Data Collection And Labeling Market Size, Share & Trends Analysis Report By Data Type (Audio, Image/Video, Text), By Vertical (IT, Retail & E-commerce), By Region, And Segment Forecasts, 2022 - 2030

Report Overview

The global data collection and labeling market size was valued at USD 1.67 billion in 2021 and is expected to expand at a compound annual growth rate (CAGR) of 25.1% from 2022 to 2030. The market is expected to witness a surge in the adoption of the technology owing to benefits such as extracting business insights from socially shared pictures and auto-organizing untagged photo collections. It also contributes to developing enhanced safety features in autonomous vehicles, such as condition monitoring, terrain detection, wear detection, and emergency vehicle detection. Machine learning has been incorporated in various industries, including facial recognition on social networking websites, automated picture arrangement on visual websites, and robotics and drones.

One of the most popular data collection applications is social media monitoring as visual listening and visual analytics are essential digital marketing factors. Also, this technology is highly used in applications related to safety and security, such as data gathering for facial recognition used by law enforcement agencies. The need for a constant flow of data to evaluate is expanding as the importance of data-backed decisions for businesses increases. Analysts derive insights and information from data about their target clientele through data mining.

Artificial intelligence-enabled data labeling service is rapidly gaining traction in security monitoring technology in many countries. Person/object tracking, traffic monitoring, parking occupancy detection area monitoring, and vehicle analysis are some of the primary AI applications in surveillance settings. Many companies have invested a lot of time developing AI-based data processing technologies to maintain social isolation in open spaces, especially during the global covid-19 epidemic.

The introduction of automatic data processing technologies such as computers and other communication devices that process massive amounts of information rapidly and efficiently with minimal human interaction and disseminate it to a select audience is driving the market forward. Several companies are taking up strategic initiatives to build strong machine learning models by outsourcing data collection and labeling services.

For instance, in January 2022, AIMMO, a data labeling service provider, created an AI data annotation platform to help organizations quickly label data. The company raised USD 12 million in Series A financing to enhance its data tagging technology and accelerate worldwide expansion. The platform model helps improve the data annotation process's inefficiency, allowing users to emphasize their AI models solely.

Primary data collection methods, such as interviewing, surveying, or conducting experiments, will propel the data collection and labeling industry forward. Data collecting and labeling are likely to play an essential part in the healthcare sector as medical imaging uses computer vision technology to recognize patterns and detect injury or disease. Various data collection methods and annotation tools aid in teaching AI systems to distinguish information from medical pictures such as CT scan images, X-rays, and MRI (magnetic resonance imaging).

Furthermore, it aids medical practitioners in the automatic data processing of reports on persons who have been evaluated. For instance, in April 2022, Encord, a startup, introduced its beta version of CordVision, an AI-assisted labeling application that intends to provide labeled data sets for machine vision projects. The business has created a suite of tools that enables radiologists to zoom in on Digital Imaging and Communications in Medicine (DICOM) images, a standard format for transmitting medical images. Instead of having a radiologist label a whole picture, the program is meant to label only critical sections of the image.

Furthermore, data mining solutions that enable organizations to extract valuable data from a massive quantity of raw data and analyze latent data patterns to organize these patterns into useable information are propelling market expansion. With the rise of cloud media services and the proliferation of mobile devices, new data processing technologies, such as data classification, multilingual speech transcription, and data annotation, have evolved. However, inaccuracy in data annotation continues to be a barrier to the industry's progress. For example, low-resolution photos are difficult to label, and labeling errors add cost and work to the process. As a result, automated technologies are being deployed to lessen reliance on manual operations. Tagtog Sp. z o.o., for example, offers a flexible text annotation tool with automatic annotation.

Data Type Insights

The image/video segment led the market in 2021 with a revenue share of over 35.0%. The large percentage can be due to the rising use of computer vision in various industries, including automotive, healthcare, media, and entertainment. For instance, in May 2022, Researchers at the Massachusetts Institute of Technology (MIT), a private land-grant research university, created a machine learning model that learns to describe data in a manner that incorporates concepts shared by video and aural modalities. Their model can identify and mark where particular actions occur in a video. The developers limit the technique to only 1,000 words to label vectors, and the model can choose which concepts or activities to put into a single vector.

The text segment accounted for a significant share in 2021 owing to its rising applications in clinical research and e-commerce. For instance, Taskmonk Technology Pvt Ltd., an e-commerce data labeling platform, offers a centralized procurement of labeled data to create better and faster AI retail. Further, it would help e-commerce enterprises get reliable data and save time with the help of AI data labeling. It would benefit enterprises by maximizing their labeling budget, boosting data accuracy, orchestrating labeling projects for any data type, and speeding up data labeling. With the growing implementation of EHR (Electronic Health Record) systems, the accumulation of clinical datasets, including unstructured text documents, has become a valuable resource for clinical research. Statistical NLP (natural language processing) models have been developed to unlock information embedded in clinical text.

