Data Collection And Labeling Market Size, Share & Trends Analysis Report By Data Type (Audio, Image/ Video, Text), By Vertical (IT, Automotive, Government, Healthcare, BFSI), By Region, And Segment Forecasts, 2023 - 2030

Market Segmentation

• Data Collection and Labeling Data Type Outlook (Revenue, USD Million, 2017 - 2030)
  • Audio
  • Image/ Video
  • Text
• Data Collection and Labeling Vertical Outlook (Revenue, USD Million, 2017 - 2030)
  • IT
  • Automotive
  • Government
  • Healthcare
  • BFSI
  • Retail & E-Commerce
  • Others
• Data Collection and Labeling Regional Outlook (Revenue, USD Million, 2017 - 2030)
  • North America
    • North America Data Collection and Labeling Market, by Data Type
      • Audio
      • Image/ Video
      • Text
    • North America Data Collection and Labeling Market, by Vertical
      • IT
      • Automotive
      • Government
      • Healthcare
      • BFSI
      • Retail & E-Commerce
      • Others
    • U.S.
      • U.S. Data Collection and Labeling Market, by Data Type
        • Audio
        • Image/ Video
        • Text
      • U.S. Data Collection and Labeling Market, by Vertical
        • IT
        • Automotive
        • Government
        • Healthcare
        • BFSI
        • Retail & E-Commerce
        • Others
    • Canada
      • Canada Data Collection and Labeling Market, by Data Type
        • Audio
        • Image/ Video
        • Text
      • Canada Data Collection and Labeling Market, by Vertical
        • IT
        • Automotive
        • Government
        • Healthcare
        • BFSI
        • Retail & E-Commerce
        • Others
    • Mexico
      • Mexico Data Collection and Labeling Market, by Data Type
        • Audio
        • Image/ Video
        • Text
      • Mexico Data Collection and Labeling Market, by Vertical
        • IT
        • Automotive
        • Government
        • Healthcare
        • BFSI
        • Retail & E-Commerce
        • Others
  • Europe
    • Europe Data Collection and Labeling Market, by Data Type
      • Audio
      • Image/ Video
      • Text
    • Europe Data Collection and Labeling Market, by Vertical
      • IT
      • Automotive
      • Government
      • Healthcare
      • BFSI
      • Retail & E-Commerce
      • Others
    • Germany
      • Germany Data Collection and Labeling Market, by Data Type
        • Audio
        • Image/ Video
        • Text
      • Germany Data Collection and Labeling Market, by Vertical
        • IT
        • Automotive
        • Government
        • Healthcare
        • BFSI
        • Retail & E-Commerce
        • Others
    • U.K.
      • U.K. Data Collection and Labeling Market, by Data Type
        • Audio
        • Image/ Video
        • Text
      • U.K. Data Collection and Labeling Market, by Vertical
    • France
      • France Data Collection and Labeling Market, by Data Type
        • Audio
        • Image/ Video
        • Text
      • France Data Collection and Labeling Market, by Vertical
        • IT
        • Automotive
        • Government
        • Healthcare
        • BFSI
        • Retail & E-Commerce
        • Others
  • Asia Pacific
    • Asia Pacific Data Collection and Labeling Market, by Data Type
      • Audio
      • Image/ Video
      • Text
    • Asia Pacific Data Collection and Labeling Market, by Vertical
      • IT
      • Automotive
      • Government
      • Healthcare
      • BFSI
      • Retail & E-Commerce
      • Others
    • China
      • China Data Collection and Labeling Market, by Data Type
        • Audio
        • Image/ Video
        • Text
      • China Data Collection and Labeling Market, by Vertical
        • IT
        • Automotive
        • Government
        • Healthcare
        • BFSI
        • Retail & E-Commerce
        • Others
    • Japan
      • Japan Data Collection and Labeling Market, by Data Type
        • Audio
        • Image/ Video
        • Text
      • Japan Data Collection and Labeling Market, by Vertical
        • IT
        • Automotive
        • Government
        • Healthcare
        • BFSI
        • Retail & E-Commerce
        • Others
    • India
      • India Data Collection and Labeling Market, by Data Type
        • Audio
        • Image/ Video
        • Text
      • India Data Collection and Labeling Market, by Vertical
        • IT
        • Automotive
        • Government
        • Healthcare
        • BFSI
        • Retail & E-Commerce
        • Others
  • South America
    • South America Data Collection and Labeling Market, by Data Type
      • Audio
      • Image/ Video
      • Text
    • South America Data Collection and Labeling Market, by Vertical
      • IT
      • Automotive
      • Government
      • Healthcare
      • BFSI
      • Retail & E-Commerce
      • Others
    • Brazil
      • Brazil Data Collection and Labeling Market, by Data Type
        • Audio
        • Image/ Video
        • Text
      • Brazil Data Collection and Labeling Market, by Vertical
        • IT
        • Automotive
        • Government
        • Healthcare
        • BFSI
        • Retail & E-Commerce
        • Others
  • MEA
    • MEA Data Collection and Labeling Market, by Data Type
      • Audio
      • Image/ Video
      • Text
    • MEA Data Collection and Labeling Market, by Vertical
      • IT
      • Automotive
      • Government
      • Healthcare
      • BFSI
      • Retail & E-Commerce
      • Others

Data Collection And Labeling Market Dynamics

Driver: Growing need to make text/image more interactive and engaging

The introduction of digital capturing devices, particularly smartphones with cameras, has resulted in an exponential increase in digital content. The use of various applications, websites, social networking sites, and other digital tools captures and shares a great deal of visual and digital information. Several businesses have leveraged this content to deliver smarter and better services to their customers with the use of data annotation. Scale AI, Inc., a technology startup based in the U.S., has offered data labeling services to several autonomous driving companies, including Waymo LLC, Zoox, Lyft, Inc., and Toyota Research Institute, among others. These services have provided significant value to these companies by enabling them to train their autonomous driving models using accurately labeled data.

