Five things to know about data annotation

In a world driven mainly by technology, businesses that rely heavily on the latest in the field have a cutting edge over others. 

As artificial intelligence (AI) takes over most of the work, we expect machines to emote accurate responses while performing in various scenarios. Data annotation services come in handy in these situations. Data annotation is one such service that is essential to the growing field of machine learning (ML). Some of the critical things about data annotation are listed below:

Application of Data Annotation

Growing service industries, i.e., healthcare, hospitality, and all other industries that are striving to take customer experience to enhanced levels are exploring data annotation services. Data annotation is nothing more than tagging or labelling various data forms, like text, images, videos, etc., for the machines to understand. In most conceivable situations, it is not possible for machines to extract all scenarios and their context, and thus, tagging or labelling data provides the machines with near accurate information. An example to elaborate on this would be an AI-driven machine like a surveillance camera; it is imperative to maintain accuracy to avoid mishaps. Here, image annotation done with the help of data can help the machine understand whether an alert alarm should be sounded or not in a given situation.

Importance of Data Annotation

With the increasing reliance on artificial intelligence (AI) in all fields, data annotation has a bright future because it allows for a precise interface between human experiences and AI. Annotating data can enhance the customer experience for a business; for instance, a positive, accurate interaction with a chatbot on the homepage can convert a client into a potential customer. The text annotated for the chatbot increases its accuracy and response time to satisfy the customer.

Types of Data Annotation

Depending on what input/information is crucial to scale up and boost the business, annotation can be of different types, namely: image annotation, video annotation, audio annotation, and semantic annotation.


Data annotation is tedious and time-consuming, so most companies prefer to outsource their data annotation work .The cost also varies depending on the volume of data. The safety of available data while outsourcing is also taken care of by data annotation companies. Data annotation also takes into account “human-in-the-loop,” which keeps it safe and reliable because human intervention or validation is not completely eliminated. The data given by a company is secure, as the data annotation companies abide by non-disclosure agreements (NDAs).


Data annotating companies only need raw data like texts, videos, and images, and they use their tools to convert this into annotated data for machines to understand. So even if a business is a start-up or an established company, the transition to an AI-driven business is more manageable with the data annotation services available in the market. Any business planning to get its data annotated to scale up the business in the virtual world can easily do so by partnering with a data annotation company.


A business can boom into a fast-trending business if it has the correct combination of an attractive, scalable idea and a cost-efficient data annotation service.

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