PUBLISHER: KBV Research | PRODUCT CODE: 1335879
PUBLISHER: KBV Research | PRODUCT CODE: 1335879
The Global Data Labeling Solution and Services Market size is expected to reach $46.9 billion by 2030, rising at a market growth of 19.5% CAGR during the forecast period.
The adoption of data labeling solutions and services technology and medical imaging techniques for the early & precise diagnosis of diseases is expected to lead to more data collection. Therefore, the healthcare segment has acquired $1779.8 million revenue in the market in 2022. Several market participants are launching strategic endeavors to create a strong artificial intelligence network by outsourcing data labeling solutions and services. Solutions based on artificial intelligence can be trained to recognize marked and labeled data. Common information sources include medical images, X-rays, CT scan images, and magnetic resonance imaging. Data solution labels and services are crucial in healthcare since medical imaging uses computer vision technology to recognize patterns and identify illnesses and injuries.
The major strategies followed by the market participants are Product Launches as the key developmental strategy to keep pace with the changing demands of end users. In June, 2022, Google introduced a dedicated server for AI system training along with example-based explanations into its Vertex to expedite the adoption of machine learning models in businesses. Additionally, In February, 2023, Appen Limited launched Automated NLP Labeling, Reinforcement Learning with Human Feedback, and Document Intelligence.
Based on the Analysis presented in the KBV Cardinal matrix; Google LLC (Alphabet Inc.) is the forerunner in the Market. Companies such as Appen Limited, TELUS International (Playment, Inc.) and Alegion, Inc. are some of the key innovators in the Market. For instance, In May, 2021, Cogito expanded its capabilities in Pathology, Ophthalmology & Cardiology. The adoption of AI in healthcare requires expertise for accurately annotated data in healthcare.
Market Growth Factors
Increasing Use of Labeled Data in Healthcare Imaging
The healthcare industry is growing due to adopting Al-enabled systems for better patient care, faster diagnostics, and earlier medication discovery. With the help of adequately labeled medical images, algorithms have been built that can identify patients' disorders and illnesses without a human's aid. Medical staff also work with knowledgeable data labeling solutions and service providers to compile a database of precisely labeled operation videos. The dataset would serve as a fundamental component in developing autonomous surgical robots. Such wide usage of data labelling solutions and services in healthcare is estimated to support the expansion of the market during the projection period.
Increased Digitalization Across Various Industries
This market is growing significantly due to the adoption of digitalization. Data collection has increased significantly across numerous industries as a result of digitalization. An enormous amount of text data is available for analysis because of the growth of digital platforms, social media, and online communication. There is a need for effective data labeling services to make sense of the information and gain useful insights. Machine learning uses enormous volumes of data regularly, and firms invest time and money in giving employees the proper tools and training for data enrichment. It is anticipated that the market for data labeling solutions and services will gain support as the usage of digital platforms becomes widespread.
Market Restraining Factors
Problems Caused by Low-Quality Training Data
The shortage of high-quality input data continues to be one of the main obstacles to the market growth of data labeling solutions and services. Every attempt to use poor-quality data for training Al models leads to inaccuracies in the anticipated results, with certain techniques deteriorating to the point that they are never fully optimized. This is because the accuracy of the data used to feed the algorithms is highly correlated with their performance. Data accuracy is essential for industries with strict regulations, such as healthcare. As a result of the recent pandemic, increasing the data quality for upcoming pandemics is more important than ever. As a result, it is projected that the market for data labeling solutions and services will be restricted due to the these factor.
Based on type, the market is classified into text, image/video, and audio. The text segment garnered a significant revenue share in the market in 2022. Text data refers to any information written down, such as documents, articles, chat logs, social media posts, customer reviews, emails, etc. As enterprises increasingly rely on machine learning and natural language processing technologies for mining vast amounts of textual data for insightful information, the text data labeling segment has grown significantly.
Labeling Type Outlook
By labeling type, the market is fragmented into manual, semi-supervised, and automatic. In 2022, the manual segment registered the highest revenue share in this market. The process of manually classifying or labeling any data involves humans. The method is fascinating compared to automatic labeling because of its advantages, including high integrity, consistency, and minimal data annotation effort. Manual labeling is essential when working with edge instances or niche sectors/industries in public or synthetic datasets is inadequate or insufficient.
Sourcing Type Outlook
On the basis of sourcing type, the market is segmented into in-house, and outsourced. The in-house segment acquired a substantial revenue share in the market in 2022. Businesses may develop trustworthy labeling procedures and a replicable data management system by implementing their in-house data labeling solutions. Per the clients' applications and requirements, the vendors also provide specialized solutions. Additionally, setting up in-house labeling teams offers a better understanding and management of operational processes, which is advantageous from the organization's point of view.
