Five core demand pain points in the field of artificial intelligence data labeling丨manfu technology
Posted Jun 16, 2020 • 4 min read
With the commercialization of artificial intelligence in the fast lane, areas such as unmanned driving, face recognition, and smart security have become popular application scenarios. The focus of AI companies has begun to focus on industrial landing capabilities.
As the foundation of the artificial intelligence industry, data is one of the decisive conditions for achieving this capability. Therefore, providing high-quality labeled data services for machine learning algorithm training has become one of the important conditions that determine the height of artificial intelligence applications.
Relevant data statistics show that the amount of data generated in 2025 will be as high as 163ZB, of which 90%is unstructured data. Only after cleaning and labeling can these unstructured data be awakened in value, which has created a constant demand for cleaning and labeling. The data labeling industry has therefore been able to prosper and expand rapidly.
As the industry has become an industry development trend, more forward-looking massive data set products and highly customized services have become the main service form of the data annotation industry. However, due to the problems of low threshold and uneven service quality in the data labeling industry, the demand side often encounters pain points such as data quality, service efficiency, data security, management capabilities, and service capabilities when selecting data services. It has become a core issue hindering the development of the industry.
1. Data quality
The training of deep learning algorithms under supervised learning is very dependent on labeling data. The quality of the data set will directly determine the effect of the algorithm model.
However, there are serious data quality problems in the data labeling industry. Relevant data show that the current data labeling industry's single delivery compliance rate is less than 50%, and the delivery rate within three times is less than 90%, which is far from meeting the needs of AI companies.
The demand side hopes that the data service company can improve the accuracy of the first delivery project and significantly reduce rework.
2. Service efficiency
At present, the mainstream project operation methods of the data labeling industry are mainly "crowdsourcing" and "subcontracting". It is difficult for data service companies to directly and effectively manage the labeling team, so project extension has become a normal state.
For the demand side, the project delay means losing the first-mover advantage in the fierce business competition, so for the demand side, it is hoped that the data service company has an efficient project execution system to improve work efficiency and can complete the project on time or even in advance.
3. Data security
The particularity of the data labeling industry means that it is often exposed to a lot of sensitive data, such as face data, license plate data, etc. The storage and transmission of these data have extremely high security requirements.
Therefore, the demand side hopes that the basic data service provider has a clear and specific security management process, and pays enough attention to the data transmission, storage, and data destruction after the completion of the project.
4. Management ability
Under the "crowdsourcing" and "subcontracting" models, it is difficult for companies with weak management capabilities to focus on servicing customers with high quality while taking care of multiple projects. Such consequences are project delays and poor data quality.
Therefore, the demand side hopes that the data service enterprise can establish a perfect internal management process, optimize the project process experience, and achieve both the improvement of efficiency and quality.
5. Service capacity
The data labeling business is also essentially a service business. From the project docking to the end of the final project, each link requires continuous discussion between the demand side and the data service company to make the optimal solution.
Therefore, the demand side hopes that the data service company can actively cooperate and respond quickly during the project, and can make certain optimization suggestions for the project.
The above five points are the core demands of the demand side for data labeling companies. If these five points correspond to the corresponding scores, then the higher the overall score means that the more the demand side meets the requirements of the demand side, the more able to occupy the exclusive in fierce competition Advantage.
For data labeling companies, implementation based solely on client projects is slightly passive, with low subjective initiative and limited industry boundaries. The products and services of various data labeling companies will tend to be homogenized and competition will intensify. Not conducive to its own development, but also restricts the development of the AI basic data service industry.
Therefore, to make changes actively and cater to the core demands of the demand side, data service companies can establish differentiated advantages in the fierce market competition, especially in the context of accelerated commercialization of AI, can establish a set in vertical scenarios The complete data overall solution will add important advantages in the future market competition.