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Data Annotation Tools Market by Deployment Model (On-Premises and Cloud), Data Type (Text, Image/Video, and Audio), Annotation Approach (Automated and Manual), and End User (IT & Telecom, Automotive, Government, Healthcare, Retail & E-commerce, Agriculture, and Others) : Global Opportunity Analysis and Industry Forecast, 2020–2027
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| Author(s) : NA | Tables: NA | Charts: NA | Formats*: | |
COVID-19
Pandemic disrupted the entire world and affected many industries.
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Data annotation is a data labeling process used by machine learning model to show the outcome predicted by a user. Data annotation tools are a combination of data attributes, which eliminates the need to rewrite similar rules in multiple places and enable users to manage data definition at a single location. Moreover, data annotation tools are used to enrich data for training and deploying machine learning models. With the rise in technologies and enormous amount of data generated by these technologies such as machine learning and internet of things (IoT), it is essential for organizations to have data efficiency in their systems to create new innovations, which create the demand for adoption of data annotation tools, in turn, propels growth of the data annotation tools market.
Market scope and structure analysis:
        Report Metric |                       Details |
 Market size available for years |  2019–2027 |
 Base year considered |  2019 |
 Forecast period |  2020–2027 |
 Forecast units |  Value ($) |
 Segments covered | Deployment Model, Data Type, Annotation Approach, End User, and Region. |
 Regions covered | North America (U.S. and Canada), Europe (Germany, UK, France, and rest of Europe), Asia-Pacific (China, Japan, India, and rest of Asia-Pacific), Latin America (Brazil, Mexico, and rest of LATAM) and Middle East & Africa |
 Companies covered | Major players include: Alegion, Inc., Appen Limited, Amazon Web Services, Inc., Clickworker GmbH, CloudApp, Inc., CloudFactory Limited, Cogito, Google LLC, Hive, IBM Corporation, iMerit, Labelbox, Inc., LionBridge AI, Neurala, Inc., Playment Inc., Samasource Inc., Scale, Inc., Trilldata Technologies Pvt. Ltd., and Webtunix AI. |
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COVID-19 Scenario Analysis:Â
- Since the COVID-19 virus outbreak in December 2019, the disease has spread to almost all countries around the globe, with the WHO declaring it a public health emergency. The global impacts of the disease are already starting to be felt, and expected to significantly affect the global data annotation tools market in 2020.
- Using better AI model can help companies to reduce operating costs and increased customer satisfaction by providing better services.Â
- Data annotation tools also significantly help the healthcare segment by enhancing applications of AI in healthcare. AI-backed machines can automatically generate medical reports of 00patients by learning from their pattern and data insights.Â
- Overall, Overall, the pandemic is expected to have a positive impact on the data annotation market.
Top impacting factors: Market Scenario Analysis, Trends, Drivers, and Impact Analysis
Rise in demand for annotated data to improve machine learning models is the major factor that drives growth of the market. However, inaccuracies in data annotation tools and high costs associated with manual data annotation hamper the market growth. Furthermore, increase in investments in the development of autonomous driving technologies across the globe is expected to provide lucrative opportunities for the global data annotation tools market.Â
The market trends for global data annotation tools market are as follows:
Notable developments the data annotation tools market:
The market for data annotation tools is growing at a very fast pace, and is also a very competitive market. Thus, many prominent players are adopting different strategies such as expansion of their services, new product launches, collaborations, partnerships, and mergers to increase their reach in the data annotation tools market. For instance, in November 2019, Cogito Tech LLC, launched manual data labeling services for dentistry-based images. Experienced medical professionals were hired to label thousands of dental imaging datasets, which further helped in making better AI model for the healthcare segment.
Annotation tools impact on the automotive sector:
Asia-Pacific is also expected to witness a significant growth, owing to emerging economies such as India, China, and South Korea. Countries in this region are adopting AI for the development of self-driving vehicles, which creates demand for data annotation tools. For instance, in 2019, Toyota invested USD 500 million in Advanced Technologies Group (ATG), which is a division of Uber that focuses on self-driving car program to develop autonomous taxis.Â
Key segments covered:
        Segments |                 Subsegments |
 Deployment Model |
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 Type |
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 Annotation Approach |
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 End User |
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Key benefits of the report:
- This study presents the analytical depiction of the global data annotation tools market along with the current trends and future estimations to determine the imminent investment pockets.
- The report presents information related to key drivers, restraints, and opportunities along with detailed analysis of the market share.
- The current market is quantitatively analyzed from 2020 to 2027 to highlight the global data annotation tools market growth scenario.
- Porter’s five forces analysis illustrates the potency of buyers & suppliers in the data annotation tools market.Â
- The report provides a detailed data annotation tools market analysis based on the present and future competitive intensity of the market.Â
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