The global data collection and labeling market size was valued at USD 1.2 billion in 2023 and is projected to reach from USD 1.5 billion in 2024 to USD 8.3 billion by 2032, growing at a CAGR of 23.7% during the forecast period (2024-2032).
Data collection and labeling refer to systematically gathering and annotating raw data to improve its significance and usability for machine learning applications. This process involves curating various datasets, such as images, text, and sensor data, and adding annotations or labels to provide context and significance. The utilization of these annotated datasets is crucial in the process of training machine learning models, thereby enhancing their precision and efficiency. Data collection and labeling are essential in multiple sectors, such as autonomous vehicles, healthcare, and e-commerce. It enables the progress and enhancement of artificial intelligence technologies by providing top-notch, annotated datasets.
The data collection and labeling market share is expected to grow due to benefits such as extracting business insights from socially shared images and automatically organizing untagged photo collections. It also helps to develop advanced safety features in self-driving vehicles, such as condition monitoring, terrain detection, wear detection, and emergency vehicle detection.
AI applications are increasingly used in healthcare to improve diagnostics, treatment planning, and patient care. A crucial element involves the analysis of medical images, wherein artificial intelligence algorithms decipher intricate medical images, including X-rays, MRIs, and CT scans. According to a recent report from Morgan Stanley, the projected allocation for artificial intelligence (AI) and machine learning (ML) in health company budgets is expected to increase to 10.5% next year, compared to 5.5% in 2022. According to the investment bank, most healthcare companies, precisely 94%, utilize artificial intelligence (AI) and machine learning (ML) in various operations.
Additionally, the healthcare industry increasingly utilizes machine learning techniques to create a well-organized dataset with specific cases. This helps in developing and safeguarding organizations' stored data. It also enables healthcare operators to manage robust machine learning data effectively, which can be utilized to streamline the workflow during periods of high workload, staff shortages, and patient influx. This highlights the growing necessity for extensive automation implementation in healthcare facilities.
Therefore, using artificial intelligence (AI) in healthcare, specifically in analyzing medical images, highlights the importance of precisely annotated datasets. The market trend significantly develops datasets and promotes progress in healthcare diagnostics and treatment planning through artificial intelligence (AI) applications. The expansion of the healthcare AI market highlights the continuous need for labeled healthcare data in the data collection and labeling sector.
Data collection and labeling pose challenges when dealing with sensitive data, especially in industries where privacy is paramount. Strict measures are necessary to safeguard individuals' personal information to comply with regulations like the General Data Protection Regulation (GDPR) in Europe and similar privacy laws worldwide. The Digital Personal Data Protection (DPDP) Act of 2023, India's latest legislation on data protection, stipulates that personal data may only be processed with the explicit consent of the individual concerned. The legislation also specifies that personal data can be processed for "lawful purposes" without permission.
In addition, the International Association of Privacy Professionals (IAPP) conducted a study in 2023, revealing that European organizations' average privacy budget is Euro 1.1 million. The research additionally revealed that EU privacy professionals receive an annual base salary of Euro 98,893, and the number of privacy technology vendors has grown almost eightfold since 2017. Furthermore, the expenses associated with GDPR compliance can vary between USD 20,500 and USD 1,02,500, depending on the scale and intricacy of the organization.
Failure to comply with data privacy regulations can result in significant legal ramifications. Meta, the owner of Facebook, was fined a record-breaking USD 1.2 billion by Ireland's Data Protection Commission in May 2023. The substantial fine is associated with transferring European Facebook user data to the United States without adequate safeguards against the intelligence agencies of Washington.
Labeled datasets are crucial for advancing autonomous vehicles, drones, and other robotic systems as they provide the necessary information for navigation, object recognition, and decision-making. Data collection and labeling services can significantly contribute to the advancement of autonomous technologies by supplying datasets that improve object recognition, navigation, and decision-making abilities. Waymo, Tesla, and Cruise are actively developing autonomous vehicle technologies that heavily depend on precisely labeled datasets. These datasets are crucial for training their AI systems to navigate roads effectively, interpret traffic signs, and identify obstacles. Gartner predicts that the global market will see an increase in vehicles with autonomous driving hardware, with 745,705 units expected to be added by 2023. This is a significant rise from the 137,129 units recorded in 2018. Statista predicts that the sales of autonomous vehicles will increase from 1.4 million in 2019 to 58 million in 2030.
Moreover, companies engaged in aerial surveying, agriculture, infrastructure inspection, and delivery services use drones and uncrewed aerial vehicles (UAVs) with artificial intelligence (AI) algorithms to enable autonomous flight and data collection. For training drone AI systems to identify and navigate different landscapes and detect specific objects, it is crucial to have datasets that include aerial images, terrain maps, and annotations for object detection. McKinsey & Company reports that the Asia-Pacific region accounted for 43% of worldwide drone deliveries in the first half of 2023. North America's share accounted for only 15 percent, yet this signifies a 50 percent growth compared to its share in 2022. Africa exhibited significant progress, with its proportion of worldwide drone deliveries rising from 13 percent in 2022 to 32 percent in the initial six months of 2023.
Hence, Companies that focus on delivering superior labeled datasets customized to the specific needs of autonomous technologies are in a favorable position to benefit from this expanding market segment.
| Study Period | 2020-2032 | CAGR | 23.7% |
| Historical Period | 2020-2022 | Forecast Period | 2024-2032 |
| Base Year | 2023 | Base Year Market Size | USD 1.2 billion |
| Forecast Year | 2032 | Forecast Year Market Size | USD 8.3 billion |
| Largest Market | North America | Fastest Growing Market | Asia-Pacific |
North America is the most significant global data collection and labeling market shareholder and is estimated to grow at a CAGR of 23.8% over the forecast period. The market is presented with significant opportunities due to the adoption of AI services across various sectors and the growing utilization of smart devices and services by consumers in the region. In addition, the significant increase in manufacturing operations in the area enhances accessibility to technology and a wide range of products, all offered at affordable prices. In May 2022, Sumake North America, a reliable and comprehensive provider of automotive, electrical, and industrial solutions, will launch its latest product, the EA-SC100 tool management system. The system comprises a touchscreen interface for immediate visualization of results and a remote administration system for the collection of data and configuration of tools.
Asia-Pacific is anticipated to exhibit a CAGR of 24.1% over the forecast period. The growth can be attributed to the rising adoption of mobile phones and tablets, advancements in data processing technologies, and the widespread use of social networking platforms in emerging markets like China and India. The proliferation of intelligent devices amplifies the need for data collection and annotation. Face recognition technology in security and surveillance systems in China is projected to drive market growth in the Asia Pacific region. As an illustration, the Chinese government has enforced legislation on real-name registration within the nation, mandating that citizens connect their online accounts with their official government identification. In April 2022, a Reuter investigation of government records uncovered that numerous Chinese enterprises had created software known as "one person, one file." The software employs artificial intelligence to categorize datasets gathered on individuals in response to a high demand from authorities seeking to enhance their surveillance capabilities. The system enhances preexisting software by automating data management, eliminating the need for human intervention.
Furthermore, In January 2022, AIMMO, a Korean start-up, developed an AI data annotation platform that allows businesses to read and categorize image, video, sound, text, and sensor fusion data with incredible speed and precision. The Company has secured funding of USD 12 million in a Series A round to enhance its data labeling technology and facilitate global expansion. The software eradicates the inefficiencies associated with annotating, allowing customers to concentrate on their AI models.
The European regional market is projected to grow substantially during the forecast period. With the continuous enhancement of car obstacle detection technologies, it is expected that the European auto industry will experience growth in its market. The European Union concluded the development of a comprehensive legal structure for fully autonomous vehicles equipped with self-driving capabilities in July 2022. The revised General Safety Regulation, adopted in 2019, will take effect in July 2022 and set out the legal structure for the authorization of autonomous and automated vehicles in the European Union.
In addition, in 2021, France and Germany established a comprehensive legal framework for implementing autonomous vehicles in everyday transportation services. Since 2018, France has been actively implementing a national plan to introduce automated and connected transportation systems on its roads. Hamburg is projected to deploy approximately 10,000 autonomous shuttles by the year 2030. These factors are anticipated to influence the market throughout the projected timeframe.
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Image and video data are visual depictions of the world obtained through cameras or other imaging devices. This segment is essential in data collection and labeling, forming the foundation for training computer vision models. Annotated image and video datasets facilitate the development of object detection, image recognition, facial recognition, and video analysis applications. Precise annotation entails identifying and labeling objects, individuals, activities, and other visual components within images or video frames. The caliber and variety of annotated image and video datasets directly influence the efficacy of AI models in a wide range of tasks, including autonomous driving and content recommendation. With the increasing prevalence of visual AI applications, there is a growing demand for accurately labeled image and video datasets.
Audio data encompasses diverse sound-related information, such as spoken words, music, ambient noises, and other similar elements. Audio data plays a vital role in training machine learning models for tasks such as speech recognition, audio classification, and natural language processing (NLP) in the context of data collection and labeling. Annotated audio datasets are crucial for developing applications such as virtual assistants, voice-activated devices, and automated transcription services. Precise audio data categorization entails identifying and annotating speech, music genres, background noises, and other pertinent components. The increasing demand for voice-enabled technologies necessitates collecting and labeling diverse and high-quality audio datasets, which are crucial for advancing audio-related AI applications.
Healthcare applications extensively depend on annotated data for medical image analysis, disease diagnosis, and patient care. Annotated medical datasets, which include labeled medical images, patient records, and clinical data, play a crucial role in training artificial intelligence models for various tasks, such as identifying tumors in radiological images, forecasting disease outcomes, and customizing treatment plans. Precise categorization of healthcare data enhances progress in diagnostic precision and treatment efficacy.
Labeled data is employed for multiple purposes in the IT industry, such as cybersecurity, network optimization, and software development. Labeled datasets in cybersecurity facilitate the detection of abnormalities and potential security risks, thereby improving the system's overall security. Moreover, in software development, labeled data holds significant value for training models that pertain to code analysis, bug detection, and automated testing. This, in turn, contributes to the enhancement of software quality.
The end-user industries worldwide have observed decrement since the outbreak of the COVID-19 disrupted the entire value chain. The supply chain in the market is expected to curtail development projections until the current steep resurgence falls after the pandemic's spread. Additionally, consumers and enterprises face severe economic challenges due to irregularities in the service-based industry's operations and downtime. All the potential consumers are less likely to make investments in the technological developments in the organization. This scenario is anticipated to hamper the growth of the market.
