データアナリストとデータサイエンティストの違いを徹底解説!共通点も紹介Thorough explanation of the difference between a data analyst and a data scientist! Also introduces the similarities.

In recent years, data analysis has become an integral part of business success. However, there are two similar job titles, "data analyst" and "data scientist," and many people may find it difficult to understand the difference between their respective roles and job descriptions.

In this article, we will explain the differences between the respective roles of data analysts and data scientists, the skill sets required, and the job descriptions. We also explain the commonalities they share and the differences between them and other data-handling jobs other than data analyst and data scientist.

The following seven perspectives will now explain the differences between data analysts and data scientists.
 

• Difference in roles
• Difference in job description
• Difference in annual salary
• Difference in required skills
• Difference in careers
• Difference in future potential
• Difference in the right person for the job
   

Let's look at them one by one.

Difference in Roles

First, we will explain the difference between the roles of a data analyst and a data scientist.

The Role of the Data Analyst

A data analyst is a professional who collects and analyzes data, which is a "treasure trove" for companies, and derives useful information for business. Their main role is to contribute to corporate activities by providing proposals for problem solving and decision making based on the analysis results they derive.

With the expansion of the Internet economy in the early 2000s, e-commerce and online services exploded, and companies began to take an interest in analyzing website traffic and understanding customer behavior, making data analysis increasingly important.

Although job titles such as IT specialists and analysts who manage databases and perform simple analyses have existed for quite some time, it was around this time that the job title of "data analyst" was more clearly defined and decision support using data became a focus of attention.

The Role of the Data Scientist

Data scientists use mathematics, statistics, and information engineering to analyze vast amounts of data and extract knowledge that is useful for business decision-making and problem-solving. The main role of this position is to facilitate decision-making in corporate business strategies by analyzing data accumulated in daily operations, predicting the future, and building models to create new value.

The role of a data scientist differs from that of a data analyst in that a data analyst makes proposals to solve problems based on information obtained from "current situation analysis," while a data scientist focuses on "future prediction" and makes proposals to create new business opportunities using advanced statistics, machine learning, and other technologies.

The importance of this position has begun to grow due to the increase in big data and the widespread recognition of the value of its use. With the development of information technology, the amount of data managed by companies continues to increase, and in order to gain an edge over competitors in business in this information-rich age, this information must be strategically analyzed and utilized.

In the past, data-related tasks were divided into separate divisions of labor, but today, when strategic use of data is required, these tasks have been integrated into a single specialized field, giving rise to the data scientist profession.

Differences in Job Descriptions

The following sections describe specific job descriptions for each of the data analyst and data scientist.

Job Description of a Data Analyst

Data analysts determine which data is needed to solve various problems faced by clients and use statistical methods to derive valuable information from the data. Based on the data obtained, they make actionable recommendations and contribute to the business decision-making process. Reporting and presenting the results of analysis visually in charts and tables to clients in an easy-to-understand manner is also an important part of the job.

Main job responsibilities
 

• Identification of corporate issues and formulation of hypotheses
• Data collection, organization, and preprocessing
• Data analysis
• Visualization of analysis results
• Reporting and submission of output
   

In addition, data analysts can be broadly divided into two types: "consulting type" and "engineering type. As the name suggests, the consulting type is primarily responsible for proposing problem-solving measures based on analysis results, planning their implementation, and verifying their effectiveness.

On the other hand, engineers are involved in service quality improvement and system development using the analysis results. In some cases, this type of work involves directly developing and improving systems by utilizing programming skills, and is in particularly high demand at web service management companies and technology-related companies.

While consulting and engineering have their own characteristics, there is no strict division between the two types of positions, and the role may vary depending on the company where the employee works. In some cases, a single data analyst may perform both duties concurrently.

Data Scientist Job Description

The work of a data scientist ranges from building an environment for conducting corporate data analysis, to data collection and processing, analysis, report writing, and proposals.

The main job responsibilities of a data scientist include
 

• Identification of corporate issues
• Construction of data analysis environment
• Data collection, organization, and preprocessing
• Data analysis and hypothesis testing
• Reporting and submission of output
   

While tasks such as understanding corporate issues and reporting are common to those of data analysts, data scientists also build database environments for storing data, implement machine learning algorithms, and build analytical models, and are characterized by a more technical element than data analysts. This is a characteristic of data scientists. In addition to statistical analysis, data scientists also analyze more complex data by utilizing advanced skills such as machine learning and programming.

