Scientist Association found that job satisfaction among active practitioners had dropped to 37%—down 9 points from the previous year—and that the percentage feeling optimistic about the field’s future had hit a five-year low.

At the same time, the average annual salary stands at ¥7.4 million, and the profession is expected to face a shortage of up to 790,000 professionals through 2030, making it a highly in-demand field.

In this article, we break down the reasons behind the “don’t do it” warnings—and what the data actually says about the future of the profession—based on publicly available data from official sources.

1. 8 Reasons People Say “Don’t Become a Data Analyst”

Despite its glamorous image, data analytics comes with serious challenges that practitioners face every day. Here we examine 8 specific reasons why people warn others away from the profession.

A 2024 survey by the Japan Data Scientist Association found that job satisfaction among active practitioners had plummeted to 37%—a drop of 9 points from the prior year—while only 78% felt positive about the field’s future, the lowest figure in five years.

(Source: Japan Data Scientist Association, “2024 Survey of Individual Members”)

80% of the Job Is Tedious Data Preprocessing

Contrary to the exciting image of “data analysis,” the reality is that roughly 80% of a data analyst’s work consists of unglamorous tasks: extracting data with SQL, handling missing values, and standardizing formats.

Building machine learning models and generating strategic insights make up only about 20% of the job.

According to the Ministry of Health, Labour and Welfare’s job information site (job tag), under occupation classification code “418,” “data collection, processing, and organization” is explicitly listed as a core duty.

(Source: Ministry of Health, Labour and Welfare, “job tag (Occupational Information Site)”)

The Learning Curve for Math, Statistics, and IT Is Steep

Practical work requires statistical knowledge at roughly the level of the Statistics Certification Grade 2 to Pre-Grade 1, including probability, calculus, and linear algebra.

On top of that, mastering a broad range of technical tools—Python, SQL, BI tools—is essential, and many people burn out trying to learn it all independently.

(Source: The General Foundation for the Promotion of Statistical Quality Assurance, “Statistics Certification”)

Caught in the Middle Between Business and Engineering

From the business side comes pressure like “we need numbers by end of day”—vague, urgent requests. From the engineering side comes pushback like “that data isn’t accessible”—hard technical constraints.

Managing expectations between both groups demands negotiation skills that go well beyond technical ability, and it’s a significant source of daily stress.

Unrealistic Expectations from Leadership

It’s common for executives to believe that “data can solve anything,” setting up unrealistic expectations.

In reality, poor data quality, difficulties proving causation, and other constraints are constant obstacles—and when expected outcomes aren’t delivered, analysts can find themselves labeled as “not useful.”

The Ongoing Burden and Cost of Continuous Learning

The Japan Data Scientist Association’s 2024 survey found that 60% of active practitioners now use generative AI in their work, making continuous learning to keep up with technological change non-negotiable.

In addition to financial costs—coding bootcamps (running into the hundreds of thousands of yen), textbooks, certification exams—finding time outside of work hours to study is a serious burden.

(Source: Japan Data Scientist Association, “2024 Survey”)

Most Companies Lack a Proper Data Analytics Environment

Only 26% of companies have their own talent development programs—a figure that has stagnated. Analysts frequently encounter structural problems beyond any individual’s control: underdeveloped data infrastructure, siloed data, and insufficient tool budgets.

(Source: Japan Data Scientist Association, “2024 Survey”)

Heavy Workloads and the Risk of Overwork

Juggling multiple projects at once and responding to ad-hoc analysis requests at a moment’s notice is the norm. The sheer volume of preprocessing, cross-team coordination, and report preparation makes this a role prone to long working hours.

Generative AI Is Transforming the Role

Generative AI usage in the workplace has doubled year-over-year to 60%, and automation is already well underway for routine data cleaning, basic aggregation, and simple visualization.

A major source of anxiety is the uncertainty: “Will the skills I’m working so hard to acquire become obsolete in just a few years?”

(Source: Japan Data Scientist Association, “2024 Survey”)

2. Does Data Analytics Really Have No Future? [Verified with Official Data]

We’ve covered the reasons why people say “don’t do it”—but does data analytics truly have no future?

Here we take a comprehensive look at data from official sources including the Ministry of Economy, Trade and Industry (METI) to objectively assess the field’s long-term prospects.

A Shortage of Up to 790,000 IT Professionals Will Persist Through 2030

A METI study projects a shortfall of up to approximately 790,000 IT professionals by 2030. Narrowing the scope to AI-specific talent, the same report forecasts a shortage of roughly 124,000 by that year.

