Data Labeling Solution And Services Market Size, Share & Trends Analysis Report By Sourcing Type (In-house And Outsourced), By Type, By Labeling Type, By Vertical, By Region, And Segment Forecasts, 2023 - 2030

Market Size & Trends

The global data labeling solution and services market size was valued at USD 11.83 billion in 2022 and is projected to grow a CAGR of 21.3% from 2023 to 2030. By adding attribute tags, data labeling tools assist users in developing data value. Data labeling is a process for identifying raw data (text, videos images, etc.) and adding one or more significant and revealing labels to offer context. Machine learning has been incorporated in several industries, including facial recognition on social networking websites powered by data collection, automated picture organization of visual websites, and robots & drones. Prominent growth in the automotive business, particularly in self-driving vehicles, significantly fuels data labeling solutions and services. A self-driving vehicle has a multiplicity of sensors and networking devices that let the computer drive the vehicle.

The global data labeling solution and services market is expected to witness a surge in the adoption of the technology owing to assistance such as deriving business insights from socially shared photographs and auto-organizing untagged photo collections. Furthermore, data labeling technology is increasingly being used in autonomous vehicles, which is expected to contribute to significant growth in the automobile industry. With the help of this technology, self-driving cars can detect obstacles and notify the driver about the vicinity of walkways and guardrails. The technology is also capable of reading stoplights and road signs.

The emerging importance of data efficiency and the evolution of technology is allowing for new business innovations, economics, and infrastructure. These factors have contributed to the expansion of the data labeling market. The significant development of machine learning in automated data analytics is projected to increase demand for tools and solutions for autonomous data labeling in many data-driven applications. Furthermore, the emerging prominence of picture annotation is likely to advance retail, automotive, and healthcare operations, driving demand for data labeling technologies. Moreover, the high expenses involved with manually annotating complicated photos may limit the market's growth.

The Asia Pacific region is expected to expand fastest during the projection period. E-commerce and online shopping have a significant reliance on digital platforms for retail, healthcare, and large businesses in developing nations, contributing to the growth of the Asia Pacific market. China is anticipated to augment market expansion in the Asia Pacific data labeling market. The government of China has introduced the enactment of real-name registration laws that require residents to link their online accounts to their official government ID. These policies have increased data collection and labeling across the country.

Generative AI In Data Labeling Solution And Services

Generative AI can be a valuable tool for data labeling, as it can help automate and speed up the labeling process. Several companies have been working to combine data labeling with generative AI and have introduced various innovative platforms like GPT (Generative Pre-Trained Transformer), ChatGPT, InstructGPT, etc., to automate the data labeling task. These platforms are neural network architectures widely used in natural language processing (NLP) tasks such as text generation, translation, and sentiment analysis. Companies are launching new services to fuel the demand for data labeling solutions & services.

For instance, In June 2020, OpenAI introduced Generative Pre-trained Transformer 3 (GPT-3). The GPT3 model has made the natural language processor research community explore GPT3 as a valuable tool for data annotation. GPT3 data annotation is significantly less expensive than human labeling. Clinical professionals perform medical imaging annotations in the healthcare sector, which is costly and time-consuming. However, the capability of GPT3 can be used for deriving essential information from disparate data in electronic health records and detecting patterns in unstructured clinical data, saving time and money.

Generative AI has numerous use cases in data labeling, where it can reduce the amount of manual effort required to annotate large datasets. For example, generative models such as GANs can generate masks or bounding boxes around objects in an image. This reduces the manual effort required to annotate large datasets, making image segmentation tasks more efficient. For instance, in January 2021, OpenAI, a company based in the U.S., launched DALL-E, a generative AI model that can create images from textual descriptions. This model can be used for data labeling by generating images for specific datasets, reducing the amount of manual effort required for labeling.

Sourcing type Insight

Outsourced segment dominated the market and accounted for 84.1% of revenue in 2022. The outsourced segment is also anticipated offer promising growth prospects, expanding at the highest growth rate during the forecast period. For outsourcing companies, cost-effectiveness and short-term commitments are top considerations. Outsourced companies support organizations in accomplishing a flexible method to developing annotative capacity, solid security protocols, and consulting practices for their labeling needs.

In-house segment is expected to witness moderate growth during the forecast period. Execution of in-house data labeling solutions allows businesses to advance reliable labeling processes and a replicable system for managing data. The vendors are also offering custom solutions aligned with the applications and requirements of the customers. Moreover, positioning in-house data labeling teams provides a deeper understanding and improved control of operational procedures, which will benefit the organization viewpoint.

Type Insights

The image segment led the market and accounted for the largest revenue share of over 36.6% in 2022. The high share can be ascribed to the growing use of computer vision in various industries, including automotive, healthcare, media, and entertainment. For instance, medical imaging is one of the significant image-labeling applications.

Moreover, a factor accredited to the growth of the image/video segment is the advanced technology used in the segment. Additionally, the growing use of computer applications in the healthcare industry for X-rays, computed tomography (CT) scans, magnetic resonance imaging (MRI), and patient treatments will propel the segment growth. Also, the text segment accounted for a significant share in 2022, owing to its rising applications in clinical research and e-commerce. Over the projected period, the audio segment is expected to grow at the highest rate.

