Global Data Collection and Labeling Market (2021-2028) Size, Share and Trend Analysis Report



DUBLIN, September 15, 2021– (COMMERCIAL THREAD)–The Global Data Collection and Labeling Market Size, Share and Trend Analysis Report by Data Type (Audio, Image / Video, Text), by Vertical (IT, Automotive, Healthcare ), by region and segment forecast, 2021-2028 “ the report was added to offer.

The global data collection and labeling market size is expected to reach USD 8.22 billion by 2028. The market is expected to grow at a CAGR of 25.6% from 2021 to 2028.

Companies mentioned

  • Appen Limited

  • reality ai

  • Location Globalme Inc.

  • Global technology solutions

  • Alegion

  • Labelbox, Inc

  • Dobility, Inc.

  • Ai Scale, Inc.

  • Trilldata Technologies Pvt Ltd

  • Game inc.

Data collection and labeling refers to the process of collecting datasets from online and other sources and labeling them according to their nature, type of data and characteristic. The collection of data and its annotation, combined with artificial intelligence (AI) technology, has created valuable growth opportunities in several verticals, such as games, social media and e-commerce. For example, Twitter and Facebook, two major platforms in the social media world, have benefited from image processing technology in terms of audience engagement as they have created a more connected experience by encouraging users to share. pictures and tag their friends.

The advent of digital capture devices, especially cameras built into smartphones, has led to an exponential growth in the volume of digital content in the form of images and videos. A large amount of visual and digital information is captured and shared through multiple apps, websites, social networks, and other digital channels. Several companies have taken advantage of this available online content to provide smarter and better services to their customers through the use of data annotation. For example, Scale AI, Inc., the US-based tech start-up, has provided valuable data tagging services to its autonomous driving customers, including Waymo LLC; Lyft, Inc .; Zoox; and Toyota Research Institute.

However, data cleansing remains a significant challenge involved in data labeling. Additionally, given the time, complexity, and cost associated with developing machine learning models, many organizations may not have the resources to produce acceptable and accurate results. Therefore, several companies are taking strategic initiatives to expand their activities in the field of data collection based on artificial intelligence. For example, in July 2020, Microsoft acquired Orions Digital Systems, Inc., a US-based data management solutions provider, to strengthen its Dynamics 365 Connected Store capabilities. This acquisition is expected to proliferate the use of computer vision and IoT sensors to help retailers better understand customer behavior and manage their physical spaces.

Highlights of the Data Collection and Labeling Market Report

  • One of the most popular uses of data collection that has improved user experience and attracted customer attraction is the automated image organization offered by cloud-based applications and telecommunications companies. towards this technology.

  • Several advantages, such as better security and automation of identification, are factors favoring the implementation of facial recognition in public spaces or important events.

  • The advent of large-scale cloud-hosted AI and machine learning platforms offered by tech giants has led to the implementation of data annotations with multiple functions such as facial recognition. , object recognition and landmarks detection.

  • Growing integration of digital image processing and mobile computing platforms into various applications such as digital shopping and document verification is propelling market growth

Main topics covered:

Chapter 1 Methodology and Scope

Chapter 2 Executive summary

Chapter 3 Market Variables, Trends and Scope

3.1 Market segmentation and scope

3.2 Data collection and labeling Size and growth prospects

3.3 Data collection and labeling – Value chain analysis

3.4 Data Collection and Labeling Market Dynamics

3.4.1 Market drivers Growing need to make text / image more interactive and engaging Rapid penetration of AI and machine learning Growing R&D expenditure on the development of autonomous vehicles

3.4.2 Market restriction Lack of skilled labor High costs associated with manual labeling of complex images

3.5 Industry Analysis – Porter’s

3.5.1 Supplier power

3.5.2 Power of the buyer

3.5.3 Threat of substitution

3.5.4 Threat of the new participant

3.5.5 Competitive Rivalry

3.6 Mapping of key penetration and opportunities

3.7 Data collection and labeling – Parasite analysis

3.7.1 Policy

3.7.2 Economic

3.7.3 Social networks

3.7.4 Technological

Chapter 4 Data Collection and Labeling Market: Estimates of Data Types and Trend Analysis

Chapter 5 Data Collection and Labeling Market: Vertical Estimates and Trend Analysis

Chapter 6 Data Collection and Labeling Market: Regional Estimates and Trend Analysis

Chapter 7 Competitive Landscape

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Laura Wood, Senior Press Director
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