Although data analysts and data scientists have the same objective of "contributing to corporate activities by utilizing big data," the scope of work they are responsible for is slightly different as described above. However, there is no strict line between the two in practice, and some companies recruit data analysts as data scientists, so it is important to check carefully.

Differences in Annual Income

Next, let's take a look at the differences in their annual salaries.

Annual Income of Data Analysts

According to a survey conducted by Kakaku.com's Job Box (as of February 2024), the average annual salary of a full-time data analyst is 6.96 million yen, which is considerably higher than the average annual salary in Japan of 4.58 million yen (data from the National Tax Agency's "2022 Statistical Survey of Private Salaries"). The data is also available from the National Tax Agency's "Survey of Private Sector Salaries in 2022.

Annual Salary for Data Analysts

Annual salary varies depending on age, region, place of employment, and personal skills and experience, but it is possible to aim for an annual salary of 10 million yen if you are highly skilled.

In fact, a search for "data analyst jobs" within the job information posted by United World, Inc.

Job page of recruitment agency "United World Inc.

Job Seeker Page for "United World Inc.

Annual salary of Data Scientist

According to the same survey conducted by Jobbox (as of February 2024), the average annual salary of a data scientist is 6.47 million yen, about 500,000 yen lower than that of a data analyst.

However, when compared as full-time employees, data analysts earn between 6.08 and 7.03 million yen, while data scientists earn between 6.12 and 7.17 million yen, a slightly higher level.

See also:
Job Box Salary Navigator, "Annual salary, hourly wage, and salary for data analyst jobs (job statistics)"
Job Box Salary Navigator, “Annual salary, hourly wage, and salary for data scientist jobs (job statistics data)”

Note that the average annual salary for data scientists varies greatly depending on the source of information (approximately 5.58 million yen on job tag operated by the Ministry of Health, Labor and Welfare, 7.91 million yen on Japan Association of Data Scientists, and 5.32 million yen on doda), so care should be taken when referring to information.

Job page of the recruitment agency "United World Inc.

Job page of the recruitment agency "United World Inc.

Job page of the recruitment agency "United World Inc.

As with data analysts, annual salaries vary depending on the skills and experience required, but data scientists have more job openings than data analysts, and many companies are offering annual salaries in excess of 10 million yen.

Differences in Required Skills

Next, we will introduce the differences in skills required of data analysts and data scientists.

Skills Required of Data Analysts

Data analysts are responsible for a wide range of tasks, and the following skills are required to perform them.
 

However, as mentioned above, there are two types of this position: consulting and engineering. Although the above skills are essential in the broad category of data analyst, the level of applied knowledge required differs depending on whether you are aiming for a consulting or engineering type.

The main task of consultants is to propose solutions to problems through data analysis, so they must be able to present concrete solutions to clients. For this reason, it is especially important to have the ability to think logically in order to utilize the information obtained from data analysis to solve problems. On the other hand, engineers are engaged in service improvement and system development based on the results of data analysis, so they are required to have proficiency in programming languages and skills in operating analytical tools.

In recent years, an increasing number of data analysts have acquired both consulting and engineering skills. By acquiring both skills, they will be able to take on a wider range of jobs.

Skills Required of Data Scientists

The skills required of data scientists have much in common with those of data analysts. For example, knowledge of statistics and mathematics, skills in handling programming languages, and documentation skills are required for both types of jobs.
 

The difference is that data scientists build their own environment for advanced analysis. They are also responsible for building the environment for data analysis, selecting analysis algorithms, and implementing predictive models, which broadens the scope of skills required.

Since data analysts mostly utilize existing environments to perform data analysis, their skills related to environment construction are relatively minor. What is important for data analysts is to understand business issues and contribute to solving them by using appropriate analytical methods. Since environment construction is only one of the means, acquiring the minimum necessary skills should be sufficient.

Data scientists are also required to have more advanced and extensive statistical and programming knowledge and skills in using AI and machine learning.

Career Differences

Let's then look at the differences in careers in each of these occupations.

