Data Annotation Tools Market Growth Potential & Forecast, 2032

 

As per a recent research report, Data Annotation Tools Market surpass USD 25 Bn by 2032.

The widespread use of wearable health devices and the explosion of digital medical information has made it difficult for data scientists to identify data manually. Data annotation technologies aid healthcare institutions in addressing these difficulties by delivering accurate, high-quality, labeled medical data with minimal input from medical specialists. This, in turn, has contributed to the high adoption rate of data annotation for medical imaging data, thus, favoring market growth.

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Furthermore, market players are maximizing their efforts to introduce innovative data annotation tools to address the rising consumer demand. Citing an instance, In January 2023, CloudFactory, a prominent provider of human-in-the-loop AI technologies, introduced Accelerated Annotation. This revolutionary Vision AI product combines CloudFactory's skilled workforce with AI-assisted labeling technology to expedite the data labeling process. Technological breakthroughs such as these will help strengthen the industry outlook through 2032.

 

The data annotation tools market is categorized per annotation approach, data, application, and region.

By annotation approach, the automated data annotation tools market is set to exhibit a commendable CAGR from 2023 to 2032. With the growing usage of computer vision across several industry verticals, the demand for precise and quick picture annotation tools has increased. Manual picture annotation is time-consuming and error-prone, which has increased the acceptance of automatic image annotation technologies. As a result, its use in an array of applications, including healthcare, e-commerce, and social media, is expected to stimulate industry revenue streams until 2032.

As per data, the data annotation tools market from the text segment is poised to register substantial revenue growth owing to the rising usage of text annotation for document classification. The segment growth can also be attributed to the growing popularity of text annotation software among organizations dealing with massive amounts of textual content, such as media houses and research universities, which allow users to assign one or more categories to documents.

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The data annotation tools market from the automotive applications segment is poised to witness steady growth between 2023 and 2032. Accurate data annotation for self-driving automobiles has become essential in training a driverless ML model utilizing supervised techniques. Poor data labeling practices in autonomous driving may lead to major delays in the research and manufacturing stages. Driverless vehicles have gained substantial popularity in recent years and have even been mandatory in certain nations for environmental purposes, which may augment segment share.

Asia Pacific data annotation tools market was worth more than USD 450 million in 2022 and is estimated to record substantial growth through 2032, owing to the proliferation of IoT and AI technologies across multiple industries. Furthermore, the expanding automotive sector and the rising adoption of autonomous cars across the region will stimulate regional industry growth through 2032.

Partial chapters of report table of contents (TOC):

Chapter 2   Executive Summary

2.1    Data annotation tools industry 360⁰ synopsis, 2018 - 2032

2.2    Business trends

2.3    Regional trends

2.4    Data type trends

2.5    Annotation approach trends

2.6    Application trends

Chapter 3   Data Annotation Tools Industry Insights

3.1    Impact of COVID-19 outbreak

3.1.1    North America

3.1.2    Europe

3.1.3    Asia Pacific

3.1.4    Latin America

3.1.5    MEA

3.2    Impacts of the Russia-Ukraine war

3.3    Data annotation tools industry ecosystem analysis

3.3.1    Data annotation software vendors

3.3.2    Cloud service providers

3.3.3    Distributors and resellers

3.3.4    Third party service providers

3.3.5    End-user

3.3.6    Vendor matrix

3.4    Technology & Innovation landscape

3.4.1    Pseudo labelling

3.4.2    Online content moderation

3.5    Patent analysis

3.6    Key news & initiatives

3.7    Regulatory landscape

3.8    Industry impact forces

3.8.1    Growth drivers

3.8.1.1   Rising demand for annotated data to improve machine learning models

3.8.1.2   Increasing investments in the development of autonomous driving technologies

3.8.1.3   Growing adoption of data annotation for medical imaging data

3.8.1.4   Surging uptake of text annotation for document classification

3.8.2    Industry pitfalls & challenges

3.8.2.1   Inaccurate data labelling due to poor content quality

3.8.2.2   Lack of skilled professionals

3.8.2.3   High costs associated with manual data annotation

3.9    Growth potential analysis

3.10    Porter’s analysis

3.11    PESTEL analysis

 

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