Data collection and labeling is an AI-driven data process used to categorize, classify, and identify the data collected to create powerful algorithms. The labeled data is fed to machine learning software to train the software about the data's properties. After completing the machine learning with the available information, the unlabeled data is sorted out by the machine learning stimulator to provide the consumers with operational results. The use of data collection and labeling in multiple industries such as healthcare, manufacturing facilities, automotive vehicles, and enterprises for data organization and automated data management is expected to boost the market.
The global data collection and labeling market size is estimated to progress at a healthy CAGR of 23.75% during the forecast period 2020–2027.
The market size of data collection and labeling is estimated at USD 0.7 billion by the end of 2020. Further, the growth is attributed to several factors and extensive implementation of data collection and labeling in some crucial sectors like manufacturing, healthcare, and automotive.
According to Straits' analysis, the increasing use of social media, sharing of audio, video, and data in descriptive text format have gained more contextual in different applications, increasing the demand for the implementation of data collection and labeling. This feature improves the safety of operations, identifies the problem, and sorts the visual or audio data to use the collected data in various applications.
In the healthcare sector, the data collected using X-ray, CT scan, blood reports, MRI scans can be classified as symptoms of confirmation of a particular ailment. The patients with the same results can be diagnosed quickly due to the data labeling algorithms used for all the annotations fed to the machine learning stimulator. The augmented work for AI data training in the healthcare sectors is done with the specialized medical image annotations and applications in the pathology, ophthalmology, radiology, and dermatology.
The usage becomes more prominent in the healthcare industry as the adoption of machine learning techniques aid in developing a classified and organized dataset with a distinguished list of the particular cases, therefore, developing and protecting the stored data of the organizations. This further helps healthcare operators to maintain the strong machine learning healthcare data, the advantages of which could be leveraged to regulate the workflow in times of increased workload, imbalance in staff and incoming patients, increasing the need for extensive automation application in the healthcare facility.
Data collection and labeling prove extremely useful in manufacturing facilities to streamline the manufacturing process and help in surveillance with a strong outlook for a quality check using image data collection and labeling, therefore, providing ease in developing ideal products. In this smart factory application, customers can efficiently use data labeling for annotating defects in the faulty manufactured product, which helps classify the defect-free manufactured products and products that need reworking.
According to the industry experts, the evolution of different approaches in the data collection and labeling processes such as outsourcing, internal labeling, crowdsourcing, specialized outsourcing companies, data programming, synthetic labeling is expected to further increase the spectrum in a variety of applications and organizational setups. These customers leverage the freedom offered by multiple approaches to choose the machine learning systems and AI learning as per the enterprise's requirement or the end-user of the data collection and labeling.
The use of data collection and labeling in the automotive sector increases the target customer base for manufacturers and helps project a broader perspective of the market development. Data collection and labeling provide enhanced vehicle safety for regular vehicles, improved behavioral instincts to driverless cars, and elevate the market's ADAS safety standards. These unique abilities are forecasted to aid for substantial growth of the market during the forecast period 2020–2027.
The image and video segment are expected to comprise more than 37% of the total market due to the availability and use of data in image or video format for data collection and labeling. The photos and videos in the manufacturing sector are used to identify and make classifications base on the dimensional parameters and further analyze and collect data regarding the surgery procedure and treatment sessions.
According to the Cloud Factory Limited, consumers using AI-powered technologies utilize 80% of the total allocated time in the process concerned with data collection and labeling, which underlines the importance of data labeling in AI technology. The data collected from apt and accessible data types to develop enhanced machine learning modules better operations hold eminent importance.
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.
North America is expected to retain its strong share in the market after the market evaluation. Adopting AI services in multiple end-use sectors and consumers' increased use of smart devices and smart services in the region provides substantial opportunities for the market. Furthermore, the considerable influx of operations by manufacturers in the area increases easy access to the technology and diverse product portfolio with cost-effective solutions.
Asia-Pacific is expected to register a swift growth pace in the market due to the high level of technology integration in the different end-use sectors and increase demand for smartphones and development in smartphone usage to promote the developments of the market. Adoption of IoT and artificial intelligence techniques to propel the market with a significant pace
The market for data collection and labeling is categorized as a market with optimistic potential for developing the market with increasing applications in the variety of end-use sectors that leverage the advantages imparted by the AI technologies and machine learning stimulation. The development of specific data collection and labeling service providers provide the products based on the consideration of vital requirement factors before product procurement to develop the traction towards these manufacturers' products. The factors that need to be considered are the complexity of the use case, time parameter for the development of the system, size of the organization, the structure of the IT and data science team, and the available training data.
The prominent players identified across the value chain are Globalme Localization Inc., Trilldata Technologies Pvt Ltd, Alegion, and Reality AI. These players contribute to a promising share in the Global Data Collection and Labeling market. The market is classified as an emerging market due to the need to create awareness about the benefits of using technology. Additionally, the global market vendors are also trying to cement their sales foothold in different regions in the market by working on finalizing acquisitions and collaborations to attain better customer reach with the ability to serve more customers at a time quality of the company.
Some market players active in the Data Collection and Labeling market are
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The Middle East and Africa (MEA)
Central and South America and the Caribbean