What is data labeling? Examples of data organization and utilization are also introduced

03/19/2024

What is data labeling? Examples of data organization and utilization are also introduced

The movement to utilize data has been accelerating in recent years. With the advancement of AI technology and the expansion of its application range, various types of data, such as text data from emails and chats, as well as images and videos, which were previously difficult to utilize despite being accumulated, are now starting to be utilized in a more accessible environment.

That said, in order to utilize such data with AI, in many cases, it is necessary to develop the AI, which incurs development costs. If you want to make use of the data you have but would like to start with organizing and classifying the data, instead of jumping straight into AI development, you can first classify and organize it manually, and then consider AI implementation based on the optimal goals you have set.

One way to classify data is to assign labels that indicate what type or content the data represents. In AI development, this similar task is referred to as annotation, but this time we will explain labeling and data labeling that do not involve AI development, along with examples of their use.

Table of Contents

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

1. What is data labeling in AI development?

AI development may require a task called labeling. This is referred to as labeling or data labeling. For image labeling, you can imagine a task such as "in an image where a car is present, enclosing the car in a bounding box and assigning the label 'car' to that box." There are various methods for this task, including dedicated tools for labeling or recording labels in Excel, but all of them are done manually. While there are some tools and processes that can be automated, in many cases, it is performed almost entirely by hand.
Labeled data is used by AI to learn necessary information from the data through 'supervised learning'.
These are generally referred to as annotations, but overseas (especially in the United States), the term data labeling is also widely used.

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

We believe that data labeling and annotation are still often effective for purposes other than AI development. In particular, unstructured data, commonly referred to as such (like images, videos, and informal texts such as meeting minutes), can often be unclear in terms of their content. By establishing rules and policies for labeling and categorizing this data, it becomes possible to search and classify it using traditional methods, thereby opening up avenues for effectively utilizing data that could not be leveraged before, even without the use of AI.

Of course, while the introduction of AI has been a hot topic for some time now, before considering or implementing AI without a clear goal in sight, starting with the organization of unstructured data may be a shortcut to utilizing data without incurring unnecessary development costs. To train AI on unstructured data, labeling is indeed necessary. However, organizing unstructured data can be done without assuming the use of AI, and even that alone can enable the organization and utilization of information through traditional methods, leading to the creation of new businesses and new value.

How about considering the use of AI as an option in necessary situations after going through such processes?

3. Examples of Labeling for Data Organization and Utilization

Here, we will introduce examples of data labeling as mentioned above.
One of our clients has a large number of advertising images and videos, with each person in charge placing orders, managing, and filing independently, without a unified set of rules within the company, and the data is not centrally managed. Additionally, the copy and advertising images aimed at the target audience are created based on the experience and intuition of each person in charge. As a result, the effectiveness of the advertising copy and images in reaching the target audience and driving sales depends on the individual experiences and intuitions of each person in charge.
In order to conduct efficient and data-driven marketing and promotional activities, we are considering the future implementation of AI, but to what extent can AI be utilized? Initially, we would like to assess this after organizing and database-ing such unstructured data.

When we started working, there were a large number of types of labels for classifying images and videos, many of which were subtle in judgment. As we progressed with the labeling, we discovered new types of images and videos that we had not initially anticipated, and the variety of labels continued to increase. This is just my personal opinion, but I felt that if we had proceeded with the introduction of AI right away, we probably would not have achieved good results. In that sense, I felt that the client had a very grounded and wise vision based on their current situation.

4. Summary

In this way, you may have understood that data labeling is an effective method for utilizing unstructured data that is currently dormant within your company, serving as a means of data organization and utilization, as well as a preliminary step towards AI implementation.
Our company actively accepts data labeling for utilizing other unstructured data through annotations for AI learning. Furthermore, if you are unsure about how to utilize the data you have on hand and what technologies to employ, we encourage you to consult with us. If necessary, we can also introduce you to our development partner companies.

5. Human Science Annotation and Data Labeling Services

A rich track record of creating 48 million pieces of training data

At Human Science, we participate in AI model development projects across various industries, including natural language processing, medical support, automotive, IT, manufacturing, and construction. To date, we have provided over 48 million high-quality training data through direct transactions with many companies, including GAFAM. We handle a wide range of annotation and data labeling, from small-scale projects to long-term large projects with 150 annotators, regardless of the industry.

Resource management without using crowdsourcing

At Human Science, we do not use crowdsourcing; instead, we advance projects with personnel directly contracted by our company. We form teams that can deliver maximum performance based on a solid understanding of each member's practical experience and their evaluations from previous projects.

Support for various data according to your needs

We handle a variety of input and output data, from labeling attributes of large amounts of unorganized and uncategorized data such as videos and compiling them into Excel or CSV, to adding label information to images and text data and describing them. 

Equipped with a security room in-house

At Human Science, we have a security room that meets ISMS standards within our Shinjuku office. This allows us to handle even highly confidential projects on-site while ensuring security. We consider the protection of confidentiality to be extremely important for all projects. Our staff undergoes continuous security training, and we exercise the utmost caution in handling information and data, even for remote projects.