The global data annotation and labeling market is expected to grow at a compound annual growth rate (CAGR) of 33.2% during the forecast period, rising from an estimated USD 0.8 billion in 2022 to USD 3.6 billion by 2027. Key factors driving this growth include the increasing demand for improving machine learning models and the necessity to train AI algorithms for enhanced performance. Since AI algorithms are trained rather than programmed, there is a growing need for vast amounts of accurately labeled, high-quality data to ensure optimal efficiency.
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By vertical, healthcare and life sciences segment to register highest CAGR during forecast period
The data annotation and labeling market has been segmented based on vertical into BFSI, IT and ITES, Healthcare and Life Sciences, Telecom, Reatil and Consumer Goods, Automotive, Government, Defense and Public Agencies and other verticals. Businesses are using data annotation techniques to reliably and efficiently identify huge volumes of Al training data with minimum human assistance. Healthcare organizations are now able to do faster research in the field of automated patient diagnosis due to the adoption of healthcare training data for the development of sophisticated Al applications. This has driven the demand for high-quality annotated medical datasets to develop high-performance healthcare solutions. In May 2021, Cogito Tech had announced the expansion of its medical annotation capabilities in pathology, ophthalmology & cardiology. During the forecast period the healthcare and life sciences segment is anticipated to grow at a highest CAGR.
By applications, corporate communication is projected to register largest market size during the forecast period
The data annotation and labeling market has been segmented based on applications into corporate communication, training and development, and marketing and client engagement. Corporate communications solutions facilitate communication with employees irrespective of their geographic locations. Executives can build a more personal relationship with the workforce with the use of data annotation and labeling tools. Due to the growing need for businesses to facilitate communications with both internal and external workers, the market for corporate communications via data annotation and labeling is developing. During the forecast period this segment is anticipated to register the largest market size.
By data type, audio is anticipated to grow at a highest CAGR during the forecast period
The data annotation and labeling market has been segmented based on data type into image, text, video, and audio. Among these data type, audio is anticipated to grow at a highest CAGR during the forecase period. It adds appropriate metadata and tags in audio recordings to enable machines to comprehend sounds and speech based on their emotional, sentimental, and semantic contexts for training natural language processing systems. Audio annotation can be used for a variety of purposes, such as organizing audio files, enhancing searchability, and making it simpler to find specific parts of an audio recording. However, the most important use of audio annotations is in the training and development of speech recognition systems, including chatbots, virtual assistants, security systems, and more.
North America anticipated to account for largest market size during forecast period
Among the regions, North America is anticipated to account for the largest market size during the forecast period. The presence of various key market players and the demand for highly customized data sets curated by trained professionals would fuels the growth of data anootation and labeling market across the region. Due to its well-established economies, countries across North America are witnessing significant investments in data annotation and labeling projects.
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Unique Features in the Data Annotation and Labeling Market
The data annotation and labeling market offers a variety of techniques tailored to different data types, including text, image, video, and audio. These techniques range from basic labeling to complex tasks such as semantic segmentation, object recognition, and sentiment analysis.
A defining feature of this market is its focus on producing high-quality, accurately labeled datasets. These datasets are essential for training machine learning and AI models.
Data annotation platforms offer scalability to handle large datasets efficiently. Whether a company needs to annotate small sets of data or large volumes, the market provides flexible solutions that can be scaled according to business requirements.
Many data annotation services incorporate human-in-the-loop systems, where human annotators collaborate with automated tools. This combination of human oversight and AI-driven automation ensures higher accuracy, especially for complex tasks that require nuanced judgment, such as emotion detection or complex object classification.
Companies in the data annotation and labeling market offer customizable tools that allow businesses to define specific labeling criteria according to their project needs. These tools can be adapted for industry-specific requirements, ensuring that the annotations align with the particular demands of sectors such as finance, healthcare, and retail.
Major Highlights of the Data Annotation and Labeling Market
A key highlight of the market is the rising need for high-quality annotated data to train AI and ML algorithms. As AI models become more integral to industries such as healthcare, finance, automotive, and eCommerce, the requirement for massive, accurately labeled datasets has surged, driving the expansion of the annotation market.
The integration of human annotators alongside automated systems is a crucial feature of the market. This HITL approach ensures that complex and nuanced tasks—such as image recognition, sentiment analysis, or medical diagnosis—are handled with greater accuracy, blending human judgment with machine efficiency.
The increasing adoption of automation within the data annotation process is another market highlight. AI-powered annotation tools are being used to streamline repetitive tasks, speed up processes, and reduce costs while maintaining high accuracy.
