What Is Data Labeling in Machine Learning? An Explainer

Businesses spend a lot of time, revenue and manpower on collating raw data.Irrespective of industry backdrop, several functional units spend hefty software budgets, networking resources and staffing to label data. But, as the spectrum of machine learning grows at an astounding frequency, these data labeling tasks are being operationalized with data labeling software to annotate new and unstructured data.

Be it healthcare administration, automotive, banking and financial services, legal services, or IT, data labeling has massively reduced costing overheads, cash investments and liabilities.

Data labeling harnesses the robust calibre of machine learning software to pre-train algorithms on labeled data. With AI data labeling, users can segregate image, audio or video raw data into categories and speed up product ideation or analysis to ensure a good brand experience.

Data labeling understands the context between data variables and their distance from other variables to predict a potential match or category. During the first stage of machine learning model production, this technique is used to crunch large volumes of diverse datasets, segregate them with main attributes, and eliminate outliers. 

Also known as data preprocessing, once the training data is labeled and ready, it needs external human annotation to recheck whether the inputs are accurate or not. After pre-training and training, the labeled data is deployed in a live ML code environment. 

This data is used to validate, test and predict the usability of a machine learning model. Labeled data is used to perform predictive modeling on test data. This way, it accurately analyzes and categorizes datasets to train an AI model and detect patterns.

Why use data labeling?

Given the critical role of data in AI, labeling guarantees that training data and testing are structured meaningfully for the intended applications. Data labeling is critical in supervised learning as it allows a machine learning model to learn and make predictions based on data structure and patterns. 

High-quality labeled data results in precise and accurate machine learning models. On the other hand, if the data label is incorrect, the model's output will likely also be inaccurate. It will struggle to perform its intended task effectively.

Data labeling also fosters a deep understanding of data. The process involves careful examination and categorization of data points, which can often reveal an organization’s hidden patterns and insights that may not be apparent at first glance. 

This deeper understanding supports various applications, such as improving existing machine learning models, identifying new business opportunities, or simply gaining a better grasp of the information you possess.

Labeled data vs. unlabeled data 

While both labeled and unlabeled data is used to train ML model, there are different end use cases and applications you can expect from each:

Labeled data is used in supervised learning to train and test a machine learning model. Based on physical attributes and features, data is labeled and categorized into one or more classes, like dog, cat, building and so on. The process of labeling data is time and resource incentive but is beneficial for improving machine learning model performance. ML models trained on labeled datasets can provide better predictions, reduce retraining or outlier possibilities and empower to build better products and services.

Unlabeled data is a heterogeneous raw dataset that lacks labels and annotations and is used in unsupervised learning. Machine learning algorithms trained on unlabeled data look for inward patterns, links, styles and similarities within data attributes for data tagging. Unlabeled data is readily available and doesn't require much external annotation in the training phase. But, if the unsupervised algorithm couldn't predict the class, those data points are labeled by a human oracle.  

Data labeling vs. data annotation

The prime purpose of data labeling and data annotation is to provide more context on category of data to predict unseen data better. But both label data in different ways:

Data labeling, or data labeling service is a way to classify raw and unstructured data  in the initial phase of an ML development workflow.  The labeled training data is utilized in the machine learning model to predict new categories or improve existing ML workflows. Data labeling analyzes the features of existing data and improves the prediction accuracy. it aids to faster data analysis because the algorithm has a vast understanding of previous datasets and uses it to classify new ones.

Data annotation involves enriching raw data with metadata, descriptions, or context to make it machine readable. It includes techniques like bounding boxes, background illumination, hyperpixel segmentation to divide the input data into buckets and simplify the classification process for the ML algorithm. Data annotation can be either done manually or through data annotation tools like SuperAnnotate, LabelBox and so on.

How does data labeling work?

The process of data labeling involves a series of steps that often include human annotators and machine algorithms to assign meaningful labels to different kinds of information.

