Data Annotation Tool Market Size, Share, Statistics, Trends, Competitive analysis, Regional Growth from 2022 to 2030

The Global Data Annotation Tool Market is expected to grow at a CAGR of around 34.3% from 2022 to 2030 and expected to reach around US$ 14.7 Billion by 2030.

North America dominates the data annotation tool market; Asia Pacific to record fastest growing CAGR

North America has held the lion's share of the data annotation tool market in the past and is expected to do so again during the forecast period. The rapid product expansion based on geography is attributed to the growth, which is widely adopted by the key players residing in this region to expand and gain a competitive edge in the regional market. This is one of the most important factors that promote the growth of data annotation tools market.

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Asia Pacific, on the other hand, is expected to have the fastest growing CAGR in the data annotation tool market. The data annotation tool market in the Asia Pacific region is expanding rapidly as a result of the widespread adoption of advanced technologies, particularly in the healthcare and financial services sectors. As the healthcare sector grows in popularity in the APAC region, so will the demand for data annotation tools, which will grow at an all-time high CAGR in the coming years.

COVID-19 impact on the global data annotation tool market

The COVID-19 pandemic has affected every aspect of society on a global scale. The government's stringent regulations imposed globally to combat the spread of the corona virus have resulted in supply chain disruption. Furthermore, the pandemic's nationwide lockdown and temporary shutdown of manufacturing facility units resulted in decreased labor on the supply side and a decrease in demand for several products and services globally. These stringent restrictions have a negative impact on the market for data annotation tools. It is estimated that as the economy will get on track the data annotation tool market will record a significant demand globally.

Segmental Outlook

The global data annotation tool market is segmented as type, annotation type, and vertical. Based on type, the market is segmented as text, image/video, and audio. By annotation type, the market is segregated as manual, semi-supervised, and automatic. Based on vertical, the market is segmented as IT, automotive, government, healthcare, financial services, retail, and others.

According to annotation type, the manual segment dominates the global data annotation tool market. Furthermore, based on vertical, the IT segment dominates the global data annotation tool market with a sizable revenue share. The text segment will dominate the global data annotation tool market in terms of type.

Competitive Landscape

The prominent players of data annotation tool involve Annotate.com, CloudApp, Appen Limited, Deep Systems, Cogito Tech LLC, Labelbox, Inc., Playment, and among others.

Some of the key observations regarding data annotation tool include:

• In February 2021, Appen Limited announced enhancing capabilities that ensure autonomous vehicles manufacturers have access to high-quality training data and can get the most value from the training data investment.

• In July 2021, TELUS International, announced the acquisition of Playment, a leader in data annotation and computer vision tools and services specialized in 2D and 3D image, video, and LiDAR technologies. Through this acquisition, TELUS International team will harness the full potential of computer vision technology and advances joint mission of expediting the AI age through much partnership with brands present globally.

• In February 2021, NextBillion AI announced a partnership with Google Cloud for the deployment of data pipelines. As NextBillion AI is an industry leading startup involved in deployment of a platform used for mapping that offers software-as-a-service (SaaS) for enterprises.

• In November 2017, Appen Limited, announced signing of a definitive agreement to acquire Leapforce Inc. to strengthen their expertise in artificial intelligence and machine learning with high-quality human annotated data.

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This release was published on openPR.