There were 85 press releases posted in the last 24 hours and 202,062 in the last 365 days.

New Trends and Challenges in Data Annotation Industry

ByteBridge real-time process and cost estimation dashboard

ByteBridge real-time process and cost estimation dashboard

Labeled data has been compared to a well-known phrase: new oil that business needs to run.

BEIJING, CHINA, April 25, 2022 / -- Data Annotation Market Size
The market size of data annotation tools surpassed $1 billion in 2021 and is expected to record more than 30% CAGR during 2022-2028, says GMI report. The booming data annotation market is witnessing tremendous growth in the forthcoming future.

The data annotation industry is driven by the increasing growth of the AI industry. At present, the commercialization of artificial intelligence has reached a stage of basic maturity in terms of computing power and algorithm. In order to better meet the landing needs and solve specific pain points in the industry, scalable annotated data for algorithm training is indispensable.

It is said that data determines the success of AI implementation. In addition, forward-looking data products and highly customized data services have become the mainstream of industry development.

Trend: Industry Reshuffles, Intensifying Competition
In the next few years, the data annotation industry will have the following trends and challenges.
From the micro point of view, the continuous expansion of the market means more participants and more competition. Due to the low entry threshold and the excessive dependence on human resources, a large number of small and medium-sized data service providers are clustered in the industry.

However, with the improvement of the technical threshold, the various demands of AI enterprises, and the increase in labor costs, small and medium-sized data service providers will face increasing cost pressure. In the next 1–2 years, the industry will likely usher in a wave of “shuffling period”.

With the speeding up of commercial landing, the AI companies also put forward new requirements for data service. The quality, refinement, and customization are more and more popular on the demand side. On the supply side, technical strength, and management ability have brought new challenges.

Challenges: the Outmoded Industry Development Under the New Demand
As mentioned, more forward-looking data products and highly customized data services have become the mainstream of industry development. However, the current level of the data labeling industry is far from meeting these new needs. The data annotation industry faces the following challenges:

1. Different industries and business scenarios have different requirements for data annotation. The existing annotation ability is not refined enough to support customization services.

Data annotation has a wide range of application scenarios, including autonomous driving, intelligent security, new retail, AI education, industrial robots, intelligent agriculture, and other fields.

Different scenarios have different labeling requirements, for example, the automatic driving industry mainly focuses on pedestrian recognition, vehicle identification, traffic lights, road recognition, etc. The security industry mainly focuses on face recognition, face detection, visual search, key points, and license plate recognition.

2. Customer pain points: low labeling efficiency, poor data quality, lack of human-machine cooperation.
The particularity of the data annotation industry determines its high dependence on manpower. Currently, the mainstream annotation method is that the annotator completes the work with the help of labeling tools.

Due to the uneven ability of the annotators and the imperfect functions of the annotation tools, the data service is always found deficient in efficiency and data quality.

In addition, many data service providers ignore or do not have human-machine cooperation capability. In fact, the AI-assisted tool can not only improve efficiency but also improve accuracy.

3. Data labeling service providers, who rely on crowdsourcing and subcontracting, fail to guarantee quality.
At present, data labeling mainly relies on human resources, and human resources account for the most part of the total cost. Therefore, many data service providers give up their in-house labeling teams and turn to subcontracts to complete the labeling business.

Compared to the in-house labeling team, crowdsourcing and subcontracting have lower costs and become more flexible. However, the labeling loop is too long to cooperate and data quality is difficult to control.

To sum up, the data annotation industry has a broad prospect, but it also faces many challenges. In the foreseeable period of industry transformation, both medium-sized and large-sized data service providers cannot avoid the changement. Only by enhancing the self-developed technical strength and by speeding up the evolution can they be competitive in the new era.

ByteBridge is a human-powered and ML-powered data labeling tooling platform. We provide scalable, high-quality training data for the ML/AI industry. On the dashboard, we support end-to-end data labeling solutions including visualizing the labeling rules, and all the processes are managed in real-time. As individuals can decide when to start and end the task, the by yourself service makes it possible to engage and take control of the labeling loop. Meanwhile, transparent pricing lets you save resources for the more important parts.

Anna QI
Visit us on social media: