Data Labeling – why would you need a specialized company?

Data Labeling – why would you need a specialized company?

Automation and artificial intelligence (AI) are playing an ever-growing role in today’s business. The benefit of this however is in direct relation to the quality of data used in the training of the AI models. And here is how data labeling companies can be of great help in this crucial phase of every AI project.

What is data labeling?

During data labeling raw data samples are reviewed and noted/tagged with meaningful and informative labels to them. ‘Data’ can be any type of data, such as text, audio, images and videos. This is the first step in developing a machine learning model or AI. The process of labeling gives the context so that the model can learn from it. This is needed for example in the training of self-driving cars or in voice and video recognition.

The process is in effect manual – humans produce very accurate labels for a mass of data which are then used to teach the AI to recognize patterns according to a given task. This process is known as ‘annotation’. Using the properly labeled dataset, the AI learns by example, leading to predictable and accurate labels of new unlabeled data, thus refining its algorithm.

Application of data labeling

Many businesses are growing to the idea that machine learning can help make their business operations more productive. Companies with better-trained AI models are better equipped to win new business, capitalize on opportunities, and foresee threats. And the key factor here is the accuracy of the data sets built in the machine learning models.

Which is the best option for data labeling: in-house, crowdsource, or outsource?


One can assume that having an in-house data labeling team will mean higher security, more direct control, and better protection for their IP. However, few companies can afford the expensive, complicated, and time-consuming process of creating the necessary training data. You will need a professional team of data labelers, office space and the right software and tools.


If you choose crowdsourcing you hand in your data-labeling requirements over to an online pool of various people. This could be a good option if the cost, rather than the quality and accuracy of data, is of highest importance. Some studies indicate that crowdsourced workers operate with an average 4-8% error rate in basic transcription tasks. In comparison, the error rate for managed teams – in-house and outsourced- is under 1%. This is over four to eight times difference in the error rate. Another minus to this method is the lower degree of confidentiality.

Outsourcing to data labeling companies

To combine the advantages of both methods, the most preferable option is to work with a specialized data-processing company. Working with an experienced and trustworthy partner can help companies save costs without compromising quality. In data labeling companies a managed team of trained, professional annotators is able to quickly adapt to any requirement and is familiar with the most up-to-date and sophisticated annotation tools. By outsourcing you can establish a long-term relationship with your partner which is particularly useful when new projects come up. In addition if the work has some seasonality and requires scaling up or down the workforce, your outsourcing partner will save you the laborious process of hiring, training and laying off people.

Types of Data Labeling to Outsource


Contrary as it may be to expectations understanding unstructured text data is difficult for AI. The applications here are, for example, training a chatbot for a website, document management systems, or labels on packaging. Text annotation involves training the model to identify words and phrases and to understand paraphrasing and synonyms.


With image labeling, humans help the vision systems of an AI to ‘see’ specific objects, but this requires a considerable amount of training. Data labelers have to add bounding boxes around objects in an image (such as a person, flower, or cat) and label them so that the model can understand and recognize them in an unlabeled image later on.


Like in image annotation, video annotation involves adding bounding boxes, polygons, or key points on a frame-by-frame basis. This helps the vision system to track the movement of the object in the video. For example, humans are needed to outline all the pixels containing faces of people or trucks in an image.


Everybody has heard of Alexa or Siri – digital voice assistants are becoming increasingly part of our daily lives. Companies are also training their own virtual assistants to understand voice communication to operate in their specific industry. Natural language generation and processing mean transcribing thousands of hours of audio recordings and transferring the data to the AI model. These are huge data sets which represent a challenging task to process.

Labeling data is not a simple task, data labelers have to have skills, focus, and great attention to detail. If you do not want to risk the success of your project, it is of utmost importance to be able to rely on a company with good track record, competent teams and trustworthy relations.

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