データアナリストの将来性は?データアナリストの魅力や将来性のあるアナリストの特徴も解説What is the future of data analysts? The attractiveness of data analysts and the characteristics of analysts with future potential are also explained.

In recent years, the use of data in companies has become more important, and the demand for data analysts is rapidly increasing. However, with advances in AI technology, some may worry, “Will AI replace data analysis jobs in the future?” However, with the advancement of AI technology, some people may be concerned that data analysis jobs may be replaced by AI in the future.

In this article, we discuss the future of data analysts, the benefits of working as a data analyst, and the characteristics of data analysts who will be in increasing demand in the future. If you are considering a career change to data analyst, please take a look at this article.

Data analysts are specialists who analyze data held by companies to help solve problems, improve operational efficiency, and create new value. Data analysts handle a wide range of data, including customer information, sales data, website access logs, and SNS data. The role of a data analyst is to analyze large volumes of complex “big data,” predict user behavior patterns and future needs, and use this data for business strategies.

Data analyst work can be divided into two types: consulting and engineering. Consulting-type data analysts mainly belong to marketing companies and consulting firms, and use data to propose solutions to problems. This type of analyst is required to have marketing knowledge, hypothesis building skills, and communication skills, and strategic consulting using data is important.

On the other hand, engineering-type data analysts belong to platform and web media management companies, and work on improving products and services based on the data they collect. They are sometimes involved in development work, which requires programming skills and knowledge of machine learning and data mining.

However, these roles are not strictly separated, and there are many data analysts who combine both roles.

Differences between a data analyst and a data scientist

One profession that is often confused with data analyst is data scientist. Data scientists analyze the vast amounts of data accumulated daily to predict future trends and build models to create new value. Their main role is to help companies make strategic decisions by doing so.

Data analysts focus on “solving current problems,” while data scientists focus on “predicting the future. This is where they differ.

In actual practice, however, the roles of data analysts and data scientists are not necessarily clearly separated. There is a lot of overlap in the skills and duties of the two. Data analysts may be responsible for predictive analysis, while data scientists may be responsible for current data analysis.

In addition, job postings may be lumped together as “data scientist” and may not detail the specific duties of the position.

Please refer to the following article for more information on data scientists.

See also: What is a Data Scientist? Main job description and how to become one are also explained.

For those considering a career as a data analyst, the future potential is a very important factor.

Data is rapidly becoming more important these days, and many companies are making decisions based on data. This change has led to a rapid increase in demand for data analysts. In particular, the demand for data analysts will continue to increase as advances in big data and AI technologies expand the accuracy and scope of data analysis.

In fact, according to a research report by IDC Japan, the big data and analytics market in Japan is expected to grow at an average annual rate of 14.3%.

Although data analysts have existed in the past, their activities have been mainly limited to certain fields. Recently, however, data analyst expertise is being sought not only in the IT industry, but also in many other industries, including finance, manufacturing, logistics, and healthcare.

Demand for data analysts will continue to remain high due to the advancement of digital transformation (DX), the growing importance of data analysis, and the shortage of highly specialized human resources.

(See: IDC Japan, “Japan's Big Data/Analytics Market Forecast to be 2.749 trillion yen by 2024”)

The work of a data analyst includes understanding corporate issues, formulating hypotheses, collecting and organizing data, analyzing data, and visualizing the results of analysis.

The main tasks of a data analyst are

• Identifying and hypothesizing corporate issues
• Collecting, organizing, and pre-processing data
• Data analysis
• Visualization of analysis results
• Reporting and submitting outputs

We begin by understanding the issues facing the company and hypothesizing a solution. Next, we collect the data needed to test the hypotheses and organize it into a form that is easy to analyze. Since this data is obtained from many sources, pre-processing is essential, such as extracting from databases, handling missing and outlier values, and converting the data format.

Once the data is ready, we proceed to the analysis phase. This phase uses statistical analysis and data mining techniques to find insights from the data that can be used to solve problems. We also visualize the data in order to communicate the results of the analysis in an easy-to-understand manner. Graphs, charts, and dashboards must be created so that they can be understood intuitively by stakeholders who do not have specialized knowledge. Finally, the results of the analysis are compiled into a report and a specific action plan is proposed. This report is then submitted to management and other departments for decision-making.

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We will now focus on four advantages of becoming a data analyst. The main advantages are as follows
 

• High income potential
• Opportunities in a variety of industries
• You can be entrusted with important positions.
• Independence is an option

High income potential

Data Analysts are highly sought after in the corporate world. Many companies highly value individuals with data analysis skills, and data analysts can develop their careers while earning a stable income.

According to a survey by Jobbox, the average annual salary of a full-time data analyst is 6.96 million yen. This is significantly higher than the average annual salary of approximately 5.07 million yen for all industries in Japan, as shown in the 2023 Basic Survey on Wage Structure by the Ministry of Health, Labor and Welfare, a difference of approximately 1.9 million yen.

As this data shows, data analysts can easily aim for higher income compared to other types of jobs. Data analysts with more experience and advanced skills can aim for annual incomes of 10 million yen or more.

