Labeled data is data that has been annotated with tags or identifiers to add meaning and context. It is essential in supervised learning, where a machine learning (ML) model learns patterns by training on examples that include known outcomes. These annotations provide the “answers” that models use to learn how to map input data to correct outputs.

Labeling involves assigning metadata, such as categories, keywords, or numerical values, to raw data (e.g., text, images, or audio). Labels can be binary (e.g., yes/no), multi-class (e.g., category selection), or continuous values (e.g., risk scores). The accurately labeled dataset used to train a model is known as the ground truth — a reference that reflects real-world information and serves as the benchmark for evaluating model performance.

Why is data labeling important?

Data labeling plays a crucial role in machine learning (ML), especially during the training phase of supervised learning. It enables models to learn patterns within a dataset by associating input data with known outcomes. Labeled data tells the model what the inputs represent, allowing it to make accurate predictions based on those relationships.

The quality and completeness of labeled data directly affect model performance. Well-labeled data enhances a model’s ability to identify trends, generate reliable predictions, and generalize to new inputs. In enterprise settings, such as fraud detection in finance, patient risk stratification in healthcare, or demand forecasting in retail, this precision is essential.

Conversely, poorly labeled or unlabeled data limits a model’s ability to distinguish between data points, resulting in reduced accuracy and lower-quality outputs. Incorrect labeling can also introduce bias or errors that degrade the model’s overall performance.

How does data labeling work?

Machine learning models require labeled data to be trained using supervised learning. The labeled dataset forms the foundation from which the model learns to perform its designated task. Labels help the model distinguish between different types of input, enabling it to make accurate predictions.

Labels are assigned to raw data, such as images, text, video, or audio, based on the categories or values the model needs to learn. These labels guide the model in recognizing patterns and making distinctions when generating outputs.

The labeled dataset used for training is referred to as the ground truth. It provides a reference for evaluating how well the model’s predictions align with known outcomes, helping determine whether the model needs further fine-tuning or other adjustments.

What are common approaches to data labeling?

Data labeling is typically done using one of four main approaches:

Manual data labeling

In this approach, humans manually assign labels to data based on predefined guidelines to ensure consistency. While it allows for high accuracy, it can be time-consuming and resource-intensive, especially when working with large datasets.

Semi-automated data labeling

This hybrid approach combines machine-generated labeling with human oversight. An algorithm applies initial labels, which are then reviewed and corrected by humans as needed. This method can improve efficiency while maintaining accuracy.

Automated data labeling

Automated data labeling uses algorithms or software to assign labels to raw data without human intervention. It is significantly faster than manual labeling but may introduce inaccuracies. The models performing the labeling infer patterns and conventions from previously labeled data, which they then apply to new, unlabeled inputs.

Crowdsourcing

Crowdsourcing distributes manual labeling tasks across a large group of contributors, such as contractors, employees, or the general public. This method can speed up the labeling process compared to relying solely on in-house manual efforts, though it requires clear quality controls to ensure consistency.

What are the different types of data labeling? 

There are five common types of data labeling that help models achieve different outcomes:

Image labeling

Image labeling enables machines to identify information within images. It is used for tasks such as object detection, key point detection, image classification, and facial recognition. Labeled images help the model learn to distinguish different elements, allowing it to interpret visual data and perform specific tasks.

Text labeling

Text labeling supports tasks like sentiment analysis, named entity recognition (NER), text classification, and summarization. It helps models understand context, intent, and semantics — improving their ability to generate accurate outputs, identify patterns, and perform language-based tasks.

Audio labeling

Audio labeling involves annotating sounds for use in speech and audio recognition. Models are trained to identify various types of sounds — such as speech, alarms, or environmental noises. This often begins with transcription, which converts audio into text before it is labeled, helping the model associate sounds with specific meanings.

Video labeling

Video labeling assigns tags to objects or actions across video frames, allowing models to track movement and recognize activities. It is commonly used for object tracking, action recognition, and scene segmentation.

Time series labeling

Time series labeling involves tagging data points collected over time to help models understand temporal patterns. This enables predictive tasks such as trend analysis, anomaly detection, or forecasting based on historical data.

How is labeled data used in practice?

Labeled data is essential for training various types of AI models — including computer vision, large language models (LLMs), and natural language processing (NLP) systems — to perform specific tasks.

  • Healthcare: Labeled data is used to identify patterns in diagnostic imagery, helping models learn to detect abnormalities. Medical images are tagged with information about specific conditions, allowing the model to accurately recognize diseases and support clinical decision-making.
  • Finance: Data labeling supports fraud detection, risk assessment, and credit scoring by teaching models to recognize trends in payment behavior and identify anomalies. This improves the accuracy and speed of financial decision processes.
  • Retail: Retailers use labeled data to enhance customer experience through personalized product recommendations based on purchase and browsing history. It also supports more accurate demand forecasting and inventory management.