The data annotation market serves a wide range of industries. In healthcare, it supports medical imaging analysis; in automotive, it aids in training autonomous vehicle algorithms; and in retail and eCommerce, it powers recommendation engines and personalized marketing efforts.
One of the major highlights of the market is the emphasis on high-quality, accurately labeled data. Ensuring that AI models are trained on precise datasets is critical to their success. Market players are increasingly focusing on creating annotation systems that prioritize accuracy to deliver better AI outcomes.
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Top Companies in the Data Annotation and Labeling Market
Some of the major data annotation and labeling market vendors are Google (US), Appen (Australia), IBM (US), Oracle (US), TELUS International (Canada), Adobe (US), AWS (US), Alegion (US), Cogito Tech (US), Anolytics (US), AI Data Innovation (US), Clickworker (Germany), CloudFactory (UK), CapeStart (US), DataPure (US), LXT (Canada), Precise BPO Solution (India), Sigma (US), Segment.ai (US), Defined.ai (US), Dataloop (Israel), Labelbox (US), V7 (UK), LightTag (Germany), SuperAnnotate (US), Scale (US), Datasur (US), Kili Technology (France), Understand.ai (Germany), Keylabs (Israel), and Label Your Data (US).
Appen is one of the world’s leading companies in data for the AI Lifecycle. With more than 25 years of experience in data sourcing, data annotation, and human model evaluation, the company helps businesses introduce cutting-edge AI systems. In addition to the market’s most sophisticated AI-assisted data annotation platform, its expertise includes a worldwide pool of over one million professional contractors who know more than 235 languages in over 70,000 locations across 170 countries. To offer the highest level of quality and efficiency, Appen has developed specialized capabilities that are integrated into products and processes. It offers a wide range of products and services, enabling global leaders in technology, automotive, financial services, retail, healthcare, and governments to launch top-tier AI products confidently. Appen’s AI platform provides the highest-quality training data for ML projects by fusing the human intelligence of over a million individuals around the world with cutting-edge models. The platform can also annotate various types of raw data, including text, video, image, and audio, to produce the precise ground truth required for models.
TELUS International develops, builds, and distributes cutting-edge digital solutions to improve customer experience for disruptive and international businesses. The company’s services help its clients rapidly adopt next-generation digital technology and produce superior business results over their digital transformation journey. The integrated solutions and capabilities of TELUS International include AI Data Annotation, Ground Truth Annotate which enable teams to be more efficient, rapid, and precise in producing quality AI training datasets at scale. They also include digital strategy, innovation, consulting, and design, as well as digital transformation and IT lifecycle solutions. TELUS International partners with companies across high-growth sector verticals, such as tech and games, communications and media, eCommerce and fintech, healthcare, and travel and hospitality, to fuel corporate growth at all stages.
Oracle is a world leader in offering a wide range of products, services, and solutions that are intended to satisfy the needs of business IT environments, including platforms, applications, and infrastructure. It operates through three business segments: cloud and license, hardware, and services. Oracle’s customers include businesses of all sizes, governments, academic organizations, and resellers. The company offers its goods and services both directly and indirectly through a worldwide sales force and the Oracle Partner Network. The business focuses in developing, distributing, and marketing databases, application software, and hardware systems. It offers solutions such as Cloud Infrastructure Data Labeling, Cloud Infrastructure AI services and Machine Learning services to build, train, deploy, and manage custom learning models. It is present in over 175 countries and caters to more than 4,30,000 customers across the globe in various verticals such as banking, telecommunications, engineering and construction, financial services, healthcare, insurance, public sector, retail, and utilities verticals.
LightTag, based in Germany, is a prominent player in the data annotation and labeling market, specializing in text data. Their platform is designed to streamline and enhance the efficiency of text annotation tasks such as entity recognition, classification, and sentiment analysis. LightTag’s tools support collaborative annotation, enabling teams to work together effectively while maintaining consistency and quality. The platform also incorporates robust quality control mechanisms to ensure the accuracy and reliability of annotated data, making it a valuable resource for training AI and machine learning models.
Keylabs, headquartered in Israel, is a significant player in the data annotation and labeling market, offering comprehensive solutions for various types of data including images, videos, and text. Their platform is designed to support high-quality annotation processes essential for training AI and machine learning models. Keylabs focuses on providing scalable and customizable annotation services, ensuring precision and efficiency through advanced tools and a managed workforce. Their services cater to diverse industries such as autonomous vehicles, healthcare, and retail, helping clients enhance their AI applications with accurately labeled data.
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