One of the ways to solve business challenges is through data analysis. By collecting and analyzing vast amounts of data from both within and outside the organization, we can clarify the current situation and find solutions. The professional responsible for this is the "Data Analyst."
In this article, we’ll summarize the essential knowledge about the role of a Data Analyst, including job responsibilities, salary expectations, and career paths. We hope you find this information helpful!
Data analysis is becoming an essential tool for solving business challenges. By collecting and analyzing vast amounts of data from both inside and outside an organization, companies can clarify their current situations and find solutions. The role responsible for this crucial task is the data analyst.
In recent years, there has been a significant focus on big data and artificial intelligence, and the demand for data analysts continues to grow across various industries. According to IDC Japan, the domestic big data and analytics market is expected to see an average annual growth of 14.3% from 2022 to 2027, reaching a staggering ¥3.541 trillion by 2027.
Rocco Song, Division Director of the Enterprise Data Division at Robert Half Japan, emphasizes the increasing importance of data utilization: "The demand for data analysts is expanding rapidly, not just in the IT sector but across all industries, including finance, gaming, healthcare, advertising, e-commerce, and manufacturing. As the domestic big data and analytics market continues to grow, it will open up even more opportunities for skilled data analysts."
A data analyst collects and analyzes both internal data—such as user data and performance metrics—and external data, like social trends and competitor activities, to propose solutions to business problems. For instance, they might analyze historical data to understand how to attract a specific age group of customers or determine which PR strategies will be effective. Strong analytical and logical thinking skills are crucial, as are programming skills.
Data analysts can be categorized into two types: consultant-type and engineer-type. The exact role of a data analyst can vary by company, and sometimes one person may fulfill both roles.
Consultant-type data analysts primarily hypothesize based on their analysis and propose solutions to clients' problems. In addition to data collection and analysis, they may also create reports and deliver presentations. Therefore, they need to communicate their findings clearly and logically. These analysts typically work in consulting firms and marketing agencies.
Engineer-type data analysts focus on building systems that allow for efficient analysis of collected data. They work on data integration for analysis, automate processes, and ensure data security. These analysts often work for companies that run their own media or web portal sites.
While data analysts collect and analyze data to identify clients’ challenges and propose solutions, data scientists work more on the technical side. They extract relevant data from big data to build predictive and analytical models. Data scientists require advanced skills in machine learning and fast processing of large datasets.
Both roles necessitate knowledge of statistics and programming, but data scientists generally demand higher technical expertise. Some companies may not have a clear distinction between the two roles.
The responsibilities of a data analyst can be broadly categorized into three main areas:
The first step is to understand the business challenge. Data analysts listen to client concerns and anticipate what might be contributing to the problem. They then plan what data is needed and how to analyze it.
For example, if declining sales are an issue, they might analyze customer buying behavior and sales data while also considering external factors such as legal changes and competitor movements. After establishing a plan, they create a team structure and schedule.
Based on the established plan, data analysts collect data according to their roles within the team. They standardize the data format to ensure it’s ready for analysis. Analysis techniques may include statistical analysis, machine learning, and data mining, using programming languages like Java and tools for web analytics. If the required analysis is highly technical, they may consult data scientists to build analytical models.
When summarizing their findings, it’s crucial that analysts present the information in a way that is easily understandable. Visual aids such as graphs and tables can help. The key takeaway from the analysis should directly address the business challenge, using simple language to enhance the client's understanding and strengthen the persuasiveness of the proposed solutions.
According to Robert Half's internal data, the average salary for a data analyst ranges from ¥7.5 million to ¥13.5 million. There are even job postings offering salaries exceeding ¥20 million, indicating that high earnings are possible depending on skills and experience.
Here’s a snapshot of average salaries for related roles:
Job Title
| Entry-Level Salary
| Average Salary
| Senior Salary
|
Data Engineer / Architect
| ¥7 million
| ¥9 million
| ¥12 million
|
Data Scientist
| ¥7.5 million
| ¥10.5 million
| ¥13.5 million
|
Data Science Manager
| ¥10.5 million
| ¥13.5 million
| ¥15.5 million
|
Chief Data Officer (CDO) / Analytics Director
| ¥14 million
| ¥22.5 million
| ¥30 million
|
Here are some specific job postings for data analysts:
Position
| IT Business Analyst
|
Expected Salary
| ¥8 million - ¥11 million
|
Job Description
| - Serve as a primary point of contact for IT solution development supporting product and service planning.
- Collaborate with product, sales, and operations teams to propose IT solutions for new products and services.
- As the product owner after release, evaluate feedback from business units and ensure project KPIs are met.
- Manage product improvement requests from both business and IT teams and organize them as backlog items.
- Lead communication with stakeholders, prioritize backlog items, and develop action plans to enhance product/service value and drive business growth.
- Collaborate with stakeholders to create business cases for new IT initiatives, define scope and execution plans, and gain approval for project execution.
- Work with IT delivery team leaders, UX teams, and data science teams to finalize requirements and align architectural and solution directions.
|
Qualifications
| - Experience in cross-departmental system business analysis.
- End-user and data-driven mindset.
- Strong presentation skills.
- Logical thinking and problem-solving abilities.
- Native-level Japanese and intermediate-level English.
- Experience with IT core system projects (ERP, accounting systems, etc.).
|
Location
| Tokyo 23rd Ward
|
A successful data analyst should possess the following four skills:
Data analysts analyze data and make proposals for solving issues based on their findings. Therefore, analytical skills to identify problems from data are essential.
It’s important to acquire skills such as the basic concepts of statistics, the types of data and analysis methods, and data extraction from databases using SQL. Familiarity with cloud services like Google Cloud Platform (GCP) and Amazon Web Services (AWS) can also be beneficial for analysis, so it's advisable to get accustomed to these tools.
