Data labeling is essential for teaching machines how to comprehend various types of information. This process involves annotating and categorizing data to enable machines to interpret and understand different data formats.
By labeling data, tags or labels are added to information like images or text, helping machines grasp this information, which is crucial for developing intelligent technologies.
For example, labeling photos of cars and people helps self-driving cars identify these objects on the road. Accurate data labeling enhances machine intelligence and reliability, while inaccurate labeling can lead to errors in machine performance.
Many organizations utilize data labeling software to convert unlabeled data into labeled data and create corresponding artificial intelligence (AI) algorithms. This process goes by various names such as data annotation, data tagging, training data, and data classification, all of which refer to data labeling.
Top data labeling statistics
Data labels play a crucial role in helping AI systems understand patterns and improve system performance. This process requires considerable time and resources, often outsourced to developing countries with reasonable labor costs.
The market for AI-based automated data labeling tools is projected to grow at a CAGR of over 30% by 2025.
Europe is expected to hold the third-largest share of the global data labeling market.
70%
Data labeling is predominantly done in India, China, and other developing countries due to cost-effective labor.
Source: Gitnux
Over 60% of enterprises adopted in-house labeling with dedicated teams in 2020. It is predicted that almost 80% of leading companies will require external assistance for their labeled data needs by the end of 2022.
Data labeling market statistics
The global data labeling market is on an upward trend, as indicated by the following statistics. Explore growth prospects in various regions like Asia Pacific, North America, China, and worldwide to understand the major contributors to the market.
The global market for data labeling solutions and services was valued at $11.83 billion in 2022 and is projected to grow at a CAGR of 21.3% from 2023 to 2030.
The IT sector accounted for 32.6% of the global data labeling market revenue in 2022.
North America led the data labeling market in 2022, contributing over 31.0% of the total revenue.
76.9%
The manual data labeling segment accounted for 76.9% of revenue share in 2022.
Source: Grand View Research
The Asia Pacific region is expected to grow at a significant CAGR of 22.8% over the forecast period (2020 to 2025). The global data collection and labeling market size was valued at $2.22 billion in 2022, with a forecasted CAGR of 28.9% from 2023 to 2030.
Industry-wide data labeling statistics
Various sectors like healthcare, retail, e-commerce, BFSI, and automotive are leveraging data labeling for smart technologies. Explore their market sizes and growth predictions in the coming years.
The healthcare industry relies on data labeling for automation in diagnostics and treatment prediction, with a market expected to reach $1 billion by 2026.
Retail and e-commerce sectors use image labeling technologies to enhance online shopping experiences, with retail projected to lead in CAGR during the forecast period.
Data annotation technology is crucial for developing autonomous vehicles, contributing to the growth of the automotive sector in data labeling, which is estimated to reach USD 5.55 billion by 2024.
25%
The automotive industry is predicted to dominate 25% of the data labeling market by 2026.
Source: Srive
The BFSI sector had a market size of over $200 million in the data labeling market in 2019, indicating strong use of data labeling tools in financial services. The text-labeling segment holds a 28% share of the global data-labeling market, with semi-supervised data labeling anticipated to grow at a CAGR of 30.3% during 2020-2027.
You can’t overlook accuracy
Data labeling is a crucial step in building reliable AI systems, with substantial market growth predicted. Companies are embracing new technologies for higher accuracy in data labeling, ensuring efficient functioning of smart technologies. This presents new business opportunities in the market. Explore the best data labeling tools for small businesses to understand the accuracy they offer.
