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What Is Data Labeling? Everything You Need To Know

Raw data is an important component of any machine learning or AI model, and it is typically collected from large groups of people or sensors that gather information about an object or event. 

However, raw data does not have enough built-in meaning to teach or train a machine-learning model, the majority of time is spent wrangling data and labeling it. 

Data labeling is an important step in data preparation and preprocessing because it gives meaning to your raw data by adding a critical layer of metadata that connects raw data to the prediction you want to make.  

The procedure entails adding tags or labels to raw data that represent the class of objects to which the data belongs. This allows your machine learning model to identify that specific class of objects in data that does not have a tag.

How does Data labeling work?

Data labeling is similar to an assembly line in that it takes raw source data and creates meaningful metadata that your machine learning algorithms can understand and use to make predictions. The process is typically divided into three types: initial model training, model fine-tuning, and human in the loop. 

The first category of Initial model training includes creating the unlabeled dataset and breaking it down into labeling tasks, which you then assign to Annotators who label each sample properly. 

In the second category, you use labeling to improve the accuracy of an existing model, primarily because there is new data that, if not labeled, will harm your model’s accuracy. 

For the Human in Loop category, you employ annotators that review your data to ensure only highly accurate results are delivered to users.

Benefits and challenges of data labeling

Effective data labeling is an essential aspect of creating models that can help you reduce a significant amount of time spent on a variety of business activities. What’s more, is that despite the high cost involved in maintaining the accuracy of data labeling the value that it provides is usually well worth the investment.

That being said, let us review some of the common benefits you stand to gain with data labeling and what challenges you might encounter. 


Efficient data labeling contributes to consistent data results and long-term reliability. The process also helps to reduce costs and time by utilizing software to complete the task. 

Furthermore, accurate data labeling ensures better quality assurance and data usability within machine learning algorithms, allowing your model to train and produce the expected results.


The most common issue with data labeling is that it necessitates a large workforce of skilled data scientists and engineers to produce and maintain high-quality work. Furthermore, the requirement to produce consistent and accurate data can lead to communication issues, which can result in faulty data tags or labels.

Other significant challenges include developing an efficient data Labeling process based on your company’s requirements and adhering to local and global data confidentiality and security regulations.

Data labeling approaches 

To ensure that your labeled data assists you in developing a high-performance ML model, you must consider a variety of factors and methods to determine the best labeling approach. For instance, the approach you use will be determined by factors such as the complexity of the unstructured datasets, the project’s time frame, and the number of people involved in labeling.

Some of the most common types of data labeling approaches include; In-house Data labeling, Crowdsourcing, Outsourcing, Machine based labeling, and Synthetic labeling.