Reference: Job Box Salary Navi
(Reference: 2023 Basic Survey on Wage Structure, Ministry of Health, Labour and Welfare)

Prospects for success in a variety of industries

Data analysts are not limited to specific industries, and can utilize their skills in a variety of fields. Typical examples are the IT and web industries that handle big data, but recently demand for data analysts is increasing in many other industries as well. Typical examples include the financial industry, the gaming industry, the medical industry, the advertising industry, the e-commerce industry, and the manufacturing industry.

There are also a wide variety of career paths for data analysts. As they gain experience and develop advanced analytical skills, they can advance to more specialized positions such as data scientist or data engineer. These positions are also expected to provide leadership in the use of data in various departments within a company and contribute to strategic decision making.

Assigned to Important Positions

Due to their expertise and skills, data analysts are often assigned important roles within a company. For example, they can directly influence corporate decision-making by formulating management strategies and proposing operational improvements based on the results of data analysis. By logically deriving solutions to problems based on objective evidence in the form of numbers and contributing to management, you can contribute to the growth of a company.

Data analysts also participate in large-scale projects such as building new systems or improving existing systems. By using the results of data analysis to design and implement more efficient and effective systems, they will also contribute to improving the overall performance of the company.

Working in such an important position is very rewarding for a data analyst and will help you grow as a business person.

Independence is also becoming an option.

In recent years, the demand for data analysts has been increasing rapidly as it becomes more important for companies to utilize data. At the same time, the demand for a way of working that is not restricted to traditional corporate work is increasing, and the option of freelancing is also attracting attention.

Data analysts with excellent skills can work at their own pace and participate in a variety of projects without being tied to a specific company. This is a major attraction for those who seek freedom and want to broaden their careers.

In addition, many freelance jobs offer good conditions. This can make it easier to earn a high income with less income instability. Flexible work arrangements that emphasize work-life balance, such as full remote or three-day workweeks, while accepting high unit-paying jobs, can also be achieved.

Success as a freelancer requires good self-management and marketing skills, but if you can meet these requirements, you can expect further career advancement and increased income.

Let's now look at the characteristics of a promising data analyst.
 

• Highly skilled as an engineer to begin with
• Able to think logically as a consultant
• Constantly learning about machine learning
• Has management skills
• Has a wealth of practical experience in the field

Highly skilled as an engineer to begin with

Data analysts with advanced skills as engineers are very likely to be very successful in the future. Familiarity with programming and languages such as JavaScript and Python allows them to analyze data quickly and accurately, contributing greatly to client decision making and problem solving.

Furthermore, utilizing databases and cloud services can automate the collection and integration of data and greatly improve the efficiency of operations. Thus, data analysts who can utilize their skills as engineers to facilitate projects will continue to be in high demand.

Ability to think logically as a consultant

The ability to think logically is one of the key characteristics for promising data analysts: even with advances in AI technology and automation of data processing, human judgment is still required for upstream tasks such as organizing business issues and planning analysis. The quality of requirements definition and design at this stage greatly affects the final analysis results, so the value of data analysts with extensive experience remains high.

Objective and rational judgment is essential in data analysis. It requires the ability to think logically based on data, eliminating emotion and subjectivity. Subjectivity can lead to the use of unsubstantiated information or data, which can lead to inappropriate results that differ from actual results. Therefore, it is important not only to tabulate and visualize data, but also to always consider “What does this data mean?” is always important. This will help to extract insights that are beneficial to the business and lead to concrete actions.

Thus, consulting skills and logical thinking skills to link data analysis to business value cannot be replaced by AI. Therefore, the demand for data analysts with these skills will increase in the future.

Constantly learning about machine learning

Continued learning about machine learning will have a significant impact on the future of data analysts.

Machine learning is rapidly evolving in the field of data analytics, and there is a shortage of people with advanced expertise. Therefore, continuing to learn and develop expertise in machine learning will increase the likelihood of being valued as a rare and valuable data analyst.

Furthermore, routine data analysis and forecasting are expected to be replaced by AI in the future. However, data analysts with advanced expertise in machine learning and data mining will be able to handle complex problems and areas that are difficult to automate, and will be expected to play an active role in a variety of fields.

Thus, data analysts with advanced analytical skills will be able to set themselves apart from others in the future.

They also possess management skills.

Simply having strong analytical skills is not enough to have a future as a data analyst. Since data analysis projects are usually conducted by a multi-member team, management skills are also important to lead the team and maximize the abilities of the team members.

To keep the project running smoothly, it is necessary to assign tasks, manage progress, and communicate closely with team members. Especially in large-scale data projects, where members from different fields of expertise often work together, the data analyst will have more opportunities to exercise leadership.

Therefore, a data analyst who can see the entire project, manage schedules and team members, and proactively work on problem solving will be valued as a highly marketable talent.

Extensive practical experience in the field

Data analysts with a wealth of hands-on experience in the field will continue to be in high demand. Experience gained through actual work is extremely important for data analysts. The ability to find real problems from large amounts of data and derive effective solutions cannot be acquired through theory alone. By participating in actual projects and working on problem solving while communicating closely with clients, practical analytical and solution skills are improved.

Therefore, it is important for aspiring data analysts to change jobs as soon as possible and gain as much practical experience as possible.

This article discusses the future of data analysts. As companies emphasize data-driven decision making, the demand for data analysts is expected to increase further in the future. Data analysts with advanced analytical, business, and management skills, as well as a willingness to continue learning new technologies such as machine learning, will be able to play an active role in many fields.

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