On the basis of vertical, the market is categorized into IT, automotive, government, healthcare, financial services, retails, and others. In 2022, the IT segment witnessed the largest revenue share in the market. The industry's extensive use of AI applications essentially contributes to the growth in this segment. The market is further predicted to grow in the segment with the growing innovations and adoption of cutting-edge technologies within the IT sector globally.
Region wise, the market is analyzed across North America, Europe, Asia Pacific, and LAMEA. In 2022, the North America region generated the highest revenue share in the market. This region's growing investment in data labeling solutions is driving market expansion. Canada and the U.S. were early adopters of AI in the North American region and are on the cutting edge of data labeling solutions and services. Modern research demands have compelled businesses to include strong virtual capabilities, which have expanded the use of these services.
The market research report covers the analysis of key stakeholders of the market. Key companies profiled in the report include Google LLC (Alphabet Inc.), Appen Limited, TELUS International (Playment, Inc.), Yandex N.V., Uber Technologies, Inc. (Mighty AI, Inc.), Zight, Alegion, Inc., Scale AI, Inc., Labelbox, Inc., Cogito Tech LLC
Recent Strategies Deployed in Data Labeling Solution and Services Market
Partnerships, Collaborations & Agreements:
Mar-2022: Labelbox, Inc. signed an agreement with Hitachi Solutions Co., Ltd., a core IT company of the Hitachi Group which provides IT solutions. Through this agreement, both companies get data labeling tools that help the creation of learning data for AI development and begin selling.
May-2021: Labelbox teamed up with Databricks, an enterprise software company. Together, the companies announced the features for teams to develop unstructured data for AI and analytics in Databricks. By integrating Databricks and Labelbox, users get an end-to-end surrounding for unstructured data workflows, a query engine developed around Delta Lake, quick annotation tools, and a strong Machine Learning computing environment.
May-2021: Alegion, Inc. signed an agreement with Yayasan Peneraju Pendidikan Bumiputera, a Malaysian government agency. This agreement was signed to address the quickly emerging technologies of artificial intelligence (AI) and machine learning (ML) by offering training and certification in ML data labeling.
Feb-2021: Google Cloud formed a partnership with NextBillion AI, an industry-leading startup in mapping platforms. The partnership aims to enhance time-to-market for hyperlocal AI solutions by operating datasets & algorithms on Cloud Storage & Cloud SQL to reduce the operational overheads with Google Kubernetes Engine
Product Launches and Product Expansions:
Feb-2023: Appen Limited launched Automated NLP Labeling, Reinforcement Learning with Human Feedback, and Document Intelligence. The launched products would leverage generative AI capabilities and zero shots learning techniques to rate up data annotation. Additionally, the product would unlock generative AI and strengthen exceptional customer experiences.
Jun-2022: Google introduced a dedicated server for AI system training along with example-based explanations into its Vertex. This product expansion aimed to expedite the adoption of machine learning models in businesses. In addition, the company also aimed to democratize AI To allow more people to deploy models in production, continuously monitor, and drive business impact with AI.
Jun-2022: Google rolled out Imagen, a text-to-image AI model. The new product aimed to generate photorealistic images of text and is pre-trained on text data. In addition, the new solution also outperforms DALL-E 2 on the COCO benchmark.
Oct-2021: Scale AI Inc. announced the launch of Scale Rapid, a service that aims to solve the problem by labeling a data sample within one to three hours. With this launch, users would be able to ensure the labeling is being done correctly, recapitulate upon their labeling instruction if important, then ramp up to have Scale AI label all of their datasets.
May-2021: Cogito expanded its capabilities in Pathology, Ophthalmology & Cardiology. The adoption of AI in healthcare requires expertise for accurately annotated data in healthcare.
Feb-2021: Appen Limited launched the latest off-the-shelf (OTS) datasets. These datasets are developed to make it simpler and quicker for companies to get the high-quality training data required to boost their artificial intelligence (AI) and machine learning (ML) projects.
Acquisitions and Mergers:
Aug-2021: Appen Limited agreed to acquire Quadrant, a global leader in mobile location data, Point-of-Interest data, and corresponding compliance services. This acquisition aimed to strengthen Appen's position in the market and also enable the company to provide high-quality data to companies that depend on geolocation for their business.
Jul-2021: TELUS International took over Playment, a complete data labeling platform. Through this acquisition, Playment would enhance TELUS' deep domain expertise and uniquely position it to support customers in developing AI-powered solutions across verticals.
Mar-2021: TELUS International took over Lionbridge AI, a leading and global provider of scalable data annotation services for text, images, videos, and audio. This acquisition aimed to expand TELUS International's global service offerings and penetration into the fast-growing economy services market under its digital transformation strategy.
Market Segments covered in the Report:
By Labeling Type
By Sourcing Type
Unique Offerings from KBV Research
List of Figures