Data Collection and Labeling Market

Data Collection and Labeling Market Outlook (2024 to 2034)

The global data collection and labeling market size is estimated at US$ 2.57 billion in 2024 and is projected to expand at a robust CAGR of 18% to reach US$ 13.45 billion by 2034-end, as per the latest market study by Fact.MR.

Data collection and labeling refer to the process of gathering and categorizing data for various purposes, particularly in the context of machine learning (ML) and artificial intelligence (AI) applications. Data collection involves gathering raw information or observations from various sources. This data can take many forms, such as text, images, videos, sensor readings, and user interactions. The primary goal of data collection is to amass a large and diverse dataset that can be used for training and improving AI and machine learning models.

Data labeling is the subsequent step in which human annotators or specialized software tools add meaningful labels or annotations to the collected data. These labels provide context, categorization, or classification for the data. Data labeling is a crucial aspect of supervised machine learning, as it allows algorithms to learn from the labeled data and make predictions or classifications based on patterns they identify during training.

Data collection and labeling are labor-intensive processes, often requiring human expertise, and high-quality labeled data is essential for training accurate machine learning models. Technological advancements and increasing demand for convenience are directly contributing to the data collection and labeling market growth. The artificial intelligence software that is built into products such as smart speakers is trained with data collection and labeling. Tools for collecting and labeling data are projected to play a critical role in planning and executing a digital transformation in business processes in the coming decade.

• Image/video analysis accounted for 35% of the global data collection and labeling market share in 2023.

Generalization of facial recognition technology and public surveillance by governments across the globe are becoming one of the key factors driving economic growth. Also, the ubiquitous use of facial recognition as a prominent feature in smartphones produces even more demand for image/video data collection and labeling. Audio data collection and labeling, however, is predicted to increase at a rapid pace during the coming decade.

Report Attributes	Details
Data Collection and Labeling Market Size (2024E)	US$ 2.57 Billion
Forecasted Market Value (2034F)	US$ 13.45 Billion
Global Market Growth Rate (2024 to 2034)	18% CAGR
Historical Growth Rate (2018 to 2023)	16% CAGR
Leading Regional Market	North America
Key Companies Profiled	Appen Limited Reality AI Globalme Localization Inc. Global Technology Solutions Alegion Labelbox Inc. Dobility Inc. Scale AI Inc. Trilldata Technologies Pvt. Ltd. Playment Inc.

Data Collection and Labeling Revenue Analysis (2018 to 2023) vs. Market Projections (2024 to 2034)

As per this detailed study by Fact.MR, a market research and competitive intelligence provider, from 2018 to 2022, global demand for data collection and labeling increased at a CAGR of 16%.

Sales of global data collection and labeling solutions are driven by a multitude of factors such as increasing awareness among consumers about digitalization, evolving healthcare treatments, and advanced technologies.

Increasing popularity of drones and robotics is predicted to boost the demand for machine learning in the next 10 years. The market for autonomous vehicles is predicted to surge at a CAGR of 18% from 2024 to 2034.

Why Has Data Collection and Labeling Software Become So Popular?

“Widespread Adoption of Electronic Health Records in Healthcare Industry”

Medical care is extremely complicated in the modern era. Hospitals, insurance companies, pharmaceutical companies, and government entities are all part of its network. Healthcare organizations can boost their competitiveness by using data collection and analysis software.

Several data collection and labeling initiatives are having a significant influence on the healthcare industry. The increasing number of chronic patients and various diseases across the globe is fueling the demand for data collection and labeling technologies. With the growing shift in demand for medical imaging employing computer vision technology to sense patterns and detect injury or disease is predicted to boost the sales of medical image collection and labeling solutions during the forecast period.

Widespread adoption of electronic health records in the healthcare sector as well as the need for verified clinical information for further studies on patients is also driving the demand for data collection and labeling technology. The use of big data by hospitals and other healthcare companies is allowing them to improve organizational decision-making, market more competitively, increase patient satisfaction, and ultimately expand their bottom line.

“Ongoing R&D to Introduce Innovative Data Collection and Labeling Technologies”

Modern applications for well-being rely heavily on real-time physiological data collection and analysis. Use of personalized classifiers and detectors outperforms general classifiers in several contexts. As a result, several challenges arise, ranging from the development of an effective system for collecting signals and labels to creating strategies to interact with the users to create a dataset that represents the various environments in which users interact daily.

Various studies have been conducted on the development of software for collecting consumer data from IoT and social networking sites. Researchers are conducting various studies to determine what information is collected and what information can be excluded from the market. Growing privacy concerns are also driving companies to create data protection programs to safeguard one's data and keep one from data breaches.

Researchers are examining how the availability of data for various industries can be leveraged for higher sales. Several R&D investments by government and non-government organizations are further boosting the growth of the market.

Why is Sales Growth of Data Collection and Labeling Software Being Hampered?

“Stringent Compliance Requirements and Complexity in Handling Diverse Data Types”

Data privacy regulations and the associated concerns pose multifaceted challenges before data collection and labeling providers’ profit growth. Data privacy regulations, such as GDPR in Europe and CCPA in California, impose strict compliance requirements on businesses. This includes obtaining explicit consent, ensuring data encryption, and providing individuals with control over their data. Meeting these requirements can be resource-intensive and may slow down data labeling processes.