• Data collection: The first step is gathering raw data from which the model will learn. This data can include images, videos, text, audio, or any other relevant format for any given task.
• Data preparation: Once collected, the data has to be preprocessed to remove irrelevant outliers, eliminate multicollinearity and calculate initial findings and human intervention. THis makes the dataset easy to understand during the training and testing stage.
• Labeling process: Once the data is prepped, human labelers or annotators come into play. They analyze the prepared data and add specific labels that describe the content perfectly.
• Quality assurance: To verify the accuracy and consistency of the labeled data, a quality assurance process begins. During this step, annotators review different subsets of labeled data to identify and rectify discrepancies.
• Model training: Finally, the labeled data is fed to the machine learning model. The model learns to identify patterns and relationships between the data and the corresponding labels by analyzing the labeled examples. This learning process empowers the model to predict new, unseen data accurately.

Common data labeling types

Different types of data labeling are used depending on the nature of the data and the problem at hand. Here are some common types. 

Computer vision

Computer vision pares down the process of assigning meaningful labels to various objects, scenes, or actions within visual data. One common application is image classification, whereby computer vision algorithms automatically categorize images into predefined classes. For instance, in a dataset of animal images, a computer vision model can be trained to recognize and label images of cats, dogs, or birds. 

Another critical aspect is object detection, which sees computer vision identifying and outlining specific objects within an image using bounding boxes. This is particularly useful for scenarios where multiple objects coexist in an image, such as detecting and labeling different vehicles on a road. 

Another computer vision technique is semantic segmentation. It involves labeling each pixel in an image with a corresponding class to provide a detailed understanding of object boundaries and segmentation. These computer vision approaches significantly accelerate the data labeling process and reduce the manual effort required for annotating large datasets. 

Computer vision facilitates the creation of more granular and precise annotations, which enhances the quality of labeled datasets. It enables applications like facial recognition so computer vision automatically detects and labels faces in images or videos. It enhances efficiency and contributes to the accuracy and scalability of machine learning models.

Natural language processing (NLP)

NLP involves identifying and classifying attributes such as names, locations, and organizations within text. NLP models assist annotators by automating parts of this process. Sentiment analysis, another NLP application, helps with labeling text with sentiments like positive, negative, or neutral, expediting the annotation of emotions or opinions in large datasets. It's essential to initially segment and annotate sections of text with relevant tags within your dataset. 

For instance, this process might comprise marking the underlying sentiment or purpose behind a section of text, pinpointing various parts of speech, classifying locations and personal names, or highlighting text embedded within images. By using NLP technologies, data labeling in the realm of natural language becomes more efficient, accurate, and scalable, ultimately supporting the training of robust machine learning models for chatbots, language translation, and sentiment analysis.

Audio processing 

Audio processing techniques convert spoken words into written text to facilitate labeling oral content. It transforms an array of sounds, ranging from human speech to nature sounds like animal calls, into a structured format suitable for machine learning applications. 

The initial step in this process typically consists of transcribing the audio content into text format. The data can then be enriched with labels and classified into categories for deeper analysis and understanding of the audio's characteristics. 

This labeled and categorized dataset serves as the foundational training material for machine learning algorithms that target audio-based tasks. It refines the data labeling process for audio datasets to support the training of models for applications such as speech recognition, speaker identification, and audio event detection.

Data labeling key methods

Organizations apply data labeling using different methods depending on the scale of the dataset, quality standards, and resource availability. Here are key approaches to data labeling.

• Internal labeling: The internal labeling approach requires more time and effort since it uses in-house data science experts. It typically favors large companies with comprehensive resources but does provide greater accuracy, simplify tracking, and increase quality.
• Synthetic labeling: Synthetic data labeling generates new project data from pre-existing data sets. It does so by using extensive computing power – in turn increasing the pricing. This method enhances data quality and time efficiency. Synthetic data tools play a crucial role in this process by providing the necessary algorithms and frameworks.
• Programmatic labeling: This approach uses scripts that reduce time consumption and human annotation. Due to the possibility of technical problems, human-in-the-loop (HITL) is part of the quality assurance process.
• Crowdsourcing: Crowdsourcing platforms, like Amazon Mechanical Turk or Figure Eight (formerly CrowdFlower), allow you to distribute your data labeling tasks to a large online workforce. This approach can be faster and less expensive than in-house labeling, though the quality of the labels may vary due to the range of skill levels of the crowd workers. 

