Data Collection And Labeling Market Size, Share & Trends Analysis Report By Data Type (Audio, Image/Video, Text), By Vertical (IT, Automotive, Healthcare), By Region, And Segment Forecasts, 2021 - 2028

Report Overview

The global data collection and labeling market size was valued at USD 1,307.7 million in 2020. It is expected to expand at a compound annual growth rate (CAGR) of 25.6% from 2021 to 2028. 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. Also, it helps to offer enhanced safety features in autonomous vehicles, such as emergency vehicle detection, terrain detection, wear detection, and condition monitoring, among others. Machine learning, powered by data gathering, has been embedded in several fields, such as robotics & drones, automated image organization of visual websites, and face identification on social networking websites. One of the most popular data collection applications is social media monitoring, as visual listening and visual analytics are the essential factors of digital marketing. 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.

Several companies are taking strategic initiatives for building strong machine learning models by outsourcing data collection and labeling services. For instance, Globalme Localization Inc., the U.S. based AI data collection company, provided the dialect and accent audio collection to Sonos Inc., the U.S. based audio company. Sonos Inc. integrated the smart home assistants with its wireless speakers by collecting accents and speech data across three countries. This integration helped the company to fine-tune its speech recognition engines to provide a better voice experience.

Data collection and labeling are expected to play a significant role in the healthcare industry as medical imaging uses computer vision technology to sense patterns and detect injury or disease. Data annotation tools help training the AI systems in differentiating information obtained from medical images, including magnetic resonance imaging (MRI), X-ray, and CT scan images. Furthermore, it helps medical practitioners in the automatic generation of reports of examined individuals. For instance, TrainingData.io, the U.S. based tech startup, helps healthcare radiology customers increase the labeling efficiency by ten times and decrease the error rate by more than 15%. The company has developed a web-based platform to help companies manage their data collection workflow.

With the advent of cloud media services and a surge in mobile devices, numerous data processing technologies have emerged, such as multilingual speech transcription, data classification, and data annotation, among others. However, inaccuracy in data annotation remains a challenge for the industry’s growth. For instance, images of low resolution are difficult to label, and errors in labeling lead to the additional cost and effort to the process. Therefore, automated tools are being introduced to reduce the dependency on manual processes. For instance, tagtog Sp. z o.o. provides a versatile text annotation tool that offers automated annotation.

Data Type Insights

The image/ video segment led the data collection and labeling market in 2020, accounting for over 35% share of the global revenue. The high share can be attributed to its increasing implementation of computer vision in several industries, including healthcare, automotive, and media & entertainment industry, among others. For instance, medical imaging is one of the significant image labeling applications. Also, the text segment accounted for the significant share in 2020, owing to its rising applications in clinical research and e-commerce.

With the growing implementation of electronic health record (HER) systems, the accumulation of clinical data, including unstructured text documents, has become one of the valuable resources for clinical research. Statistical NLP (natural language processing) models have been developed to unlock information embedded in clinical text. Also, with the advancement in sentiment analysis, text labeling is highly used in social media monitoring to build recommendation systems. For instance, e-commerce companies use social media data to influence their customers to purchase.

Vertical Insights

The IT segment led the market in 2020, accounting for over 30% share of the global revenue. The high share can be attributed to the wide adoption of AI applications across the industry. Besides, the healthcare industry is expected to grow at a noticeable rate 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, among others, training of datasets with deep learning and machine learning algorithms is required. It directly influences the growth of the industry positively owing to the requirement of highly accurate data labeling for efficient AI-based applications.  

Besides, the retail and e-commerce segment secured significant market shares in 2020. 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 in 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.

Regional Insights

North America dominated the market in 2020, accounting for over 38% share of the global revenue. This can be attributed to the rapid growth of cloud-based media services as media services are one of the potential data sources for collection. The growth of the North America segment is attributed to the growing integration of artificial intelligence and mobile computing platforms in the field of digital shopping and e-commerce. It creates a large amount of data for annotation. Europe is expected to witness significant growth over the forecast period. The growing advancements in automobile obstacle detection technologies are expected to fuel the growth of the market in the automobile sector of the European region over the forecast period.

On the other hand, Asia Pacific is projected to demonstrate growth at the highest CAGR over the forecast period. This growth is attributed to the increasing use of mobiles and tablets, rapid technological advancements, and the popularity of social networking sites in emerging economies, such as China and India. Such a growing number of smart devices boosts the need for data gathering and its annotation. The growing applications of face remembrance in security and surveillance systems in China are projected to drive market growth in the Asia Pacific region. For instance, the Chinese government has enforced real-name registration policies in the country, under which citizens are required to link their online account with the official government ID. These policies have made the use of data collection and labeling more ubiquitous across the nation.

Key Companies & Market Share Insights

Vendors in the market are focusing on increasing the customer base to gain a competitive edge in the industry. Therefore, vendors are taking several strategic initiatives, such as collaborations, acquisitions & mergers, and partnerships with other key players in the market. For instance, in February 2020, Labelbox, a data annotation tool provider, received USD 25 million in additional venture capital funding from a prominent U.S.-based venture capital firm Andreessen Horowitz, Kleiner Perkins, and Google LLC’s AI-focused venture capital fund, Gradient Ventures.  Furthermore, in June 2019, Uber Technologies Inc. completed the acquisition of Mighty AI, Inc., a U.S. based start-up, to provide computer vision models for self-driving cars. Also, in February 2019, Walmart Inc. completed the acquisition of Trilldata Technologies Pvt Ltd, an India-based NLP solution provider, to bring their deep domain expertise in machine learning and extensive application development experience. Some of the prominent players operating 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

Segments Covered in the Report

This report forecasts revenue growth at global, regional, and country levels and provides an analysis of the latest industry trends in each of the sub-segments from 2017 to 2028. 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 - 2028)

  • Text

  • Image/ Video

  • Audio

• Vertical Outlook (Revenue, USD Million, 2017 - 2028)

  • IT

  • Automotive

  • Government

  • Healthcare

  • BFSI

  • Retail & E-commerce

  • Others

• Regional Outlook (Revenue, USD Million, 2017 - 2028)

  • North America

    • U.S.

    • Canada

    • Mexico

  • Europe

    • Germany

    • U.K.

    • France

  • Asia Pacific

    • China

    • Japan

    • India

  • South America

    • Brazil

  • Middle East and Africa (MEA)