Data Analyst Careers

The career path of a data analyst includes progressing from junior to middle to senior data analyst and eventually becoming a data analysis manager or data analysis leader. They can also deepen their expertise to become BI (Business Intelligence) consultants, or expand their domain to become data scientists and other positions.

Examples of major career paths for data analysts
 

• Senior Data Analyst
• Data Analysis Manager
• Data Analysis Leader
• BI Consultant
• Data scientist
• Independent Freelance
   

Data Scientist Careers

Career paths are expected to progress to senior management positions, such as senior data scientist, project manager of a data science team, or CDO (Chief Data Officer), as you further develop your technical expertise. There may also be a path to becoming an independent consultant specializing in a particular technology or industry.

Examples of major career paths for data scientists
 

• Senior Data Scientist
• Project Manager
• CDO
• AI Engineer
• Management Consultant
• Research Professionals
• Independent freelancer
   

As the importance of data analysis has increased in recent years, the demand for management consultants with data analysis skills has grown rapidly, and more and more people are looking to make a career change to management consulting.

Differences in Future Prospects

Next, we will explain the differences in the future potential of each.

Future Prospects for Data Analysts

In recent years, the volume of data has exploded in all industries, and its utilization has become indispensable for improving corporate competitiveness. Through data analysis, it has become possible to solve a variety of issues, such as analyzing customer behavior, conducting market research, managing risk, and improving operational efficiency, and people who can handle data are playing an important role in supporting corporate growth.

Against this backdrop, the demand for data analysts with data analysis skills is expected to continue to grow.

However, while the future is promising, mere data analysis skills are not enough to be an active data analyst. You must have the logical thinking ability, business knowledge, and communication skills to understand corporate issues and make concrete proposals based on the results of data analysis, as well as the attitude to keep learning the latest technologies.

Data analysts who can hone these skills and contribute to solving corporate issues will continue to be in high demand.

Future Prospects for Data Scientists

The demand for data scientists is also increasing steadily year by year, making it one of the jobs with high future potential. In particular, data scientists who can contribute to the development of new products and services using machine learning and deep learning are extremely valuable to companies, and demand for data scientists is expected to continue to grow in the future.

According to a Yano Research Institute survey, the shortage of data analysis-related personnel (data scientists, analysis consultants, analysis architects, and project managers) will become increasingly serious, and is expected to reach 176,300 by 2025.

Yano Research Institute's "Forecast for the Scale of Human Resources Related to Data Analysis in Japan" (Japanese only)

The Difference Between the Right Person for the Job

In order to maximize your strengths, increase your annual salary, and advance your career, it is important to confirm that the job title fits your aptitude. In this section, we will look at the characteristics of people who are suited to the respective positions of data analyst and data scientist.

Who is suited to be a data analyst?

A good fit for a data analyst is someone who does not mind working with numbers and data for long periods of time. Specifically, they are suited to people who are interested in mathematics and statistics, who are good at processing data in Excel, and who have a natural habit of thinking about things in terms of numbers in their daily lives.

The majority of a data analyst's daily life is spent dealing with numbers and data. Therefore, it is an absolute requirement that you are at least comfortable with looking at numbers on a daily basis.

People who are intellectually curious and enjoy learning new things, as well as those who are meticulous and good at detailed work, are also considered suitable for this position. To be successful as a data analyst, you will need business skills as well as statistics and IT skills, and you will need to continuously update your knowledge of these areas.

In particular, those who have worked as system engineers or programmers, who are confident in their programming skills, and who want to advance their career to a position that deals more with mathematics and statistics, may be suitable for the engineering type data analyst position, while those with previous work experience as consultants may be suitable for the consulting type data analyst position. If you have previous experience as a consultant, you may be a good fit as a consultant-type data analyst.

Who is suited to be a data scientist?

Data scientists have a wider range of work than data analysts, but the core of their work is still "data analysis. Therefore, as with data analysts, the candidate must be comfortable working with numbers and data.

In addition, this position requires the ability to find unknown patterns and relationships in vast amounts of data and apply them to solving business and scientific problems. Therefore, it is essential to have a curiosity to explore the depths of data, always looking for new ideas and solutions, without being satisfied with existing knowledge and methods.

