The Global Data Annotation Tools Market the size is expected to reach USD 3.4 billion by 2028. The market is expected to expand at a CAGR of 38.3% from 2022 to 2030. The advent of big data is expected to drive the growth of the artificial intelligence market in so much information has to be recorded, stored and analyzed. The adoption of artificial intelligence is expected to significantly boost the market growth, as annotated information acts as a catalyst to train AI models and machine learning systems in critical areas such as speech recognition and recognition pictures. These tools offer AI its strength by directly providing relevant information to determine future outcomes and decision-making.
COVID-19 Impact Analysis
In this report, market players in this field will receive information on the impact of COVID-19 after COVID-19, which will provide a useful framework to evaluate strategies. COVID-19 has affected a number of manufacturing operations and supply chains around the world. As a result, the economy experienced a recession and high inflation rates. Additionally, the pandemic has disrupted the international movements of a number of industries. Due to labor shortages and lack of logistical movement, the entire industry has faced sustained challenges. Lockdowns have caused severe financial losses to end-user industries.
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Currently, there is a growing trend of manufacturing autonomous vehicles in the automotive industry, which attracts greater investment for the development of these vehicles. An autonomous vehicle includes a combination of various sensors and networking systems that help the computer drive the vehicle. Annotated information allows computer models of autonomous vehicles to recognize and learn from it. Several technology providers such as Google LLC; Tesla Motors; Apple Inc.; and Huawei Technologies Co., Ltd. have also entered the autonomous vehicle market. Rising investment in the autonomous driving market is expected to drive the future demand for the data annotation market. For example, in February 2021, Appen Limited, a provider of effective training data and AI systems, announced the launch of new training data annotation and quality assurance services for autonomous vehicle manufacturers.
Growing innovations in the retail segment, especially in improving the e-commerce sector, using the information tagging tools are expected to drive the market demand over the forecast period. For example, in February 2019, Walmart Labs acquired Dataturks, an India-based data annotation startup. Dataturks offers annotation tools for image and text information using machine learning. The acquisition helped drive innovations in catalog quality and other aspects of the Walmart merchandising platform.
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Highlights of the Data Annotation Tools Market Report
- The growing demand for machine learning in automated data analysis is expected to increase the demand for automatic information labeling tools in various data-driven applications. Furthermore, the growing attention on image annotation is expected to improve the operations of automotive, retail, and healthcare industries and hence is expected to propel the growth of the market.
- In terms of annotation type, the manual segment captured the largest revenue share in 2020. Attributes such as accuracy, ability to capture edge cases, and smart human resources are effective enough to ensure high quality on large volumes of data. Which makes manual annotation tools very suitable for training machine learning algorithms for computer vision applications
- Informative labeling tools are gaining acceptance in the automotive industry, especially for autonomous vehicles. High-resolution cameras, LIDAR sensors and a huge amount of information are needed to create the training datasets for these highly sensitive visual perception models in autonomous vehicles.
- To gain a competitive edge in the market, major players have started adopting the strategy of customer expansion by raising funds for the development of their platform. For example, in February 2020, Labelbox, Inc. raised approximately $25 million in Series B funding to enhance its platform with data labeling capabilities for AI training models. The funds were used to accelerate computer vision and machine learning models by increasing the size of the company’s sales and engineering teams.
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