For instance, in September 2021, Centaur Labs, a scalable and accurate medical data labeling service provider, announced USD 15 million in series A funding. The funds will be used to further the company's aim of labeling the world's clinical data. Centaur Labs' work and emphasis on healthcare data quality align with AI pioneer Andrew Ng's current drive to transform AI development from model-centric to data-centric. Also, with the advancement in sentiment analysis, text labeling is highly used in social media monitoring to build recommendation systems.

Vertical Insights

The IT segment led the market in 2021, accounting for over 30.0% share of the global revenue. The large share can be attributed to the wide adoption of AI applications. Besides, the healthcare industry is expected to grow over the forecast period. Since artificial intelligence is being used widely in the healthcare industry for several applications, such as diagnostic automation, treatment prediction, gene sequencing, and drug development, training data set with deep learning and machine learning algorithms is required. It directly influences the industry growth positively due to the requirement of highly accurate data labeling for efficient AI-based applications.

For instance, in May 2021, ByteBridge, a human-powered and machine-learning-powered data collecting and labeling SAAS platform, took a significant step ahead with the release of its automated data gathering and labeling platform. It provides researchers with high-quality labeled datasets relating to health care and public health, giving the machine learning industry high-quality training data.

The retail and e-commerce segment accounted for a significant market share in 2021. With the help of image labeling, online shoppers can search for clothing or accessories by taking a picture of the texture, print, or color of their choice. The photo captured by the smartphone is uploaded to an app that searches an inventory of products to find similar products using AI technology. Also, data annotation technology is being increasingly adopted in autonomous vehicles, which is anticipated to contribute to the noticeable growth of the automotive segment.

Self-driving cars can detect obstacles and warn the driver about the proximity to walkways and guardrails with the help of this technology. The technology is also capable of reading stoplights and road signs. For instance, in February 2022, Annotell, a company providing high-quality training data for supervised machine learning, raised USD 24 million to create data labeling tools for self-driving systems. The firm claims to provide a solution in the form of a platform that ostensibly allows for the safe perception of self-driving automobiles by integrating software with the knowledge to reduce the production timeline of driverless cars.

Regional Insights

North America dominated the market in 2021, accounting for more than 35.0% share of global revenue. This is due to the rise of cloud-based media services, one of the potential data sources for collecting. The expanding integration of mobile computing platforms and artificial intelligence in digital shopping and e-commerce is contributing to the regional growth. It generates a lot of data for annotation.

For instance, in May 2022, Sumake North America, the most dependable and complete source for automotive, electrical, and industrial applications, is introducing the EA-SC100 tool management system, its newest product. The system includes a touchscreen interface for real-time result visualization and a remote administration system for data collection and tool setup. The European regional market is predicted to grow significantly during the forecast period. Constant improvements in car obstacle detection technologies is likely to boost the growth of the European automobile industry throughout the forecast period.

Asia Pacific is expected to expand at the fastest CAGR during the projected period. This expansion can be ascribed to the increased usage of mobile phones and tablets, data processing technologies, and the popularity of social networking sites in emerging economies such as China and India. The expanding number of smart devices increases data collection and annotation demand. Face recognition applications in security and surveillance systems in China are expected to fuel market expansion in the Asia Pacific region.

For example, the Chinese government has implemented real-name registration laws in the country, requiring residents to link their internet accounts to their official government ID. For instance, in April 2022, a Reuters investigation of government records revealed that dozens of Chinese enterprises had developed software called "one person, one file." The software utilizes artificial intelligence to classify data set collected on citizens amid significant demand from authorities looking to expand their surveillance tools. The system improves on existing software, which takes data and then leaves it up to people to manage.

Key Companies & Market Share Insights

Vendors in the market are focusing on expanding their customer base to gain a competitive edge in the industry. Therefore, vendors are taking up several strategic initiatives, such as collaborations, acquisitions & mergers, and partnerships with other key players in the market. For instance, in December 2021, Sight Machine, a provider of a digital manufacturing platform aimed to address fundamental concerns in quality and productivity throughout the enterprise, announced a partnership with NVIDIA Corp. to accelerate manufacturing data labeling. Sight Machine intends to overcome the data labeling barrier by connecting its streaming data pipeline to the NVIDIA AI platform, which runs on Microsoft Azure infrastructure to locate data to assets on a global scale. Some prominent players in the global data collection and labeling market include:

• Reality AI

• Globalme Localization Inc.

• Global Technology Solutions

• Alegion

• Labelbox, Inc.

• Dobility, Inc.

• Scale AI, Inc.

• Trilldata Technologies Pvt. Ltd.

• Appen Limited

• Playment Inc.

Data Collection And Labeling Market Report Scope

Global Data Collection And Labeling Market Segmentation

This report forecasts revenue growth at the global, regional, and country levels and provides 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 collection and labeling market report based on data type, vertical, and region:

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

  • Text

  • Image/ Video

  • Audio

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

  • IT

  • Automotive

  • Government

  • Healthcare

  • BFSI

  • Retail & E-commerce

  • Others

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

  • North America

    • U.S.

    • Canada

    • Mexico

  • Europe

    • Germany

    • U.K.

    • France

  • Asia Pacific

    • China

    • Japan

    • India

  • South America

    • Brazil

  • Middle East and Africa (MEA)