Market Size in the World of Data Labeling

There are various methods for AI development. In particular, the method called deep learning has greatly improved the recognition accuracy of AI. The range of AI applications has also expanded to various fields such as manufacturing, services, healthcare, and education, making it an essential presence in society.

In deep learning, it is necessary to provide a large amount of data to AI. By doing so, AI will find and learn patterns that match the purpose from the large amount of data. "Supervised learning" is often used for AI learning, in which case, data called "teacher data" is required. Teacher data is created by a process called data labeling, and in this case, we will explain the mechanism and market size of data labeling in the world.

Table of Contents

1. What is Data Labeling?
1-1. Definition and Characteristics of Data Labeling
1-2. What is the difference between data annotation?
1-3. Differences in Terminology Usage between Japan and Overseas
2. Market Size of Data Labeling
2-1. Current Market Growth Rate
2-2. Future Trends in Data Labeling Market
2-3. Relevance to Next-Generation Technology and Industry Development
2-4. Impact of the Increasing Demand for Data Labeling
3. What are the benefits of outsourcing data labeling tasks?
3-1. Possibilities for Cost Reduction and Streamlining
3-2. Utilizing Expertise and Skills
3-3. Points to Consider When Outsourcing Data Labeling Tasks
4. Human Science's Achievements
5. Data Labeling Outsourcing Service by Human Science Co., Ltd.

1. What is Data Labeling?

What is data labeling, which is essential for "supervised learning"? As the name suggests, it refers to the process of labeling data. But what does it mean to label data? Here, we will explain the mechanism of data labeling.

1-1. Definition and Characteristics of Data Labeling

Data labeling is the process of adding labels (or annotations) to the objects that need to be recognized by AI within the data. There are various ways to add these labels. For example, in image labeling, there are methods such as "bounding box" which surrounds the object with a rectangle, "keypoint" which marks a specific point on the object, and "segmentation" which outlines the object. In text labeling, there are methods such as selecting the target part (word, sentence, paragraph, etc.) with underline or highlight, or labeling the entire text as a whole. Although the purposes may differ, the comment function in Word documents can also be considered as a type of labeling. The labels that are added are assigned with information (called classes) such as "car", "person", "traffic light", etc. to indicate what the object is. This labeled data becomes the training data for AI learning.

1-2. What is the difference between data annotation?

Many of you may have seen explanations about "data annotation" in order to create training data. Our company also has a blog that explains "data annotation".

>>What is Data Annotation? Explanation from its meaning to its relationship with AI and machine learning.

Although they may seem to have different meanings at first glance, "data labeling" and "annotation" can be considered the same when it comes to creating training data. However, they also have different meanings as English words. Since deep learning and other AI learning methods have mainly developed in English-speaking countries (especially the United States), let's take a look at how these two words are treated in English.

1-3. Differences in Terminology Usage between Japan and Overseas

In English, "annotation" originally means "to add notes or comments to a text". For example, it refers to adding an asterisk or underline to a specific part of a text and inserting a note. When creating training data, a similar process is used where marks are added to the data and classes are assigned, which is why it is called "annotation". On the other hand, "data labeling" also refers to the same process of adding labels, such as price tags, to products to provide information such as product name and price, and is also used when creating training data.

When searching for the word "annotation" on the web, content related to writing and proofreading, such as "how to add annotations to a text," tends to rank higher. On the other hand, in the case of "data labeling," content related to AI tends to rank higher, so it seems that "data labeling" is more commonly used to refer to creating training data. For example, in the United States, there are tools for creating training data with names like "labelimg" and "labelme" that evoke the idea of labeling. Additionally, on the blog of a company called Superannotate, which provides online tools, the phrase "be labeled" appears frequently.

However, both "data labeling" and "annotation" can be considered as the same word that refers to the task of creating training data. However, "data labeling" evokes a more specific action, so it seems to be used more frequently in tool operation instructions and training data work procedures. Also, the person who creates the training data is called a "data labeler".

On the other hand, in Japan, the process of creating teacher data is often referred to as "data annotation". It is common in Japan to refer to the person creating the teacher data as a "data annotator".

2. Market Size of Data Labeling

Various reports have been released on the market size of data labeling, each with different numbers. However, all reports indicate that the data labeling market is expanding. Here, we will refer to a report released by Markets and Markets, a market research company, in February 2023 (Report), and an analyst note released by UBS in July 2023 (Analyst Note), to explain the market size of data labeling.

2-1. Current Market Growth Rate

As of 2022, the global market size for data labeling is 800 million USD. According to reports, it is predicted to expand to 3.6 billion USD by 2027, with an annual growth rate of 33.3%.

