Data Annotation Tools Market Demand to Hit $7 Bn by 2027; Global Market Insights, Inc.

Some of the data annotation tools market participants are Appen Limited, Amazon Web Services, Inc., Clickworker, CloudApp, CloudFactory Limited, Cogito Tech, Dataturks, Google LLC, Hive, IBM Corporation, iMerit, Labelbox, LionBridge AI, MonkeyLearn, Neurala, Playment Inc., Samasource, Scale, and Webtunix AI.

Global Market Insights Inc. has recently added a new report on the data annotation tools market which estimates the market valuation for data annotation tools will cross US$ 7 billion by 2027. The steadily growing uptake of data annotation tools by healthcare institutions for medical image labeling and classification of medical literature is likely to drive the industry demand.

The text annotation tools segment will witness a rapid growth rate of over 30% from 2021 to 2027 as textual data labeling becomes increasingly crucial for developing document classification algorithms and performing sentiment analysis. Textual data annotation is also gaining importance in 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.

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The manual data annotation tools segment held a substantial market share in 2020 due to the better quality of labeled data compared to automated data labeling. Manual data labeling is performed by highly skilled professionals with strong expertise in 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.

The agriculture segment is anticipated to register exponential growth over the forecast period due to the increasing proliferation of artificial intelligence in precision agriculture. Data labeling solutions enable enterprises to develop computer vision models, which are used for a variety of agricultural end-uses such as livestock monitoring, crop monitoring, pest & disease control, soil quality assessment, and crop harvesting.

Europe is set to witness a lucrative growth rate of nearly 30% by 2027 due to the extensive adoption of AI technologies by European business corporations. The region has a mature AI industry with several countries including Germany, the Netherlands, and the 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.

Companies operating in the data annotation tools industry are focusing on various business growth strategies including investments in new data labeling solutions, strengthening partner network, and geographical expansion. Through such strategic moves, companies are trying to gain a broader market share and maintain their leadership in the market. For instance, in January 2020, IBM launched a cloud annotation tool to automate image labeling processes with the help of AI applications. The tool helps developers to annotate data without manual inputs and for easy data labeling.

Some major findings of the Data Annotation Tools market report include:

  • 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 the benefits of automated data labeling tools, which will facilitate quick & accurate labeling of large-scale datasets including support for big data and Hadoop platforms.
  • Some of the leading market players are Amazon Web Services, Inc., Clickworker GmbH, CloudApp, Inc., Google LLC, and IBM Corporation.
  • Data annotation tool providers are focusing on strategic collaborations and long-term contracts with clients to gain market share.

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Partial chapters of report table of contents (TOC):

Chapter 3   Data Annotation Tools Industry Insights

3.1    Introduction

3.2    Industry segmentation

3.3    Impact of COVID-19 outbreak

3.3.1    Global outlook

3.3.2    By region    North America    North America    Europe    Asia Pacific    Latin America    Middle East & Africa

3.3.3    Industry value chain    Suppliers    Data annoation technology providers    Marketing and distribution channels

3.3.4    Compeitive landscape    Strategy    Distribution network    Business growth

3.4    Evolution of data annotation tools

3.5    Data annotation tools industry architecture

3.6    Data annotation tools industry ecosystem analysis

3.6.1    Data annotation software vendors

3.6.2    Cloud service providers

3.6.3    Distributors and resellers

3.6.4    Third party service providers

3.6.5    End-users

3.7    Investment portfolio

3.8    Patent analysis

3.9    Technology & innovation landscape

3.9.1    Pseudo labelling

3.9.2    Online content moderation

3.10    Regulatory landscape

3.10.1    North America    NIST Special Publication 800-144 – Guidelines on Security and Privacy in Public Cloud Computing (U    Health Insurance Portability and Accountability Act (HIPAA) of 1996 (U    Personal Information Protection and Electronic Documents Act [(PIPEDA) Canada]

3.10.2    Europe    General Data Protection Regulation (EU)    German Privacy Act (Bundesdatenschutzgesetz- BDSG)

3.10.3    APAC    Information Security Technology- Personal Information Security Specification GB/T 35273-2017 (China)    Secure India National Digital Communications Policy 2018 – Draft (India)

3.10.4    Latin America    National Directorate of Personal Data Protection (Argentina)    The Brazilian General Data Protection Law (LGPD)

3.10.5    MEA    Law No. 13 of 2016 on protecting personal data (Qatar)    Federal Law No. 2 of 2019 on the use of ICT in Healthcare (UAE)

3.11    Industry impact forces

3.11.1    Growth drivers    Rising demand for annotated data to improve machine learning models    Increasing investments in the development of autonomous driving technologies    Growing adoption of data annotation for medical imaging data    Surging uptake of text annotation for document classification

3.11.2    Industry pitfalls & challenges    Inaccurate data labelling due to poor content quality    Lack of skilled professionals    High costs associated with manual data annotation

3.12    Growth potential analysis

3.13    Porter’s analysis

3.14    PESTEL analysis

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Source: Global Market Insights Inc.