Automated Data Annotation Tools Market will reach at $ 2322.34 million by 2032 Growing at a CAGR 73.0% - Key Player-Webtunix AI., Scale, Inc., Samasource Inc., Playment Inc., Neurala, Inc., MonkeyLearn Inc., LionBridge AI, Labelbox, Inc., iMerit, IBM Corp

The global data annotation tools market size was valued at USD 789.5 USD million in 2021 and is anticipated to expand at a compound annual growth rate (CAGR) of 24.6% from 2022 to 2032. The growth is majorly driven by the increasing adoption of image data annotation tools in the automotive, retail, and healthcare sectors. The data annotation tools enable users to enhance the value of data by adding attribute tags to it or labeling it. The key benefit of using annotation tools is that the combination of data attributes enables users to manage the data definition at a single location and eliminates the need to rewrite similar rules in multiple places. The rise of big data and the surge in the number of large datasets are likely to necessitate the use of artificial intelligence technologies in the field of data annotations. The data annotation industry is also expected to have benefited from the rising demands for improvements in machine learning as well as in the rising investment in advanced autonomous driving technology.

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Pivotal players studied in the Automated Data Annotation Tools report:

Webtunix AI., Scale, Inc., Samasource Inc., Playment Inc., Neurala, Inc., MonkeyLearn Inc., LionBridge AI, Labelbox, Inc., iMerit, IBM Corporation, Hive, Google LLC, Dataturks, Cogito Tech LLC, CloudFactory Limited, CloudApp, Inc., Clickworker GmbH, Appen Limited, Amazon Web Services, Inc.

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Type Insights
The text segment led the market in 2021, accounting for over 36% share of the global revenue. Based on type, the market is segmented into text, image/video, and audio. The image/video annotation segment is expected to dominate the market over the forecast period. Some of the major applications of image data annotation are in the medical industry in the field of medical imaging. For example, the total investment in startups developing machine learning solutions using medical images reached $522 million by the first half of 2018. Startups such as Infervision, Zebra Medical Vision, and Arteries are some of the prominent startups within the healthcare sector in the data annotation market.
COVID-19 Impact Analysis:

In this report, the pre- and post-COVID impact on the market growth and development is well depicted for better understanding of the Automated Data Annotation Tools based on the financial and industrial analysis. The COVID epidemic has affected a number of Automated Data Annotation Tools is no challenge. However, the dominating players of the Global Automated Data Annotation Tools are adamant to adopt new strategies and look for new funding resources to overcome the rising obstacles in the market growth.

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Product types uploaded in the Automated Data Annotation Tools are:

Image/video, Text, Audio

Key applications of this report are:

IT & Telecom, BFSI, Healthcare, Retail, Automotive, Agriculture, Others

Vertical Insights
The IT segment led the market in 2021, accounting for a 33% share of the global revenue. Based on verticals, the market has been segmented into IT, automotive, government, healthcare, financial services, retail, and others. The healthcare segment is expected to grow at a good pace over the forecast period. AI is widely adopted in the healthcare sector for various applications such as treatment prediction, diagnostic automation, drug development, and gene sequencing. The data sets in healthcare are required to be trained with machine learning algorithms. The quality of the training significantly impacts the efficacy and accuracy of the algorithm used for developing AI-based applications. Access to accurate and high-quality data sets is the key step in developing a successful AI-enabled product in the healthcare sector. Thus, data annotation tools drive the development of the sector by providing training data sets to the AI.

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Report SummaryTable of ContentsSegmentationMethodologyRequest a FREE Sample Copy
Report Overview
The global data annotation tools market size was valued at USD 629.5 USD million in 2021 and is anticipated to expand at a compound annual growth rate (CAGR) of 26.6% from 2022 to 2030. The growth is majorly driven by the increasing adoption of image data annotation tools in the automotive, retail, and healthcare sectors. The data annotation tools enable users to enhance the value of data by adding attribute tags to it or labeling it. The key benefit of using annotation tools is that the combination of data attributes enables users to manage the data definition at a single location and eliminates the need to rewrite similar rules in multiple places. The rise of big data and the surge in the number of large datasets are likely to necessitate the use of artificial intelligence technologies in the field of data annotations. The data annotation industry is also expected to have benefited from the rising demands for improvements in machine learning as well as in the rising investment in advanced autonomous driving technology.