In addition, it is essential for data scientists to be constantly curious about new technologies and algorithms, and to be willing to learn new knowledge on their own. Technology is advancing daily, and new algorithms and data processing techniques are constantly emerging. Therefore, the data scientist must be sensitive to the latest technological trends and have the flexibility and learning ability to incorporate them into his or her own projects.

Data analysts and data scientists, while having different specialties, have in common that they are the people responsible for promoting digital transformation (DX) in companies and organizations.

Data analysts contribute to business efficiency and cost reduction by identifying issues through data analysis and proposing improvement measures. Data scientists, on the other hand, use advanced technologies such as statistics and machine learning to predict the future and create new business opportunities, and formulate corporate growth strategies.

Thus, both play an important role in promoting corporate DX and contributing to corporate growth by taking different approaches while leveraging their respective expertise.

Another point in common between data analysts and data scientists is that they require knowledge and skills in a wide range of areas, including data analysis methods such as statistics and machine learning, programming skills, database knowledge, and business understanding

In addition to data analysts and data scientists, there are a variety of other professions that deal with data. Here we will discuss three of these professions: data engineers, data architects, and AI engineers.

Data Engineer

Data engineers are engineers who create the infrastructure for data utilization. Their main role is to create the foundation of the data analysis environment and organize it into a form that is easy to utilize, so that data scientists and data analysts can concentrate on analysis.

Although we mentioned earlier that building the infrastructure for a data analysis environment and organizing and pre-processing data are the duties of data scientists, these tasks are generally handled by data engineers when they belong to a company.

However, for companies that have a growing demand for data analysis but do not have the personnel or budget to hire a full-time data engineer, the data scientist may be responsible for some or all of the data engineer's tasks.

Another major difference between data engineers and data analysts or data scientists is that data engineers basically do not analyze data or make proposals to clients.

Data Architect

Data architects are professionals who design the structure and flow of data so that the vast amount of data held by companies and organizations can be efficiently used in line with business strategies. Data architects are responsible for overall optimization of all data-related processes, including data collection, storage, analysis, and utilization.

Main Responsibilities
 

• Design and construction of data architecture (e.g., determining data storage and access methods)
• Operation and management of data infrastructure (data security measures, data quality management, etc.)
• Consulting for data utilization
• Communication with stakeholders
   

The relationship between each position is as follows
 

• Design of data structure and flow (data architect)
• Based on the design, build and operate databases, data pipelines, etc., to realize the data infrastructure (data engineers)
• Collecting and analyzing data from the data infrastructure and extracting information useful for solving business issues (data analysts)
• Create new knowledge and value from data using advanced technologies such as statistics and machine learning (Data Scientist)
   

Data architects, data engineers, data analysts, and data scientists maximize the use of data in companies and organizations by leveraging their respective expertise and working together.

AI Engineers

AI engineers are technicians who develop systems using artificial intelligence (AI) technology. Specifically, they are involved in the development and operation of various AI systems, such as image recognition, speech recognition, natural language processing, and predictive analytics, using technologies such as machine learning and deep learning.

[Main job description
 

• Design and development of AI systems
• Collection and preparation of data to be trained
• Analysis of training data
   

Compared to data scientists, AI engineers are characterized by a greater focus on technical implementation and the development of AI solutions. On the other hand, the work of an AI engineer includes "data analysis," which is common to the job descriptions of data analysts and data scientists.

At first glance, they may seem similar, but they differ in that data analysts and data scientists analyze data with the objective of "contributing to corporate activities based on the results of data analysis," whereas AI engineers aim to build highly accurate machine learning models.

This article details the differences between data analysts and data scientists. While data analysts and data scientists share the commonality of data analysis, their roles, areas of work, required skills, and career paths are different.

As the use of data in business is becoming increasingly important today, data analysts and data scientists are two positions that are expected to remain in high demand. If you are currently working in the IT industry and considering career advancement or a career change, you may want to consider becoming a data analyst or data scientist, as the future is bright.

However, since the job descriptions of data analysts and data scientists may be defined differently by different companies, it is a good idea to check the job descriptions carefully when considering a career change.

United World Inc. supports career change activities of those who aim to become data analysts and data scientists. After carefully listening to your detailed requirements and career plans, we will introduce you to the best job opportunities that suit you best, so please feel free to contact us if you want to make your job change more efficient.
 

Talk to United World about 
career change.