Data labeling teaches machines how to understand different pieces of information. It is the process of annotating and categorizing information, which enables machines to interpret and comprehend various data formats.
When you label data, you put tags or labels on information, such as pictures or words. This helps machines understand this information, which is crucial in designing intelligent technologies.
For example, when we label photos of cars and people, we're helping self-driving cars recognize these things on the road. The better we label the data, the wiser and more reliable the machines become. Conversely, machines won't perform as expected and might even make mistakes when you don’t label data accurately.
Many organizations use data labeling software to turn unlabeled data into labeled data and build corresponding artificial intelligence (AI) algorithms. The process has many names, such as data annotation, data tagging, training data, and data classification, but all refer to data labeling.
Top data labeling statistics
Data labels help AI systems understand and recognize patterns. The quality of these labels directly affects the system’s performance. It’s a time-consuming process that involves lots of resources.
To do this, many organizations use crowdsourcing platforms or outsource from developing countries where labor costs remain reasonable. The statistics below will highlight the same with precise details.
- The market for AI-based automated data labeling tools is estimated to grow at a compound annual growth rate (CAGR) of over 30% by 2025.
- Europe is estimated to hold the third-largest share of the global data labeling market.
70%
of data labeling is done in India, China, and other developing countries because of reasonable labor costs.
Source: Gitnux
- More than 60% of enterprises have adopted in-house labeling with their dedicated team of labelers in 2020.
- It’s predicted that almost 80% of leading companies will require external help with their labeled data needs by the end of 2022.
Data labeling market statistics
The data labeling market is trending upward. It’s evident from the statistics below. Look at growth prospects in Asia Pacific, North America, China, and worldwide and understand what regions contribute the most.
- The global market for data labeling solutions and services was valued at $11.83 billion in 2022 and is projected to grow at a compound annual growth rate (CAGR) of 21.3% from 2023 to 2030.
- The IT sector accounted for 32.6% of the global data labeling market revenue 2022.
- North America led the data labeling market in 2022, accounting for over 31.0% of the total revenue.
76.9%
of the revenue share came from the manual data labeling segment in 2022.
Source: Grand View Research
- The Asia Pacific region is expected to expand 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 forecast CAGR of 28.9% from 2023 to 2030.
- The Chinese market, which makes up over a third of the global market, is predicted to grow at a CAGR of 21.8% between 2019 and 2024.
Industry-wide data labeling statistics
Several sectors like healthcare, retail, e-commerce, banking, financial service, and insurance (BFSI), and automotive have been leveraging data labeling to create smart technologies. Let’s take a look at their market size and how it’s predicted to grow in the foreseeable future.
- The healthcare industry relies on data labeling for diagnostic automation and treatment prediction. Its market is anticipated to reach a $1 billion valuation by 2026.
- The retail and e-commerce sectors are significant users of image labeling technologies to improve online shopping experiences. The retail sector will dominate the industry in terms of CAGR during the forecast period (2020-2025).
- Data annotation technology is crucial for developing autonomous vehicles, contributing to the automotive sector's growth in data labeling. The global data annotation market will be worth USD 5.55 billion by 2024.
25%
of the data labeling market will have dominance in the automotive industry by 2026.
Source: Srive
- The BFSI sector held a market size of over $200 million in the data labeling market in 2019. It highlights the robust use of data labeling tools in financial services.
- The text-labeling segment holds a 28% share of the global data-labeling market.
- Semi-supervised data labeling is anticipated to grow at a CAGR of 30.3% during 2020-2027.
- The market for machine learning data labeling tools in Europe will reach a valuation of around $1 billion by the end of 2030.
You can't take accuracy lightly
Data labeling is more than just a routine task, it's a vital step toward building reliable AI systems. The statistics show the market promises substantial growth. Companies are open to leveraging new technology to achieve higher accuracy data labeling. More accuracy will make smart technologies function effectively and as expected from them.
This presents new opportunities for businesses to enter and saturate the needs of the market.
Check out the best data labeling tools on the market for small businesses and understand the level of accuracy they offer.