Asia Pacific data annotation tools market size, by type, 2020 - 2030 (USD Million)
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Data annotation is expected to play a major role in enhancing the applications of AI in the healthcare sector. AI-backed machines use machine vision or computer vision in medical imaging data technologies to sense patterns and identify possible injuries, which assists medical practitioners in automatically generating reports after the individual is examined. The database of CT scans, MRI, and X-Ray images can be easily screened by the AI to determine various injuries. Data annotation tools help train AI systems in differentiating data obtained from normal and injured medical images to generate the final reports of the examined individuals. Thus, data annotation is expected to play a major role in enhancing the applications of AI in the healthcare sector. For instance, in March 2021, Innodata Inc., a U.S.-based company, announced its expansion of AI data annotation tools capabilities to include the medical reports of the patients. Innodata has established plans to synthesize its capabilities from the AI Data annotation tools platform and Synodex medical data extraction platform, to create a medical record data annotation platform. Via this, high-quality AI training data will be created that is likely to carry HIPAA compliance and follow all the security protocols.

Technologies such as the Internet of Things (IoT), Machine Learning (ML), robotics, advanced predictive analytics, and Artificial Intelligence (AI) generate massive data. With changing technologies, data efficiency proves to be essential for creating new business innovations, infrastructure, and new economics. These factors have significantly contributed to the growth of the industry. Owing to the rising scope of growth in data labeling, companies developing AI-enabled healthcare applications are collaborating with data annotation companies to provide the required data sets that can assist them in enhancing their machine learning and deep learning capabilities. For instance, in November 2020, Telus International, a provider of digital customer experience (CX), and digital IT solutions & services announced to acquire Lionbridge AI, which offers training data and annotation platform solutions used for designing AI algorithms to power machine learning. The acquisition is expected to enhance Telus International's next-generation digital solution portfolio and expand its reach worldwide.

However, the inaccuracy of data annotation tools acts as a restraint to the growth of the market. For instance, a given image may have low resolution and include multiple objects, making it difficult to label. The primary challenge faced by the market is issues related to inaccuracy in the quality of data labeled. In some cases, the data labeled manually may contain erroneous labeling and the time to detect such erroneous labels may vary, which further adds to the cost of the entire annotation process. However, with the development of sophisticated algorithms, the accuracy of automated data annotation tools is improving and thus reducing the dependency on manual annotation and the cost of the tools in the near future.

Type Insights
The text segment led the market in 2021, accounting for over 36% share of the global revenue. Based on type, the market is segmented into text, image/video, and audio. The image/video annotation segment is expected to dominate the market over the forecast period. Some of the major applications of image data annotation are in the medical industry in the field of medical imaging. For example, the total investment in startups developing machine learning solutions using medical images reached $522 million by the first half of 2018. Startups such as Infervision, Zebra Medical Vision, and Arteries are some of the prominent startups within the healthcare sector in the data annotation market.

The text annotation segment is expected to expand at a promising pace over the forecast period, owing to the rising applications in e-commerce and clinical research applications. Text annotation will dominate the global market owing to the need to fine-tune the capacity of AI so that it can help recognize patterns in the text, voices, and semantic connections of the annotated data. The audio segment is expected to cater moderate share in the market. For instance, in April 2021, Zoom, a video telephony software, announced the launch of numerous updates to its platforms such as enhanced screen annotation, advanced hardware solutions for zoom rooms, expanded management abilities for zoom chat, and advancement in user experience based on customer feedback. With these updated features, users can highlight text or objects without the need to erase highlighted annotations. Users can make use of a new pen feature named the vanishing pen feature to highlight text or objects.

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Annotation Type Insights
The manual segment led the market in 2021, accounting for over 81% share of the global revenue. Based on annotation type, the market is categorized into manual, semi-supervised, and automatic. Manual data annotation is a process of labeling or annotating any data by humans. The approach is popular due to its benefits such as accuracy, high level of integrity, minimal data annotation efforts, and a higher chance of discovering intriguing insights pertaining to the data compared to automatic annotation, which can be later integrated into an algorithm. However, as manual annotation can be expensive and time-consuming, labeled data gathered through crowdsourcing activities is used for various applications.

