What is Data Labeling? Introducing Examples of Data Organization and Utilization

The movement to utilize data has been accelerating in recent years. With the development of AI technology and its wider application, it has become possible to utilize various types of data such as text data from emails and chats, as well as images and videos, which were previously difficult to utilize as they were only accumulating.

However, in order to utilize this data with AI, in many cases, AI development must be carried out, which incurs development costs. If you want to use the data at hand, but first want to start with organizing and classifying the data, instead of immediately working on AI development, there is also a method of first classifying and organizing by human hands, and then considering AI implementation by setting the optimal goal.

To classify data, there is a method of attaching labels that indicate what type or content the data is. In AI development, this process is called annotation, but in this case, we will explain the use cases and examples of labeling and data labeling that do not involve AI development.

Table of Contents

1. What is data labeling in AI development?
2. What is data labeling and annotation for data organization?
3. Labeling Examples for Data Organization and Utilization
4. Summary
5. Human Science's Data Annotation and Labeling Outsourcing Service

1. What is data labeling in AI development?

Data annotation may be necessary for AI development. This is also known as labeling or data labeling. For example, for labeling images, you can imagine the process of "enclosing a car in a bounding box in an image of a car and labeling that box as "car". There are various methods for this process, such as using specialized tools or recording labels in Excel, but all of them are done manually. There are some automated tools available, but in most cases, it is done almost entirely by hand.
The labeled data is used for AI to learn necessary information from the data through "supervised learning".
These are generally referred to as annotations, but in overseas (especially in the United States), the term data labeling is also widely used.

2. What is data labeling and annotation for data organization?

At our company, we believe that data labeling and annotation, also known as data annotation, is still effective for purposes other than AI development. Especially for unstructured data, such as images, videos, meeting minutes, and other non-structured text, it is often difficult to understand the content without any context. By establishing rules and guidelines for such data and labeling them, it becomes possible to search and classify them using traditional methods. This opens up the possibility of utilizing data that was previously not effectively utilized, even without the use of AI.

Of course, AI implementation has been called for a long time, but before considering or implementing AI without a clear goal, it may be a shortcut to data utilization to start with organizing such unstructured data without wasting development costs. In order to train unstructured data with AI, labeling is also necessary. However, even without assuming the use of AI, it is possible to organize unstructured data, and with that alone, information organization and utilization can be achieved with conventional methods, and new businesses and creation of new value can be expected.

Why not consider utilizing AI as an option in the future, after going through such a process?

3. Labeling Examples for Data Organization and Utilization

Here, we would like to introduce an example of data labeling as shown above.
One of our clients has a large number of advertising images and videos, and each person in charge is responsible for ordering, managing, and filing them individually. There is no unified rule within the company, and the data is not centrally managed. In addition, the copies and advertising images that appeal to the target audience are created based on the experience and sense of each person in charge. Therefore, the copies and images of the advertisements depend on the experience and sense of each person in charge, making it difficult to determine which target audience is being reached and how it is contributing to sales.
In order to conduct efficient and data-driven marketing and promotional activities, we are considering AI in the future, but how much can AI do? The request was to first organize and database such unstructured data and then determine what can be done.

When we actually started working, there were a large number of label types for classifying images and videos, and many of them were difficult to judge. As we progressed with labeling, we also discovered new types of images and videos that were not initially anticipated, and the number of label types continued to increase. This is just my personal opinion, but I felt that if we had just introduced AI without considering the current situation, the results would not have been as good. In that sense, I felt that the client had a very grounded and wise plan based on their own situation.

4. Summary

As a first step in utilizing the unstructured data that is sleeping in your company, data labeling is effective as a means of data organization and utilization, as well as a pre-stage of AI implementation.
At our company, we actively accept data labeling for utilizing non-structured data through annotation for AI learning. In addition to AI implementation and the accompanying annotation and labeling, if you are wondering how to utilize the data at hand and what technologies to use, please feel free to consult with us. If necessary, we can also introduce you to our development partner companies.

5. Human Science's Data Annotation and Labeling Outsourcing Service

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 many companies including GAFAM, we have provided over 48 million high-quality training data. We handle various annotations and data labeling, from small-scale projects to large-scale projects with 150 data annotators, regardless of industry.

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.

Corresponds to various data according to your request

We will label attributes to a large amount of data such as unsorted and uncategorized videos and compile them into Excel or CSV. We also provide labeling and description of label information for various input and output data, from images to text information. 

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.