Social media platforms such as Twitter and Facebook have harnessed image-processing technology to enhance audience engagement by encouraging users to share images and tag their friends. With the growing implementation of Electronic Health Record (EHR) systems, the accumulation of clinical data, including unstructured text documents, has become one of the most valuable resources for clinical research. Statistical Natural Language Processing (NLP) models have been developed to unlock information embedded in clinical text. Additionally, text labeling is highly utilized in social media monitoring due to improvements in sentiment analysis. E-commerce companies use social media data to influence customer's purchase decisions. By utilizing image labeling, consumers shopping online can search for clothing or accessories by simply taking a picture of the desired texture, print, or color using their smartphone. The captured photo is uploaded to an app that uses AI technology to search an inventory of products and find similar items based on the visual characteristics of the uploaded image.

Driver: Rapid penetration of AI and machine learning

The advent of big data is expected to drive the growth of the artificial intelligence market as a large volume of data is required to be recorded, stored, and analyzed for various purposes, such as optimizing business operations, supporting scientific research, gaining customer insights, advancing healthcare, enabling financial services, enhancing smart cities, and strengthening security. Businesses are seeking ways of managing and improving big data computational models, driving the rapid adoption of artificial intelligence solutions. The adoption of artificial intelligence is expected to boost the demand for data collection and labeling significantly. The annotated data can be used to train AI models and machine learning systems in critical areas such as speech recognition and image recognition. Data labeling strengthens AI solutions by directly providing data that is relevant to determining future outcomes and decision-making. Many public and private organizations gather domain-specific information required for training AI models, comprising data from different areas such as national intelligence, fraud detection, marketing, medical informatics, and cybersecurity.

Data collection and labeling enable tagging/annotating such unorganized and unsupervised data by continuously improving the accuracy of each set of data. AI is becoming vital to big data as technology allows the extraction of high-level and complex abstractions using a hierarchical learning process. The requirement for mining and extracting indicative patterns from voluminous data is driving the growth of AI, which is expected to result in the growing demand for data annotation tools. Furthermore, AI technology helps overpower challenges associated with big data analytics, including the validity of data analysis, format variation of raw data, highly distributed input sources, and imbalanced input data. Other challenges include more efficient storage and better information retrieval, as data is collected in large quantities and available across multiple domains. These challenges are overcome by semantic indexing, which facilitates comprehension and knowledge discovery. Furthermore, companies are taking various strategic initiatives in industry mergers to gain competitive advantages. For instance, in November 2021, Scale AI acquired SiaSearch to expand into the European region and speed up new product developments. This acquisition was completed to provide robust data engines

Restraint: Lack of skilled labor

Owing to the growing adoption of cloud media services and mobile devices, numerous data processing technologies have emerged, such as data classification, multilingual speech transcription, and data labeling. However, inaccuracy in data labeling remains a challenge for the industry's growth. For instance, it is difficult to label low-resolution images, and errors in labeling lead to additional costs. Therefore, automated tools are being introduced to reduce the dependency on manual processes. For instance, tagtog Sp. z o.o. provides a versatile data labeling tool that offers automated annotation. Possible inaccuracies associated with manual or automated data labeling tools are also a major market restraint. A given image may have low resolution and can include multiple objects. The primary challenge faced by the data annotation tools market is precise output. Issues such as data inaccuracy and the quality of the output are to be minimized. Manual labeling requires careful and accurate annotation, and the time taken to detect such labels may vary, which further adds to the cost for an enterprise. However, with the development of sophisticated algorithms, the accuracy of automated data labeling is anticipated to improve, reducing the dependency on manual annotation and bringing down the cost of tools. 

What Does This Report Include?

This section will provide insights into the contents included in this data collection and labeling market report and help gain clarity on the structure of the report to assist readers in navigating smoothly.

Data collection and labeling market qualitative analysis

• Industry overview

• Industry trends

• Market drivers and restraints

• Market size

• Growth prospects

• Porter’s analysis

• PESTEL analysis

• Key market opportunities prioritized

• Competitive landscape

  • Company overview

  • Financial performance

  • Product benchmarking

  • Latest strategic developments

Data collection and labeling market quantitative analysis

• Market size, estimates, and forecast from 2017 to 2030

• Market estimates and forecast for product segments up to 2030

• Regional market size and forecast for product segments up to 2030

• Market estimates and forecast for application segments up to 2030

• Regional market size and forecast for application segments up to 2030

• Company financial performance