Data Labeling Explained: How It Supports AI and Machine Learning

Key takeaways

Data labeling is the process of assigning labels to raw data, such as text, images, or video, before it is used to train machine learning models.

• Data labeling transforms raw data into training sets, allowing machine learning models to learn, adapt, and improve their accuracy over time.

• Common types of data labeling include image labeling, text labeling, video labeling, and audio labeling.

• Data labeling plays a critical role in artificial intelligence (AI) and machine learning, as it provides the data necessary for supervised learning, a process in which algorithms train to recognize relationships and patterns independently.

Learn more about data labeling, how it differs from data annotation, and the best practices for ensuring high-quality training data. Afterward, if you’re ready to gain a deeper understanding of fundamental artificial intelligence concepts, enroll in the Machine Learning Specialization from Stanford University and DeepLearning.AI. Beginner-friendly, this program will give you the opportunity to develop skills in areas like deep learning, model training, unsupervised learning, and predictive modeling.

What is data labeling?

Data labeling is the process by which raw data receives labels before being used to train machine learning models. The labels provide important context to support the model in analyzing data, enabling it to make accurate predictions when encountering new data in the future in applications like speech recognition, natural language processing (NLP), and computer vision [1].

Why is data labeling important in AI and machine learning?

Data labeling is essential because it provides supervised learning models with correctly labeled training data, helping them recognize patterns and make accurate predictions. It is an important part of supervised learning, a specific type of machine learning where you train algorithms using labeled data to provide a more guided environment for learning to recognize patterns and relationships in data. Supervised learning differs from unsupervised learning, where the model learns to identify relationships within the data on its own, without the help of any labels. Utilizing labeled data for supervised learning is advantageous because it helps the model learn faster, particularly when working toward a predefined goal.

With the ongoing advancements in generative AI, data labeling is becoming increasingly vital for facilitating multilingual understanding and agentic reasoning across modern AI applications [2].

Read more: Supervised vs. Unsupervised Learning: Pros, Cons, and When to Choose

What are the main types of data labeling?

Data labeling looks different depending on the type of data you’re working with. Some types of data labeling include image, text, video, and audio labeling [3]:

• Image labeling: When labeling images, you can label specific objects to assist with object detection or use scene recognition to classify the surrounding details of the image.

• Text labeling: With text labeling, you can use the data for name entity recognition, optical character recognition, sentiment analysis, or text summarization.

• Video labeling: As videos move from frame to frame, video labeling gives you the ability to track specific actions, as well as objects.

• Audio labeling: By labeling audio data, you can create training data for machine learning tasks like speech recognition, even going as far as learning to detect emotion from audio data.

What are examples of data labeling?

Data labeling can apply to a wide range of use cases, since it's a useful technique across many machine learning methods. Labeled data in computer vision tasks consists of labels that identify specific features and key points in an image, such as objects, their locations, and the context surrounding them. One widely used data label in computer vision is bounding boxes, which use rectangular outlines or boxes to pinpoint the position of objects in images or videos. In health care, for example, computer vision is leading to positive outcomes for patients by analyzing medical images to help diagnose patients sooner.

In natural language processing, labeling data by tagging specific portions of text gives NLP models the ability to understand sentiment within text, detect spam, and summarize text into key points. Businesses can use sentiment analysis to better understand how consumers feel about their products and services by identifying negative sentiment and therefore creating opportunities for positive change to their product offerings.

Data labeling vs. data annotation: What’s the difference?

While data labeling and data annotation both add context to data, they do so differently, with data annotation focusing more on complex relationships within the data, which often require domain expertise. Data labeling, however, uses more simplified categories when classifying data. In practice, data labeling is faster, while data annotation requires a greater time investment [4].

What are the best practices for data labeling?

As you look to label data, you can select automated or manual approaches, or a combination of the two. Manual data labeling requires a human to use their best judgment to assign labels. Alternatively, automated data labeling uses algorithms and software for a more efficient process. However, it’s important to consider the potential bias the algorithm can introduce into the labeling process, creating a need for you to check over its work. Additionally, establishing clear guidelines is necessary so labelers understand the exact criteria to follow, as well as efforts to maintain data privacy, especially when working with sensitive data.