Labeling Type Insights

In 2022, the manual segment dominated the market, with over 76.9% of the revenue share. The data labeling solution & services is segmented into manual, semi-supervised, and automatic labeling types. Manual data labeling is the process of humans classifying or labeling any data. In contrast to automatic labeling, the method is appealing due to benefits such as high integrity, consistency, and low data annotation efforts. However, because manual annotation is costly and time-consuming, labeled data collected through crowdsourcing activities are used for various purposes.

The automatic labeling segment is expected to rise favorably over the forecast period. Prominently increasing AI in the data labeling sector as it assists the abstraction of sophisticated and high-level perceptions from datasets over a hierarchical learning process is augmenting market growth. Emerging demand for automatic data annotation tools will likely increase as the need for mining and extracting meaningful patterns from large amounts of data grows. Semi-supervised systems can classify unlabeled data or identify specific labeled data. As a result of the restricted use of this annotation type, it will have a moderate market share.

Vertical Insights

In 2022, the IT segment dominated the market, accounting for 32.6% of global revenue. The leading share is attributable to the widespread use of AI applications in the sector. Furthermore, the healthcare business is expected to increase significantly during the projection period. Artificial intelligence is widely employed in the healthcare industry for various applications, including gene sequencing, diagnostic automation, diagnostic automation, treatment prediction, medication discovery, deep learning, and machine learning methods to train datasets. Since highly precise data labeling is essential for efficient AI-based applications, it directly impacts its growth.

Over the projected period, the automotive segment is expected to grow at the fastest CAGR of 22.9%. This can be attributed to the fact that data labeling technology is increasingly being used in autonomous vehicles, which is expected to contribute to the substantial growth in the automotive segment. With the help of this technology, self-driving vehicles can detect barriers and notify the driver about the vicinity of pathways and guardrails. The technology is also capable of reading stoplights and road signs.

Regional Insights

In 2022, North America led the market, accounting for more than 31.0% of total revenue. Emerging investment in data labeling solutions in this region is leading the market growth. Early adopters of AI in the North American market, such as Canada and the U.S., are at the edges of data labeling solutions and services. During the forecast years, the European market is anticipated to increase steadily. In addition, emerging growth in automotive obstacle detection technologies are expected to fuel the market's growth in the European region's automobile sector over the forecast period.

The Asia Pacific regional market is anticipated to gain significant traction in the global market and expand at a CAGR of 22.8% over the forecast period. The growth is attributable to slight technological advancements, the rapidly increasing adoption of mobiles and tablets, and the increasing prominence of social networking in developing economies such as India and China. For instance, Real-name registering laws, which the Chinese government has strictly implemented, require all citizens to connect their official government ID with an internet account. Such policies are augmenting the use of data labeling solutions across the country.

Key Companies & Market Share Insights

The competitive landscape of the market is fragmented and features the presence of several market players. The market has witnessed several mergers, acquisitions, and strategic partnerships and product launches in recent years. For instance, in February 2023, Appen launched automated NLP labeling which leverages generative AI capabilities and few shots learning techniques to speed up data annotation to build generative AI applications. This will enable users to unlock exceptional consumer experience.

Similarly, in September 2022, CloudFactory Limited announced the acquisition of Hasty GmbH, a data-centric machine learning platform that accelerates the transition from model-centric AI to data-centric AI, allowing companies to develop and deploy vision AI solutions faster using a data-centric approach. The acquisition would lead to the integration of Hasty GmbH’s AI-assisted automated labeling with CloudFactory Limited’s human-in-the-loop AI technology would ensure the faster realization of AI models. Some of the prominent players in the data labeling solution and services market are:

• Alegion

• Amazon Mechanical Turk, Inc.

• Appen Limited

• Clickworker GmbH

• CloudApp

• CloudFactory Limited

• Cogito Tech LLC

• Deep Systems, LLC

• edgecase.ai

• Explosion AI GmbH

• Heex Technologies

• Labelbox, Inc

• Lotus Quality Assurance

• Mighty AI, Inc.

• Playment Inc.

• Scale AI

• Shaip

• Steldia Services Ltd.

• Tagtog Sp. z o.o.

• Trilldata Technologies Pvt Ltd

• Yandez LLC

Data Labeling Solution And Services Market Report Scope

Global Data Labeling Solution And Services Market Report Segmentation

This report provides forecasts for revenue growth at the global, regional, and country levels and an analysis of the latest industry trends and opportunities in each of the sub-segments from 2017 to 2030. For this study, Grand View Research has segmented the global data labeling solution and servicesmarket report based on sourcing type, type, annotation type, vertical, and region.

• Sourcing Type Outlook (Revenue, USD Million, 2017 - 2030)

  • In-House

  • Outsourced

• Type Outlook (Revenue, USD Million, 2017 - 2030)

  • Text

  • Image/Video

  • Audio

• Labeling Type Outlook (Revenue, USD Million, 2017 - 2030)

  • Manual

  • Semi-Supervised

  • Automatic

• Vertical Outlook (Revenue, USD Million, 2017 - 2030)

  • IT

  • Automotive

  • Government

  • Healthcare

  • Financial Services

  • Retails

  • Others

• Regional Outlook (Revenue, USD Million, 2017 - 2030)

  • North America

    • U.S.

    • Canada

  • Europe

    • U.K.

    • Germany

    • France

  • Asia Pacific

    • China

    • Japan

    • India

    • South Korea

    • Australia

  • Latin America

    • Brazil

    • Mexico

  • Middle East and Africa (MEA)

    • Kingdom of Saudi Arabia

    • UAE

    • South Africa