What is Unstructured Data? Explanation of how to utilize Unstructured Data

1. Increase in the Importance of Data Utilization

Although it is not a new concept, data-driven management and organizational operation are being emphasized in various places. Data is being digitized in all directions and accumulating, but there are also large amounts of untouched data that are left unattended. Many companies are facing these challenges. It goes without saying, but in recent years, utilizing this data to create new value has become even more important for companies and organizations. This time, I would like to talk about "labeling" as a means of utilizing internal data.

2. Structured Data and Unstructured Data

When utilizing data, in addition to the use of structured and organized data up until now, the key to data utilization in recent times is how to effectively use unstructured data.
Structured data refers to data that can be expressed in "columns" and "rows" using tools such as Excel or CSV, making it easy to search, aggregate, and compare, and can be immediately used for analysis and analysis. This includes traditional DB-structured data, which has been widely used in conventional business systems such as ERP.

On the other hand, unstructured data is not structured like the above and it is difficult to use it as it is or to extract necessary information mechanically. In order to analyze, organize, and utilize it, it is necessary to add attributes or metadata and process it in some way.

Unstructured data includes various types of data such as text data from emails, SNS, customer reviews, video data from promotional materials, and audio data from call logs. By incorporating and analyzing this unstructured data, companies can obtain more diverse and multi-faceted information, allowing them to create new services and value, and enabling them to differentiate themselves from competitors and solve comprehensive management issues.

3. To Utilize Unstructured Data

To utilize unstructured data, it is necessary to organize the data by adding attributes and metadata that represent the characteristics of the data. This requires tasks such as "tagging" and "data labeling".
Today, with the advancement of AI technology, tools have emerged that use AI to analyze the characteristics of data and automatically create metadata, and they are being used in various fields.
However, these tools are not universal, and in cases such as the following, it is difficult to automatically label using AI technology, and in many cases, diligent labeling work by hand is still necessary.

・If specialized knowledge is required
・If the data format is complex
・If context or reading between the lines is necessary for judgment or classification, or if human sensitivity is required

4. Unstructured Data Labeling Service

Whether using AI or not for utilizing unstructured data, labeling unstructured data can shed light on siloed and unorganized data within the company and promote utilization, which can be considered as the first step towards further value creation.

Our company's services started with AI development data annotation and data labeling. Unstructured data is often ambiguous and in order to utilize it, it is necessary to clearly define the goal and classify and label it accordingly. This often requires experience, know-how, and a large amount of resources, making it a shortcut to escape fierce competition and reach the goal by entrusting it to a specialized company with expertise.

Through our data annotation service for creating AI teaching data, we have accumulated knowledge and insights by labeling various unstructured data. In addition to data annotation for creating AI teaching data, we also support labeling, attribute assignment, classification, and data cleansing of unstructured data for various companies' internal data utilization.

Through providing data annotation services, we utilize the experience and know-how gained not only in AI development, but also in labeling unstructured data. We actively support unstructured data labeling services in order to work closely with our clients in creating new value through data-driven management and utilizing data accumulated within the company.
If you are unsure if your AI model is optimal or if you want to effectively utilize unstructured data within your company, please do not hesitate to consult with us.

5. Human Science's Data 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 for a large amount of data such as unsorted and uncategorized promotional videos and compile them into Excel or CSV, as well as add and describe label information to image and text data, and handle various input and output data.

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.