Abstract:Data processing includes data cleaning, data annotation, data review, etc., which is essentially a process of improving the quality of data resources, and the higher the quality of data resources, the greater their value. Due to the increasing proportion of unstructured data, the demand for the data annotation industry has increased steadily, and a stable growth industry has been formed, and the market scale of the market annotation industry has been expanding, and the demand for image and voice accounts for more than eighty percent. In 2022, the market size of China's data labeling industry will be about 5.1 billion yuan.
1. Definitions and Classification
Data annotation refers to the behavior of manually labeling data content such as pictures, voices, texts, and videos with the help of specific software annotation tools, so that the computer can learn a large amount of data with feature labels, and finally have the ability to independently identify features. As a necessary part of providing training data, data annotation technology has promoted the rapid development of artificial intelligence. Common data annotation can be divided into image annotation, text annotation, and voice annotation according to the data type.
Second, the business model
1. Crowdsourcing mode
Nowadays, data annotation usually adopts the crowdsourcing model, and the advantage of the crowdsourcing model is that the cost is lower and the response is faster. This style is suitable for simpler items such as dotted pull boxes. Publishers often send detailed information and questions to the platform for the majority of data annotation part-time staff to answer. However, there is an obvious problem with the crowdsourcing model, that is, the quality is difficult to control, everyone's understanding of the rules is different, and it is inevitable that some people will answer the tasks randomly, which will affect the quality of the project. To this end, the platforms will also use some methods to reduce the occurrence of problems and improve the quality of the project. In addition, in order to prevent misjudgment and safeguard the interests of the answerers, an appeal link will be set up to enable the respondents to appeal the questions in question. Set the level of the annotator, the higher the accuracy rate of the annotator's task, the more answers, the more the level can be slowly increased, more tasks can be unlocked, more task rewards, and have the opportunity to enter the judgment correction link to become a judge.
2. Outsourcing model
The outsourcing model is opposite to the crowdsourcing model, which is to outsource the task to a dedicated data labeling company and team, and at the beginning of the project, the project as a whole will be evaluated, and then the project as a whole will be quoted, and the data labeling company will arrange training and manpower by itself, and only need to ensure that the data is delivered with quality and quantity before the project deadline. The advantage of this model is that the data quality and project cycle are guaranteed. However, the response speed is slower and the cost is higher, because the bidding needs to be arranged at the beginning, and the platform needs to arrange dedicated project personnel for project docking and project follow-up. Nowadays, there are many teams specializing in data annotation in China, but most of them are mainly studios and small teams of dozens of people, and the business type is concentrated on simple pull box image annotation. There are also some larger companies, such as Mengdong Technology in Guizhou, which has formed industrialization and driven local development. Or "Dianwo Technology", they have their own platform, can develop their own tools, and play both the roles of data labeling platform and data labeling company.
3. Industry policy
With the continuous growth of the data element market, all participants in the data element market are engaged in market operation. As the manager of the data element market, the government will play a role in policy support and active guidance, promote the expansion and opening of public data, and build an open data platform. The intensive issuance of relevant policy documents has promoted the rapid development of the mainland's data industry, with continuous technological progress, continuous improvement of infrastructure, and continuous deepening of integrated applications. In January 2024, 17 departments including the National Data Bureau issued the "Data Element ×" Three-Year Action Plan (2024-2026), which pointed out that the "Action Plan" selected 12 industries and fields, including industrial manufacturing, modern agriculture, commerce and trade circulation, transportation, financial services, scientific and technological innovation, cultural tourism, medical and health, emergency management, meteorological services, urban governance, and green and low-carbon, to promote the multiplier effect of data elements and release the value of data elements.
Fourth, industry barriers
1. Technical ability barriers
With the advent of the era of large models, the data annotation industry has higher and higher requirements for technical capabilities. Enterprises need to have strong data processing capabilities, including the intelligence level of the data closed-loop tool chain, the understanding of large models/AI algorithms, data engineering capabilities, and infrastructure construction. The lack of these technical capabilities can limit the growth of enterprises, especially when it comes to automating annotation and processing complex data sets.
2. Scene resource barriers
Data annotation services need to be closely integrated with specific application scenarios, which means that enterprises must have high-quality scenario data and corresponding domain experts or in-depth users. The acquisition and maintenance of these resources requires a significant time and cost investment, and the lack of these resources can be a barrier to market entry for new entrants.
3. Industry experience barriers
The development of the data annotation industry requires a wealth of industry experience, including a deep understanding of customer needs, optimization of the data annotation process, and the establishment of long-term relationships with customers. New entrants lack this experience and struggle to quickly adapt to market changes and customer needs, putting them at a disadvantage from the competition.
Fifth, the industrial chain
Data annotation is located in the middle of the industrial chain and is an important part of the commercial application of AI. The upstream of the industry is data sources and data capacity, multiple data include personal data, enterprise data, government data, etc., and capacity hospital suppliers include labeling voluntary providers and hardware resource suppliers. The midstream is a data annotation vendor, including AI basic data service providers, such as Haitian AAC. The downstream is the application of artificial intelligence, involving smart government, finance, industry, autonomous driving and other fields. Among them, the midstream AI basic data service providers mainly collect and annotate data, while the AI-oriented data governance platform service providers use the components of data governance to manage multi-source heterogeneous data, so that it can form data assets and improve data quality. The processed data can be directly provided to the downstream for AI training, thereby accelerating the implementation of AI.
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