Data labeling benefits

In today's tech-driven world, investing in data labeling is a smart move for any business that uses machine learning. Some key advantages of implementing the data labeling process are discussed here.

• Optimizing model variables: Data labeling simplifies complex categorical variables into binaries that are easier for the model to understand. It also helps include control variables, which are essential for interpreting the relationships between different variables of a model. This optimization reduces the complexity of the model and improves its efficiency.
• Improved prediction accuracy: Data labeling ensures that the model learns from the right information to reduce the chances of mistakes or biases creeping in. This keeps the model's results reliable and trustworthy. It also helps the models understand the data better and learn patterns more effectively, leading to improved performance in tasks such as recognizing objects in images or understanding language.
• Data usability: Consistent data labeling improves the usability and accessibility of labeled datasets for tasks such as model evaluation, validation, and deployment. Well-labeled datasets are easier to interpret, analyze, and visualize, and they enable stakeholders to derive actionable insights and make informed decisions based on the data.
• Consistency: Consistent data labeling improves workflow and makes it easier and faster for annotators to label large volumes of data. Well-defined labeling guidelines and standards help maintain consistency among different annotators and labeling tasks, which reduces the time and effort required for data preparation. 

Data labeling challenges

While there are benefits to data labeling, it also presents challenges. Some of the most common are:

• Dealing with the high costs: Training machine learning models requires data labeling, but it can strain resources and budgets. As datasets get bigger, labeling them becomes more expensive and time-consuming. Whether using professional annotators or services, finding the right balance between cost and quality is challenging.
• Tackling time constraints: Even with some automation, setting up data pipelines and manually labeling large amounts of data remains a lengthy process. Developing efficient labeling strategies and tools saves time.
• Reducing human errors: Humans can make mistakes when labeling data, lowering its quality and affecting model performance. Implementing quality checks catches and corrects errors early on.
• Addressing privacy and security concerns: Labeling sensitive data raises privacy and security issues. Ensuring compliance with regulations and implementing strong security measures protects labeled data from unauthorized access.

Confirming accuracy and efficiency in data labeling is crucial for training robust machine learning models and achieving desired outcomes. 

Here are some best practices to consider, regardless of your approach.

• Proper data collection: In ML, one of the key factors to consider is the quality of the data. The data has to be varied yet closely aligned with the problem at hand. Diversity in the data allows for the development of models equipped to handle different scenarios, while specificity reduces the likelihood of errors.
• Conduct bias checks: Perform thorough bias checks to prevent your model from becoming overly specialized in one particular situation. This creates a model that adapts across a range of circumstances.
• Use a proper annotation approach: Establish clear guidelines for annotators, provide training, and implement quality control. Regularly update guidelines for efficiency and adaptability. This approach creates reliable datasets for machine learning and insights.
• Establish a testing and quality assurance (QA) process: Start with clear guidelines, criteria for evaluation, and training. Assign experienced reviewers to assess labeled data in order to provide feedback. Iterate based on insights and consider automation tools. Maintain open communication for feedback among teams. 

Best data labeling software for 2025

Data labeling solutions are critical for companies that work with machine learning. These tools enable the creation of high-quality labeled data, which is useful for developing accurate and robust machine learning models.

To qualify for inclusion in the Data Labeling category, a product must:

• Integrate a managed workforce and/or data labeling service
• Ensure labels are accurate and consistent
• Give the user the ability to view analytics that monitor the accuracy and/or speed of labeling
• Allow the annotated data to be integrated into data science and machine learning platforms to build machine learning models

Below are the top five leading data labeling software solutions from G2’s Winter 2024 Grid® Report. Some reviews may be edited for clarity.*

1. SuperAnnotate

SuperAnnotate is a leading platform that lets you build, fine-tune, and iterate AI models with high-quality training data. The platform facilitates collaboration among team members and offers management tools that keep track of project progress, data curation, and automation features. It’s designed to support a secure and efficient workflow, whether for small teams or large enterprises working on multiple and challenging datasets.

