Selbyville, Delaware, Jan. 29, 2020 (GLOBE NEWSWIRE) --
According to latest report “Data Annotation Tools Market by Data Type (Image/Video [Bounding Box, Semantic Annotation, Polygon Annotation, Lines and Splines], Text, Audio), Annotation Approach (Manual Annotation, Automated Annotation), Application (Telecom, BFSI, Healthcare, Retail, Automotive, Agriculture), Regional Outlook, Competitive Market Share & Forecast 2026”, by Global Market Insights, Inc., the market valuation of data annotation tools will reach $5 billion by 2026.
The data annotation tools market growth is attributed to steadily growing uptake of data annotation tools by healthcare institutions for medical image labeling and classification of medical literature. The proliferation of wearable health devices and the surging volumes of digital medical information have made it increasingly difficult for data scientists to label data manually. These tools are helping healthcare organizations in addressing these issues by providing accurate & high-quality labeled medical data with minimum assistance from medical experts.
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The text annotation tools segment will witness a rapid growth rate of over 35% CAGR from 2020 to 2026 as textual data labeling becomes increasingly crucial for developing document classification algorithms and performing sentiment analysis. Textual data annotation is also gaining importance for social media text mining as federal agencies and national security authorities monitor the behavior of citizens for potential terrorist threats. Using deep learning neural networks integrated with text annotation solutions, security agencies can monitor potential threat topics and objectional material mentioned in social media handles.
Around 75% data annotation tools market share in 2019 was held by the manual annotation segment due to better quality of labeled data compared to automated data labeling. Manual data labeling is performed by highly skilled professionals with strong expertise on the nature and content of data. Human annotators can detect and handle even the smallest of discrepancies in the input data, which are prone to be missed by automated data labeling tools. In addition, manually labeled data is rapidly gaining traction for developing AI algorithms which are used in critical applications owing to its quality and accuracy.
The agriculture application segment in the data annotation tools market is anticipated to register a profound growth with a CAGR of nearly 30% over the forecast period driven by increasing proliferation of artificial intelligence in precision agriculture. Data labeling solutions enable enterprises to develop computer vision models, which are being used for a variety of agricultural end-uses such as livestock monitoring, crop monitoring, pest & disease control, soil quality assessment, and crop harvesting. In addition, semantic segmentation of images & videos through data labeling tools enables farming robots to perform several real-time actions such as weed control, herbicide spraying, and automated monitoring of large acreage farmlands.
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Europe data annotation tools market is set to witness a lucrative growth of over 30% CAGR from 2020 to 2026 impelled by extensive adoption of AI technologies by European business corporations. The region has a mature AI industry, with several countries such as Germany, the Netherlands, and Nordics being regional leaders in the implementation of machine learning applications.
The European Commission has also prioritized the importance of machine learning in improving the digital economy of the region, with an allocated budget of over USD 775 million for AI-related projects in its Horizon 2020 digital strategy. The increasing demand for annotated training datasets to empower machine learning algorithms across multiple applications such as robotics, cognitive systems, and big data analytics will propel the growth of the market in Europe.
Some major findings of the data annotation tools market report include:
- With the dynamically changing technology landscape in the automotive sector, many automobile manufacturers are focusing on leveraging data annotation tools to accelerate the development of autonomous technologies.
- The growing importance of accurately labeled textual data to develop novel technologies such as natural language generation, text-to-speech, and voice recognition will create a positive outlook for the data annotation tools market.
- Data scientists are reaping benefits of automated data labeling tools, which will facilitate quick & accurate labeling of large-scale datasets including support for big data and Hadoop platforms.
- Manual data annotation tools require the presence of a human data labeler for the annotation of data but are highly accurate compared to automated data labeling tools due to involvement of a human domain expert.
- The rising penetration of machine learning and artificial intelligence technologies in business environments to increase competitive advantage and improve efficiency will continue to drive the adoption of data labeling solutions.
- Some of the leading data annotation tools industry players are 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, Mighty AI, MonkeyLearn Inc., Neurala, Inc., Playment Inc., Samasource Inc., Scale, Inc., Trilldata Technologies Pvt. Ltd., and Webtunix AI, etc.
- Data annotation tools providers are focusing on strategic collaborations and long-term contracts with clients to gain the market share.
Table of Contents (ToC) of the report:
Chapter 3. Data Annotation Tools Market Insights
3.1. Introduction
3.2. Industry segmentation
3.3. Industry landscape, 2015 – 2026
3.4. Evolution of data annotation tools
3.5. Data annotation tools industry architecture
3.6. Industry ecosystem analysis
3.7. Technology & innovation landscape
3.7.1. Pseudo labeling
3.7.2. Online content moderation
3.8. Regulatory landscape
3.8.1. North America
3.8.1.1. NIST Special Publication 800-144 - Guidelines on Security and Privacy in Public Cloud Computing (U.S.)
3.8.1.2. Health Insurance Portability and Accountability Act (HIPAA) of 1996 (U.S.)
3.8.1.3. Personal Information Protection and Electronic Documents Act [(PIPEDA) Canada]
3.8.2. Europe
3.8.2.1. General Data Protection Regulation (EU)
3.8.2.2. German Privacy Act (Bundesdatenschutzgesetz- BDSG)
3.8.3. APAC
3.8.3.1. Information Security Technology- Personal Information Security Specification GB/T 35273-2017 (China)
3.8.3.2. Secure India National Digital Communications Policy 2018 – Draft (India)
3.8.4. Latin America
3.8.4.1. National Directorate of Personal Data Protection (Argentina)
3.8.4.2. The Brazilian General Data Protection Law (LGPD)
3.8.5. MEA
3.8.5.1. Law No. 13 of 2016 on protecting personal data (Qatar)
3.8.5.2. Federal Law No. 2 of 2019 on the use of ICT in Healthcare (UAE)
3.9. Industry impact forces
3.9.1. Growth drivers
3.9.2. Industry pitfalls & challenges
3.10. Growth potential analysis
3.11. Porter’s analysis
3.12. PESTEL analysis
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