When proposing solutions to problems, it's essential to think logically and derive effective solutions. Without solid evidence, your solutions may lack persuasiveness. Discard assumptions and validate hypotheses based on collected information to arrive at solutions.
Even with strong evidence, if you cannot explain it clearly and systematically, it may not be communicated effectively. Thus, the ability to organize information logically and present it clearly is also necessary.
Data analysts use statistics and machine learning for data analysis. For advanced data analysis tasks, programming skills in languages like Java, Hadoop, Python, or R may be required.
Having programming skills enables you to:
Automate preprocessing of large datasets
Create advanced predictive models
Collect data automatically using APIs
While specialized programming skills are not always essential, they can broaden the range of tasks you can handle.
Data analysts often need to extract challenges from client companies and communicate analysis results, making communication skills vital. By listening to clients, you can build trust and gather additional information useful for analysis.
It’s also crucial to explain findings without excessive use of jargon. If clients can thoroughly understand the results, they can make confident decisions.
Here are four key certifications that can benefit your career as a data analyst.
The Statistical Test Certification is a nationwide standardized exam that measures your knowledge and application skills in statistics. It is recognized by the Japan Statistical Society. Since statistical knowledge is essential for data analysts, obtaining this certification can be quite advantageous.
The certification is divided into five levels, ranging from Level 1 to Level 4 (including Pre-Level 1). Here’s a summary of the skills associated with each level:
Additionally, there are examinations related to data science, such as Basic and Advanced Data Science Certifications.
Level
| Skills
|
Level 1
| Basic abilities to meet the data analysis needs in various real-world fields.
|
Pre-Level 1
| Ability to apply appropriate statistics to various real-world issues.
|
Level 2
| Knowledge of statistics at a foundational university level.
|
Level 3
| Understanding of statistical content related to high school math and applying it to everyday problem-solving.
|
Level 4
| Knowledge of basic statistics, tables, graphs, surveys, experiments, and probabilities that are taught to students worldwide.
|
The OSS-DB Certification Exam recognizes IT professionals with knowledge and technical skills in designing, developing, and operating open-source databases (OSS-DB). It is certified by the non-profit organization LPI Japan.
There are two certifications available:
OSS-DB/Silver: Certifies your ability in the design, development, implementation, and operation of database systems.
OSS-DB/Gold: Certifies your skills in improving, managing, and consulting for large-scale database systems.
In 2023, companies like Fujitsu, Assist, and NTT Data produced many certified OSS-DB/Gold professionals. Achieving OSS-DB certification proves your PostgreSQL skills and can greatly enhance your career prospects.
ORACLE MASTER is a certification that demonstrates your foundational knowledge and management skills in Oracle Database. There are four levels: Platinum, Gold, Silver, and Bronze. The ORACLE MASTER Silver SQL certification is especially recommended for data analysts, as it validates your proficiency in SQL, a widely used database language.
Approximately 280,000 engineers have obtained ORACLE MASTER certifications. Achieving a Silver-level or higher certification also grants you corresponding global qualifications, making it appealing for demonstrating your skills both domestically and internationally.
The Database Specialist Exam, administered by the IPA (Information-Technology Promotion Agency), assesses your knowledge and technical skills related to database planning, development, operation, and maintenance.
While this certification showcases specialized skills, it is a national exam and therefore quite challenging. The pass rate for the Autumn 2023 exam was 18.5%, with an average pass rate of 16.4% over the 15 exams conducted from Spring 2009 to Autumn 2023.
Here are four key characteristics that indicate someone may excel as a data analyst.
As the name suggests, data analysts work with data, so those who are comfortable handling numbers and data tend to excel in this role. The ability to identify patterns within complex data sets leads to more accurate and reliable analytical results.
The analysis results produced by data analysts influence important business decisions such as corporate strategy and sales planning. Therefore, accuracy is paramount. Ideal candidates are meticulous, ensuring that each task is completed correctly and regularly performing self-checks to catch any errors.
Data analysis techniques are continually evolving. Individuals with a keen interest in new analytical methods and a desire to learn and incorporate new skills are well-suited for this role. Additionally, being enthusiastic about figuring out how different data sets can reveal client challenges, similar to solving a puzzle, can enhance job performance.
Data analysis often involves teamwork, so those who communicate well and work cooperatively with others are ideal candidates. It’s important to remain open to team members' opinions without becoming overly attached to one's own analytical results, striving instead for more logical and effective analyses.
Data analysts aim to solve clients' challenges through data analysis. Balancing an understanding of both numerical data and current situations, while keeping the client's support in mind, is essential in this role.
Here are two potential career paths for data analysts:
For those who want to continue working in the field, becoming a data scientist is a viable path. This role often involves building analytical systems and typically requires more advanced knowledge in mathematics and physics compared to data analysts. It’s an excellent option for those looking to further hone their IT skills.
Because there is overlap between the responsibilities of data analysts and data scientists, previous experience can be leveraged, allowing for a smoother transition into this role.
Another path is to become a manager, leading an analytical team. This role is suited for those who can see the bigger picture and communicate effectively with team members.
Managers typically decide on the focus of data analysis projects, manage schedules, and facilitate communication with clients. They also address questions and challenges that arise during the data analysis process.
If you have consulting experience, pursuing a freelance career is also an option, including the possibility of starting your own business.
Data analysts play a vital role in helping businesses make informed decisions based on data-driven insights. As demand for these professionals continues to rise, understanding their responsibilities, career paths, and required skills is essential for anyone considering a career in this field. Whether you’re just starting or looking to enhance your existing skills, the future is bright for data analysts.