Data labeling often involves handling diverse data types, some of which can be highly sensitive. Ensuring compliance with privacy regulations becomes more complex when dealing with medical records, financial information, or personally identifiable data. This complexity can significantly impede labeling operations. The stringent compliance requirements, potential legal risks, and the need to balance privacy with data utility can present significant obstacles for businesses operating in this space.

How are New Companies Mastering the Art of Data Labeling?

“AI Technology Integration Fueling Profitability of Start-ups”

New companies entering the data collection and labeling market can employ various strategies to earn more and establish a competitive edge. Focusing on niche markets or specific industries can help new companies stand out. Specializing in areas such as medical imaging, autonomous vehicles, or financial data labeling can make newcomers an expert in a particular domain and attract clients seeking specialized services.

New entrants can leverage advanced data labeling tools and AI-assisted annotation platforms to increase efficiency and reduce costs. These technologies can help streamline the labeling process, minimize errors, and improve productivity. Collaborations with research institutions or other competitors can also provide access to new markets, clients, and technologies.

• In January 2022, AIMMO, a Korean start-up built an AI data annotation platform, which enables enterprises to read and label image, video, sound, text, and sensor fusion data faster and more accurately. The company has raised US$ 12 million in a Series A round to bolster its data labeling technology and expand globally. Its software eliminates the inefficiencies of the annotating process, freeing customers to focus on their AI models.

Country-wise Analysis

In the United States, Germany, and Japan, sales of data collection and labeling solutions are driven by the growing need for tech innovations in online shopping platforms, automotive, robotics, and industrial automation. The market's expansion reflects the crucial role data labeling plays in enabling the development and deployment of AI and machine learning applications across various sectors.

How are Technological Advancements Driving the United States Market?

“Wide Use of Advanced Data Collection and Labeling Technologies in e-Commerce Industry”

Sales of data collection and labeling products in the United States are increasing at a rapid pace. The United States is at the forefront of technological innovation and AI development. This is fueling the need for high-quality labeled data to train machine learning models.

The developing automotive market and the trend for customers to purchase products through both online and offline platforms are pushing demand for data collection and labeling services upward.

Why are Data Collection and Labeling Technology Providers Lining Up in Germany?

“Use of Precision and High-quality Data Labeling Technologies in Automotive Sector”

Germany is renowned for its precision engineering and automotive industries. The growth of autonomous vehicles and smart manufacturing is leading to a significant demand for accurately labeled data, boosting the sales of data collection and labeling services.

Germany's commitment to quality extends to data labeling. The market is driven by a focus on precision and reliability, which is critical for industries such as automotive, robotics, and industrial automation.

Will Japan Be a Lucrative Market for Providers of Data Collection and Labelling Technologies?

“Role of Data Collection and Labelling in Robotics and Government Support for AI Adoption”

Japan is a leader in robotics and automation. Use of robotics in manufacturing, healthcare, and consumer products necessitates labeled data for machine learning algorithms. The Japanese government is actively supporting AI and machine learning initiatives and encouraging companies to adopt advanced technologies. This is further boosting the demand for data collection and labeling services.

Category-wise Analysis

The automotive industry places a high emphasis on data collection and labeling due to the critical role it plays in the development of autonomous vehicles, safety, testing, regulatory compliance, and technological advancements in the industry. However, during the forecast period, application of data collection and labeling technology is predicted to rise in various end-use sectors driven by ongoing innovation efforts.

Why is Deployment of Data Collection and Labeling Solutions High in the Automotive Industry?

“AI-enabled Data Collection and Labeling Solutions Boosting Autonomous Vehicle Production”

The automotive sector is at the forefront of autonomous vehicle development. To enable the production of self-driving cars and advanced driver-assistance systems (ADAS), large volumes of high-quality labeled data are required. Data labeling is essential for training AI models that recognize and respond to road conditions, pedestrians, and other vehicles.

Accurate data labeling helps ensure the safety and reliability of autonomous vehicles. In the event of accidents or safety-critical incidents, the data collected and labeled can be crucial for liability assessments and investigations. Labeled data enables AI systems to recognize and respond to changing conditions, such as traffic, weather, and obstacles, thus ensuring passenger safety.

Competitive Landscape

Collaborations with technology companies, research institutions, or industry-specific organizations are making it possible for businesses to increase their production and meet consumers’ demand. This move is also generating potential opportunities to earn high profits.

• In May 2022, Sumake North America revealed the EA-SC100 tool management solution, a comprehensive solution for electrical, automotive, and industrial applications. It features a real-time touchscreen interface and remote administration for tool configuration and data collection.
• In November 2021, Scale AI acquired SiaSearch, which allowed it to extend its reach in Europe and develop its product more quickly.
• In July 2021, DataRobot, Inc., a United States-based company, acquired Algorithmia Inc., a Machine Learning Operations (MLOps) software platform. Algorithmia's platform is designed to efficiently generate complex machine learning models at scale, primarily catering to IT operations specialists. DataRobot's aim with this acquisition is to provide its customers with a comprehensive platform for deploying any machine learning model.

Key Segments of Data Collection and Labeling Market Research

• By Data Type :

  • Text
  • Images/Videos
  • Audio

• By Vertical :

  • IT
  • Automotive
  • Government
  • Healthcare
  • BFSI
  • Retail and e-Commerce

• By Region :

  • North America
  • Latin America
  • Europe
  • East Asia
  • South Asia & Oceania
  • Middle East & Africa