Data Annotation Tools Market Size, Share, Manufacturers, Types, Applications, Share, Growth Rate and Forecast 2022-2030

Astute Analytica released a new research report on the global Data Annotation Tools Market. The worldwide Data Annotation Tools Market report 2030 is a thorough investigation that examines the current Data Annotation Tools Market trends. The report consists of market definitions, market segmentation, end-use applications, and industry chain analysis. In addition, it offers a succinct overview of the market. The study on the global market offers an overview of the market encompassing the competitive environment, current market developments, and industry trends.

The global Data Annotation Tools Market 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 competition study provides information about the major players in the Chinese market in terms of their financials, company profiles, product portfolios, and capacity. Along with the important development trends and sales channel research, the report also offers upstream raw material analysis and downstream demand analysis. The global research study also covers the investment opportunity areas.

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Impact Analysis of COVID-19

In this report, market players in this field will receive insights regarding COVID-19’s impact post-COVID-19, which will provide a useful framework for evaluating strategies. The COVID-19 has affected a number of manufacturing operations and supply chains worldwide. As a result, the economy has been experiencing a recession and high inflation rates. Furthermore, the pandemic disrupted a number of industries’ international movements. Due to the labor shortage and the lack of logistics movement, the whole sector faced sustained challenges. Lockdowns have caused severe financial losses to end-user industries.

Currently, there is a growing trend of manufacturing autonomous vehicles in the automotive industry, which is attracting larger investments for the development of these vehicles. An autonomous vehicle includes a combination of various sensors and networking systems that assist the computer in driving the vehicle. The annotated information allows autonomous vehicle computer models 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. The rising investments in the self-driving market are expected to drive the future demand of the data annotation market. For instance, 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.

The rising innovations in the retail segment, specifically in improving the e-commerce sector, using the information labeling tools is expected to drive the market demand over the forecast period. For instance, in February 2019, Walmart Labs acquired Dataturks, an India-based data annotation start-up. Dataturks offers annotation tools for image and text information by using machine learning. The acquisition helped drive innovations in the catalog quality and other aspects of the Walmart merchandising platform.

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Data Annotation Tools Market Report Highlights

  • Rising demand for machine learning in automated data analytics is expected to augment demand for automatic information labeling tools in various data-driven applications. In addition, a rising focus on image annotation is anticipated to enhance the operations of the automotive, retail, and healthcare sector and thus is expected to propel market growth
  • In terms of annotation type, the manual segment captured the largest revenue share in 2020.The attributes such as accuracy, able to capture edge cases, and intelligent human resource is efficient enough to ensure high quality across large volumes of data. Which make manual annotation tools highly suitable to train machine learning algorithms for computer vision applications
  • Information labeling tools find greater acceptance in the automobile industry, especially for self-driving vehicles. High-resolution cameras, LIDAR sensors, and a huge amount of information are needed for creating the training data sets for such highly sensitive visual perception models in autonomous vehicles
  • To gain a competitive edge in the market, key players have started adopting the client base expansion strategy by raising funds for the development of their platform. For instance, in February 2020, Labelbox, Inc. raised a series B funding of around USD 25 million to enhance its platform with data labeling capabilities for AI training models. The funds were used to accelerate the computer vision and machine learning models by augmenting the size of the company’s sales and engineering teams

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