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Data Annotation and Labeling Market Trends, Size, Share, Growth, Industry Analysis, Advance Technology and Forecast 2027

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Data Annotation and Labeling Market Trends, Size, Share, Growth, Industry Analysis, Advance Technology and Forecast 2027

March 21
19:46 2024
Data Annotation and Labeling Market Trends, Size, Share, Growth, Industry Analysis, Advance Technology and Forecast 2027
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), and Label Your Data (US).
Data Annotation and Labeling Market by Component, Data Type, Application (Dataset Management, Sentiment Analysis), Annotation Type, Vertical (BFSI, IT and ITES, Healthcare and Life Sciences) and Region – Global Forecast to 2027

The global data annotation and labeling market is projected to register a CAGR of 33.2% during the forecast period, reaching USD 3.6 billion by 2027 from an estimated USD 0.8 billion in 2022. Major drivers for the market include the growing need to enhance machine learning models and need to train AI algorithms to enhance the performance. As Al algorithms are trained rather than programmed, it drives the need for huge amounts of accurately labeled, high-quality datasets to ensure an optimum efficiency. As a result, there is a significant need for data annotation and labelling tools to annotate data streams that can be reliably fed into AI and ML models. The high cost incurred in manual data annotation is one of the underlying restraints of the market for data annotation and labeling. It might take a lot of time and money to manually annotate large datasets for projects that frequently use them in order to ensure  the high data quality.

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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.

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Unique Features in the Data Annotation and Labeling Market

Annotation workflows that are customisable and catered to certain use cases and data types are provided by providers. These workflows can accommodate a wide range of requirements, including sentiment analysis, picture classification, object recognition, and natural language processing (NLP).

Through validation and verification procedures, data annotation platforms use quality assurance techniques including adjudication, consensus labelling, and crowd verification to guarantee the accuracy, consistency, and dependability of annotated data.

Platforms use cloud infrastructure and distributed workforce models to scale resources dynamically based on project requirements and schedules. This allows them to handle enormous quantities of data annotation activities with scalability and elasticity.

An extensive labelling of various data sources is made possible by providers’ support for annotation of multi-modal data types, such as text, audio, video, and sensor data. This is beneficial for applications in computer vision, speech recognition, and sensor fusion.

By concentrating on important data subsets, some platforms use active learning approaches to choose data samples for annotation in an intelligent manner, enhancing model performance iteratively and minimising annotation efforts.

Major Highlights of the Data Annotation and Labeling Market

In order to meet the rising demand for labelled data and annotation services, companies are expanding their presence in emerging markets and geographical areas. This is encouraging innovation, competition, and teamwork within the global data annotation ecosystem.

In order to speed up model training and deployment, providers facilitate the smooth incorporation of annotated data into ML workflows. They do this by supporting well-known ML frameworks and development environments like TensorFlow, PyTorch, and scikit-learn.

To guarantee annotation accuracy, consistency, and dependability, annotation platforms incorporate quality assurance techniques like consensus labelling, human-in-the-loop validation, and machine learning-based verification.

Targeting verticals including autonomous cars, medical imaging, e-commerce, and agriculture, the market provides industry-specific annotation solutions that meet the particular labelling needs and compliance norms of each sector.

In order to safeguard sensitive information during annotation procedures, providers prioritise data privacy and security. They do this by putting in place safeguards such data anonymization, encryption, and compliance with data protection laws.

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Top Companies in the Data Annotation and Labeling Market

Some major players in the data annotation and labeling market include 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).  These players have adopted various organic and inorganic growth strategies, such as new product launches, partnerships and collaborations, and mergers and acquisitions, to expand their presence in the data annotation and labeling market.

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.

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