The automatic annotation segment is expected to grow at a promising pace over the forecast period. AI is becoming vital to the data annotation industry as the technology allows the extraction of high-level and complex abstractions from the datasets using a hierarchical learning process. The need for mining and extracting meaningful patterns from voluminous data is driving the growth of AI, which is expected to further drive the demand for automatic data annotation tools. The semi-supervised systems can be used to identify specific labeled data or can be used to classify the unlabeled data semi-supervised. Thus, limited use of this annotation type will contribute a moderate share in the market.

Vertical Insights
The IT segment led the market in 2021, accounting for a 33% share of the global revenue. Based on verticals, the market has been segmented into IT, automotive, government, healthcare, financial services, retail, and others. The healthcare segment is expected to grow at a good pace over the forecast period. AI is widely adopted in the healthcare sector for various applications such as treatment prediction, diagnostic automation, drug development, and gene sequencing. The data sets in healthcare are required to be trained with machine learning algorithms. The quality of the training significantly impacts the efficacy and accuracy of the algorithm used for developing AI-based applications. Access to accurate and high-quality data sets is the key step in developing a successful AI-enabled product in the healthcare sector. Thus, data annotation tools drive the development of the sector by providing training data sets to the AI.

Global data annotation tools market share, by vertical, 2021 (%)
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The automotive segment is anticipated to grow at the highest rate over the forecast period as data annotation tools find wide acceptance in self-driving vehicles. The growing R&D spending towards improving image annotation for pushing developments in the field of self-driving vehicles is boosting the market growth. For instance, in January 2021, TCS announced the launch of an autoscape solution set for autonomous and connected vehicle ecosystem players. It is composed of automotive OEMs, suppliers, start-ups, and fleet owners. The solution addresses technology & business challenges and provides services such as petabyte data collection & analysis, validation, and deployment of algorithms, that offer proper guidance and control of autonomous vehicles in the real world. It also provides a data annotation studio and autonomous vehicle (AV) validation services. The data annotation studio is a data categorization solution that enhances enterprise workflow by offering cost-effective data organization and model management.

Regional Insights
North America dominated the market in 2021, accounting for over 37% share of the global revenue. This is due to the rapid product and geographical expansion strategy undertaken by market vendors in order to gain an edge in the market. The European market is expected to witness a steady growth pattern over the forecast period. Furthermore, the rising focus on image annotation is anticipated to enhance the operations of retail and automotive verticals in the European region.

The Asia Pacific market is anticipated to register the highest CAGR over the forecast period. Emerging economies in the Asia Pacific region hold significant potential for the widespread adoption of data annotation tools, particularly in the healthcare and financial services verticals. The growth of the healthcare industry in the Asia Pacific region is marked by the increasing adoption of technology and innovative healthcare access programs. These factors are anticipated to boost the demand for image data annotation tools in this region in the near future. For instance, in April 2021, Congenica Ltd, a provider of data analytics tools for annotating and clinically interpreting genomic sequence data, announced a partnership with Camtech Diagnostics, a U.K.-based technology company with a specialization in microfluidics. This initiative is expected to expand Congenica's presence in countries such as Singapore, Malaysia, Japan, and South Korea.

Key Companies & Market Share Insights
Vendors in the market are taking several strategic initiatives, such as collaborations, acquisitions & mergers, and partnerships with other key players in the market. Moreover, these players are focusing on raising funds to support geographical expansion and product launches. For instance, in November 2018, CloudFactory Limited- a cloud-based platform that offers machine learning, data enrichment services, and data transcription solutions raised funding worth USD 65 million in its growth equity round, thus equating its total raised the amount to USD 78 million. Some of the prominent players in the global data annotation tools market include:

Annotate.com

Appen Limited

CloudApp

Cogito Tech LLC

Deep Systems

Labelbox, Inc

LightTag

Lotus Quality Assurance

Playment Inc

Tagtog Sp. z o.o

CloudFactory Limited

ClickWorker GmbH

Alegion

Figure Eight Inc.

Amazon Mechanical Turk, Inc

Explosion AI GMbH

Mighty AI, Inc.

Trilldata Technologies Pvt Ltd

Scale AI, Inc.

Google LLC

Lionbridge Technologies, Inc

SuperAnnotate LLC

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