The deficit is especially acute for cutting-edge roles in AI, big data, and IoT—and data analysts, as central drivers of corporate DX (digital transformation) initiatives, are expected to remain in sustained demand.

(Source: Ministry of Economy, Trade and Industry, “Survey Report on IT Human Resource Supply and Demand”)

Human Interpretation and Strategic Insight Remain Essential in the AI Era

AI excels at processing large volumes of data at speed and recognizing patterns. What human data analysts bring is something different: understanding business context, reasoning about causation, and translating findings into strategic decisions.

A 2025 PwC survey found that three in four companies are exploring data monetization, and “a lack of employees with data skills” was cited as a key challenge.

(Source: PwC Consulting LLC, “Data Monetization Survey 2025”)

Average Annual Salary for Data Analysts: ¥7.4 Million

According to Kyujin Box, the average annual salary is approximately ¥7.4 million—roughly ¥2.5 million higher than the average full-time employee salary of ¥4.928 million.

In finance and consulting, salaries exceeding ¥10 million are not uncommon, and freelancers can find contracts paying ¥1–1.5 million per month.

(Source: Kyujin Box, “Salary and Hourly Wage Information for Data Analyst Jobs”　MyNavi, “2025 Annual Review: First-Year Salary Report”)

Career Options Span a Wide Range of Industries

Data analyst skills are in demand across manufacturing, finance, retail, healthcare, and beyond.

The role also opens doors to transitions into data scientist, BI engineer, data consultant, or product manager positions—and the flexibility to work freelance or take on side projects.

■Related Reading

Curious how data analyst salaries compare to other IT roles in Japan? This guide breaks down engineer compensation from entry level to senior positions with real market data.

3. Who Is—and Isn’t—Suited to Be a Data Analyst

Making a successful career change into data analytics requires an honest, clear-eyed assessment of your own aptitude.

Here we look at both personality fit and capability fit to outline the traits of people who tend to thrive—and those who often don’t.

5 Traits of People Who Are Well-Suited

<Characteristics of a Good Fit>

• Enjoys thinking through problems with numbers and logic
• Doesn’t mind patient, methodical work
• Finds satisfaction in coordinating and explaining across teams
• Has a strong drive to learn new technologies and skills
• Can identify the real business problem beneath the surface question

Having both “tolerance for repetitive, detail-oriented work” and “strong communication skills” is especially important.

5 Traits of People Who May Not Be a Good Fit

<Characteristics of a Poor Fit>

• Gets bored with repetitive tasks quickly
• Struggles with interpersonal communication
• Prefers intuition and gut feeling over data and numbers
• Has difficulty sustaining a learning habit and gives up easily
• Needs to see results quickly and can’t handle slow feedback loops

If many of these apply to you, it may be worth exploring other roles that better leverage your strengths—such as data engineer or project manager.

■Related Reading

Not sure data analytics is the right fit? Explore the full landscape of IT career paths in Japan — from specialist to management tracks — to find the role that suits you best.

4. The Rewards and Benefits of Being a Data Analyst

After all those hard truths—is it still worth pursuing a career in data analytics?

Here we take a concrete look at the rewards and benefits that make it worthwhile for those who push through the challenges.

A Key Role in Shaping Executive Decisions

When your analysis report gets presented in an executive meeting and kicks off a project worth hundreds of millions of yen, that sense of impact is something unique to data analytics.

Data analysts often have direct access to senior leadership, and seeing your work translate into measurable business outcomes is a powerful form of professional validation.

High Market Value and Scarcity Born of Deep Expertise

Professionals who are fluent in all three domains—statistics, programming, and business—are exceptionally rare, and that scarcity is what drives the high market value.

In the job market, this expertise puts you in a strong position: receiving multiple offers simultaneously is not unusual.

The Freedom to Build a Career Across Industries

The ability to extract data with SQL, analyze it with Python, and communicate findings in business terms is valued across every industry.

This makes cross-industry career transitions straightforward, and opens the door to lateral moves into adjacent roles—as well as the flexibility to work freelance or take on side projects.

■Related Reading

Once you’ve built your data analytics career, knowing how to negotiate your salary is key. This guide covers proven strategies for foreign IT engineers targeting higher pay in Japan.

5. How to Decide Whether to Make the Move—Without Regret

Having understood both the harsh realities and the genuine appeal of data analytics, it’s time to make your decision: should you actually make the move?

Here are concrete criteria to help you reach that decision on your own terms.

