Global Data Annotation Tools Market share is predicted to exceed USD 10 billion by 2028. This industry is expanding with services demand from medical and technology organizations, as the labeling of various information like medical tests and images, datasets, and other elements consumed for artificial intelligence & machine learning technologies. BFSI, healthcare and retail sectors are fast adopting AI & ML for reinforcing business decisions, bringing notable opportunities for data annotation tool providers.
Today, online retail companies are also using different types of texts, images, and audios to make it easier for machines to identify products and display them according to a customer’s requests. Data labeling tools utilize advanced technologies, such as AI and machine learning to give accurate responses to an expansive range of customer queries.
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Global text annotation tools market share will witness a steady growth rate. Text annotation is gaining immense importance in many sectors as this tool uses tags that highlight criteria, such as phrases, keywords, and sentences that can generate a lot of virtual traffic.
AI models are trained using annotated or highlighted data to help them accurately identify the products and services customers search for. Several marketing companies use text annotation tools to increase the visibility of their products and solutions on different search engines. These tools can greatly help to enhance customer shopping experience, thereby improving the profit margins of organizations.
Automated annotation approach will capture a major share of data annotation tools industry. Manual annotation can be quite tedious and time consuming, which can delay a lot of other processes that are dependent on this approach.
An auto annotation system can greatly improve the speed of labeling a vast volume of data. This helps companies meet their business targets on time. Moreover, unlike manual annotation tools, automated ones do not have to be trained for a long time to create different labels.
The second most important advantage of using automated annotation tools is that they offer great consistency in tagging different items in the same dataset. These merits will propel the demand for an automated approach to data annotation.
AI in retail is fast becoming one of the most sought-after technologies. Retailers have been able to boost their efficiency and performance with the help of AI. Furthermore, AI systems have streamlined many processes in the sector, making it much easier for customers to get accurate responses to their search queries. Thanks to the technology, customers are also able to check out of virtual stores.
The developments have been made possible only due to the heavy use of data annotation tools to help AI machines accurately identify the different types of products available in retail shops. Self-checkouts are becoming quite popular among customers, strengthening the technology use.
For example, the Amazon Go store doesn’t depend on human employees and a customer can pick up their groceries and leave the store. This idea was turned into reality only because of the facial annotation of thousands of photographs of customers that were stored in the company’s virtual data files. Such innovations will encourage other retail companies to use data annotation tools.
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Key participants contributing towards data annotation tools Market growth include Alegion Inc., Appen Limited, Amazon Web Services, Inc. (Amazon.com, Inc.), Clickworker GmbH, CloudApp, Inc., CloudFactory Limited
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