2-2. Forecasting Trends in the Future Data Labeling Market

The market size of data labeling continues to expand, as reported not only by Markets and Markets but also by other reports. The main factors driving this expansion include the advancement of AI technology and the widening range of AI applications. Among these, the main factor for expansion is the increase in demand for medical imaging in the healthcare field. This includes the use of image diagnosis and medical robots that do not require healthcare professionals, as well as document searches for various medical records, papers, and documents issued during the development of new drugs (where the use of AI OCR and natural language processing technology specialized in medical terminology will also be necessary).

2-3. Relevance to Next-Generation Technology and Industry Development

UBS, a major Swiss bank, announced in an analyst note on July 25, 2023 that they have raised their long-term AI demand forecast from an average annual growth rate of 20% over the next 5 years (from 2020) to 61% over the next 5 years (starting in 2022). This is believed to be due to the rapid spread of generative AI, such as ChatGPT. These developments in AI are not seen as a temporary growth like the AI bubble, but rather a long-term trend.

>>UBS predicts 61% average annual growth rate for AI demand from 2022 to 2027

Data labeling is expected to play an important role in the learning of AI in the field of generative AI. Considering the expansion of its use in the medical industry as mentioned earlier, it can be said that the range of AI utilizing data labeling will continue to expand in the future.

>>What AI and Machine Learning Can Do. 12 Examples of Utilization by Industry.

2-4. Impact of the Increasing Demand for Data Labeling

The increasing demand for data labeling will create new employment opportunities. Data labeling can be done remotely depending on the security level, making it possible to secure a wide range of talent without limiting the region or time zone.

On the other hand, if data contains information related to privacy or security, there is a concern for leakage or leakage if the data is not handled in an appropriate environment. In particular, in data labeling for medical-related data, which is expected to increase in demand in the future, it is considered that such data is often handled. In order to ensure a secure management system in terms of security, data labeling vendor companies not only focus on building a secure remote work environment, but also need to prepare a security room and be able to respond on-site and at the client's site, which is more important than ever before.

3. What are the benefits of outsourcing data labeling tasks?

Data labeling currently requires human resources. In addition, labeling a huge amount of data ranging from thousands to hundreds of thousands can often take weeks to months. In companies that develop AI, development engineers may handle this labeling, but it can also take away from their time for actual development tasks. Here, we will explain the benefits of outsourcing data labeling tasks.

3-1. Possibilities for Cost Reduction and Efficiency Improvement

Data labeling is different from AI development tasks and does not require programming skills or specialized knowledge in AI engineering. Furthermore, it is a time-consuming task in the AI development process. If engineers were to do this, it would incur costs outside of their original development tasks. Additionally, even if a company were to secure personnel for labeling, it would be wasted if there were no labeling tasks. Therefore, considering the effort of securing personnel and managing data labeling, it is more beneficial to outsource to a specialized vendor rather than doing it in-house, in order to reduce costs and improve efficiency.

3-2. Utilizing Expertise and Skills

Data labeling does not require the expertise of AI development, such as engineering, but in order to maintain data quality and meet deadlines with high productivity, it is necessary to have appropriate management skills as well as expertise and know-how related to labeling. By outsourcing to external vendors, you can leverage the expertise and know-how unique to data labeling.

3-3. Points to Consider When Outsourcing Data Labeling Tasks

When outsourcing, it is recommended to discuss with multiple vendors while keeping in mind the above benefits, including the track record and quality assurance of data labeling that meets the purpose of our in-house AI development, as well as security measures. Please also refer to our blog below for reference.

>>How to Outsource Data Annotation Work? 7 Tips

4. Human Science's Achievements

We would like to introduce an interview article featuring feedback from our clients, selected from our past achievements. Please take a look.

>>Outsourcing for fast and accurate data annotation - Ensuring accuracy and reliability of machine learning systems - (Sumitomo Heavy Industries, Ltd.)

5. Data Labeling Outsourcing Service by Human Science Co., Ltd.

Rich track record of creating 48 million pieces of teacher data

At Human Science, we are involved in AI model development projects in various industries such as natural language processing, medical support, automotive, IT, manufacturing, and construction. Through direct transactions with numerous companies including GAFAM, we have provided over 48 million high-quality training data. We handle various annotation projects regardless of industry, from small-scale projects to large-scale projects with 150 annotators. If your company is interested in introducing AI models but unsure of where to start, please consult with us.

Resource Management without Using Crowdsourcing

At Human Science, we do not use crowdsourcing and instead directly contract with workers to manage projects. We carefully assess each member's practical experience and evaluations from previous projects to form a team that can perform to the best of their abilities.

Utilize the latest data annotation tools

One of the annotation tools introduced by Human Science, AnnoFab, allows customers to check progress and provide feedback on the cloud even during project execution. By not allowing work data to be saved on local machines, we also consider security.

Equipped with a security room within the company

At Human Science, we have a security room that meets the ISMS standards in our Shinjuku office. This allows us to provide on-site support for highly confidential projects and ensure security. We consider confidentiality to be extremely important for all projects at our company. We continuously provide security education to our staff and pay close attention to the handling of information and data, even for remote projects.