Criteria for People Who Should Go for It

<Decision Criteria: Go for It>

• You can commit to learning continuously for 3 or more years
• You’re aiming for a salary of ¥7 million or more
• You’re genuinely interested in both technology and business
• You can find meaning in unglamorous, detail-oriented work
• You want to use data to solve real-world problems

If 4 or more of these apply to you, your aptitude is high and the move is worth seriously pursuing.

Criteria for People Who Should Hold Off

<Decision Criteria: Hold Off>

• You want a high income right away
• You can’t carve out time to study
• You’re attracted to the image, not the reality
• Cross-team coordination feels draining or painful
• You want to focus purely on technical work

If 3 or more of these apply to you, a different career path may lead to greater satisfaction. What matters most is choosing a career where you can truly thrive and feel fulfilled.

■Related Reading

If you’re weighing a career change for higher income, this complete guide walks you through the steps foreign engineers take to successfully move into better-paying IT roles in Japan.

■Not Sure Which IT Role Suits You? Talk to a Bilingual Career Advisor

For foreign engineers in Japan with N2 or higher Japanese proficiency, our advisors will help you assess whether data analytics — or another IT path — is the right move based on your background and goals.

▼Contact BLOOMTECH Career for Global here

6. A Roadmap for Breaking Into Data Analytics with No Experience

If you’ve decided to go for it—what are the actual steps to get there?

Here we lay out a realistic roadmap broken down by age and background, along with a prioritized list of skills to acquire.

In Your 20s with No Experience (Estimated Timeline: 6 Months–1 Year)

Work through foundational statistics (1–2 months), SQL (2–3 months), and Python basics (2–3 months), then build a portfolio of around 3 projects using Kaggle datasets.

Target entry-level positions open to career changers. You’ll likely start in the ¥3–4 million range, but stepping up to ¥6–7 million within a few years is realistic.

In Your 30s Transitioning from Engineering (Estimated Timeline: 3–6 Months)

Leverage your existing programming skills and focus your learning on statistics and machine learning (2–3 months). Fill in business knowledge gaps (1–2 months) and, if possible, explore an internal transfer within your current company.

An internal move carries less risk and makes it easier to gain real-world experience—so explore that option first.

In Your 40s or Later (Recommended Timeline: 1+ Year)

Lean into your differentiators: management experience and deep business acumen. Take stock of your industry knowledge and business skills, then systematically build up SQL, statistics, and Python fundamentals.

Pursue analysis projects in your current role to create concrete, quantifiable wins—revenue increases of X%, cost reductions of ¥X. Positioning yourself as a “data analyst with deep business experience” is the key to standing out.

Skills to Prioritize

<Skill Acquisition Priority Order>

• SQL (essential — highest priority)
• Excel / Google Sheets (essential)
• Foundational statistics (essential)
• Python or R (strongly recommended)
• BI tools (Tableau, Power BI)
• Machine learning (optional — for differentiation)

If you have SQL, Excel, and statistics under your belt, you already meet the bar for most entry-level and career-changer-friendly positions.

■Related Reading

Want to see how a software engineering career progresses in terms of salary and seniority? This guide maps out 5 career routes with real compensation data to help you plan ahead.

7. Summary: Data Analytics Rewards Those Who Go In with Eyes Wide Open

In this article, we’ve covered everything from the 8 reasons people warn against becoming a data analyst to career prospects, aptitude assessment, decision criteria, and a step-by-step roadmap for breaking in without prior experience—all grounded in official data.

The challenges are real: 80% of the job is unglamorous preprocessing, the learning curve is steep, the role puts you in the middle of conflicting demands, expectations from leadership can be unrealistic, continuous learning is a constant burden, data environments at many companies are underdeveloped, overwork is a genuine risk, and the threat of AI automation looms. But so is the opportunity: an average salary of ¥7.4 million, a projected shortfall of up to 790,000 professionals through 2030, and three in four companies actively exploring data utilization.

What matters is understanding all of this clearly—and deciding that it’s worth it anyway. Use the aptitude checklist and decision criteria to make the choice that’s right for you.

For those who go in with the right mindset and commitment, data analytics is a career that will pay off.

■Ready to Take Action? Start Your IT Career in Japan in 30 Seconds

BLOOMTECH Career for Global connects foreign IT engineers (N2 or above, based in Japan) with leading Japanese companies — including those actively hiring for data and analytics roles. Registration is free and takes just 30 seconds.

▼Contact BLOOMTECH Career for Global here