By combining manual and automated approaches, you can experience the benefits of both.

Techniques to consider for effective data labeling include [1]:

• Synthetic labeling: Using preexisting data, synthetic labeling generates new data to grow the size of your data set. You might consider this approach when your training data is insufficient.

• Crowdsourcing: In case of a time crunch, you could crowdsource data labeling to reduce overall costs while benefiting from a quick turnaround. However, since the task requires input from multiple contributors, it’s important to consider quality assurance (QA) to ensure labeling standards are met.

• Programmatic labeling: This automated labeling technique removes the need for manual labelers by following preprogrammed scripts.

• Human labeling: For high accuracy, using humans for manual labeling is a reliable strategy, especially in circumstances requiring particular attention to detail, such as medical data labeling.

Consider following some best practices for enhancing data labeling accuracy, which in turn can improve the quality of training data:

• Routinely evaluate ML models’ performance using labeled data and, if needed, update labeling guidelines.

• Provide annotators with adequate training and support.

• Enforce robust verification processes to ensure labeling consistency.

Who performs data labeling and what tools do they use?

Data labeling is performed by annotators who manually go through and label the data based on predetermined guidelines. By working as a team, annotators can switch contexts to help manage cognitive load, improving efficiency and accuracy. Having several annotators label the same data is an effective way to limit mistakes and avoid bias, as you can default to the consensus when deciding how to label it.

Additional roles that assist in the data labeling process often include a QA specialist, who is responsible for ensuring labeling quality standards are upheld, as well as a data scientist for managing data sets, and software developers who create and maintain the infrastructure used throughout the process.

Data labeling teams can implement tools to accelerate the labeling process and reduce errors. Popular tools for data labeling include SuperAnnotate, Label Studio, Labelbox, and Labellerr. Selecting the right data labeling tool involves considering factors such as data type, scalability, built-in security, and support for bulk data import or export.

Can I use ChatGPT for data annotation?

With the right plan in place, you can use ChatGPT for data annotation. Using proper prompt engineering techniques, optimal temperature settings, and some fine-tuning along the way, it can be an effective and low-cost tool for data annotation use cases like entity recognition and sentiment analysis.

Is data labeling a good career? Data labeling jobs explained

According to data from Glassdoor, the median total pay for data annotators is $60,000 per year [5]. Supportive and operational roles, such as data technician and database manager, offer opportunities in the field as well, with median annual total pay of $58,000 and $105,000, respectively [6, 7]. These figures include base salary and additional pay, which may represent profit-sharing, commissions, bonuses, or other compensation. Additionally, the global machine learning market may reach $684.4 billion by 2033, with a 26 percent compound annual growth rate between 2026 and 2033. Considering these data points collectively, now could be a promising time to join this field [8].

Working as an annotator requires accuracy and precision, as errors in the labeling process will ultimately result in errors in the model's outputs. You will also need to identify subtle details and patterns within the data. Since you will work on projects as part of a larger team, strong communication plays an important role in ensuring that you follow instructions and share feedback on any issues that can impact the quality of your team's work, such as low image quality or unclear instructions.

The salary information above is the median total pay from Glassdoor as of July 2026. These figures include both base salary and additional pay, which may represent profit-sharing, commissions, bonuses, or other forms of compensation.

Explore our free artificial intelligence resources

Subscribe to our weekly LinkedIn newsletter, Career Chat, for updates on popular skills, tools, and certifications. Then, check out some of our other free resources to learn more about AI.

• Watch on YouTube: AI Tools for Data Cleaning

• Take the quiz: Which AI Course Should You Take? Find Out in 1 Minute

• Gain expert insights: 6 Questions with an IBM Data Scientist and AI Engineer

Whether you want to develop a new skill, get comfortable with an in-demand technology, or advance your abilities, keep growing with a Coursera Plus subscription. You’ll get access to over 10,000 flexible courses.