What users like best:

“The platform allows users to organize datasets, assign tasks to team members, track progress, and monitor annotation quality effortlessly. The ability to create custom workflows and automation rules further enhances productivity, enabling teams to efficiently handle large-scale annotation projects.”

- SuperAnnotate Review, Hoang D. 

What users dislike: 

" Finding results based on a specific condition is still code-based. That's one thing I found where it could use some improvement."

- SuperAnnotate Review, Sai Bharadwaj A. 

2. Appen

Appen is an easy to use data labeling platform that builds better training pipelines  and reduces manual overheads for businesses. It reduces the overall time and resources required for data entry and data mining and automates machine learning production for faster model implementation and better output accuracy. It comes with a bunch off services like pre-labeling, pre-training, database management, training quality and so on.

What users like:

"The platform's ability to provide very high levels of accuracy for our previous need for tagging images, video, and text. Analyzing accuracy and a high level of completion was extremely efficient and easy. Appen helped get my business up and running, so that is a major upside."

- Appen Review, Cliff M.

What users don't like:

"There are more worst things than good things. I am an active member of appen since 2018. First they took 6 months to approve my account. Then they started giving small data collection jobs. As a beginner I didn't know that their pay rate is much much lower than other freelancing websites. Also their rater roles are very cheap. The app AMR is the worst app on any store"

- Appen Review, Nithin R.

3. Encord

A leading data annotation and active learning platform, Encord provides tools for teams working with visual data. It’s an end-to-end platform that helps where you can safely develop, test, and deploy AI systems at scale. Use it to create high-quality training data, fine tune models, and assess quality. 

What users like best:

“I like the ability of task management and automation tools to simplify and optimize complex workflows. Such tools can help increase efficiency and productivity, reduce errors and redundancies, and enable better collaboration among team members. The convenience of having everything organized and tracked in one place also adds to their appeal.”

- Encord Review, Alve H. 

What users dislike: 

“The tool could benefit from some customization options. The ability to personalize hotkeys and tool settings according to user preference would greatly enhance the user experience.“

- Encord Review, Samuel A. 

4. Dataloop

Dataloop is a platform designed for data annotation, model development, and data management. It’s predominantly used in AI and machine learning contexts, especially when dealing with large datasets and images. It’s transforming the way organizations build and use AI applications.

What users like best:

“Dataloop has been a valuable asset in streamlining administrative tasks for my colleagues and myself by efficiently organizing management and numerical data. It functions as a convenient tool that keeps important information easily accessible, improving our work's organization and speed by providing in-depth insights into our job's operations.”

- Dataloop Review, Deepak G. 

What users dislike: 

“It took me some time to figure out the flow of the program and it would be helpful if there were tutorials available to guide users. The setup process also took longer than expected, but this may vary depending on the vendor.”

- Dataloop Review, Yogendra S. 

5. Sama 

Sama is an AI data labeling and data annotation platform that provides data annotation, data preprocessing and image annotation services for generative AI applications. The platform is deployed to detect, segment and categorize data with improved accuracy and precision. Sama is the ideal choice for enterprises that have high AI maturity and run machine learning production environments.

What users like:

"I enjoy a lot of confidence in the training data I feed my AI models, which in turn leads to better performance. Sama provides high annotations’ accuracy, which is above 95% in many scenarios."

- Sama Review, Nikita D. 

What users dislike:

"The type of work we send to Sama is not the typical AI work they do for most companies. Thus, Sama's expertise regarding our specific digital marketing needs is not that of a traditional digital marketing agency. Consequently, we aren't able to outsource more complex digital tactics to Sama."

- Sama Review, Ricarda D. 

Turning chaos into clarity 

Raw data alone isn't enough to unlock its true potential. Data labeling plays a crucial role in the development and advancement of new technologies, particularly in machine learning and artificial intelligence.

By properly labeling data and following best practices, organizations can open up new opportunities and move toward a future where decisions are driven by data.

Learn how to optimize training, validation and production of machine learning algorithms with machine learning operationalization to centralize AI operations.