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Data Annotation vs Data Labeling: Which is Right for You?

data annotation vs data labeling
The success of AI and ML projects depends on the quality of training datasets. Understanding and utilizing the differences between data annotation and labeling is the key to building robust and accurate AI and ML models for realizing AI’s full potential.

The success of any AI or ML project depends on high-quality training data. But for many companies, acquiring this data and preparing it for interpretation by machines can be a hurdle. This is where data annotation and labeling services play a role. However, with these two terms often used interchangeably in the industry, it’s crucial to understand the subtle differences between these two processes to ensure you take the right approach, or choose the right partner, for your specific project needs.

A study by MIT researchers highlighted that an AI model trained on well-annotated data could achieve accuracy levels up to 20% higher than those trained on poorly annotated datasets. This emphasizes the critical role of precise data annotation and labeling in reducing errors and enhancing the performance of AI systems.

So, what gives you smart data to train your AI and ML algorithms, data annotation, or labeling? Let’s take a closer look at data annotation and data labeling in relation to their significance, processes, and the role they play in enhancing AI and ML models.

What is data labeling?

Data labeling is the process of manually assigning relevant labels or categories to data points to improve their accuracy and effectiveness for machine learning models. It involves a human-labeled data set, which can be time-consuming but can result in significant improvements in model performance. The labeled data is then used as input into a machine learning algorithm.

data labeling example

What does data annotation entail?

Data annotation is the action of adding meaningful and informative tags to a dataset, making it easier for machine learning algorithms to understand and process the data. Previously, data annotation was not as crucial as it is now because data scientists used structured data which did not require many annotations. During the last 5-10 years, data annotation became more critical for machine learning systems so they can work effectively.

data annotation example

Data Annotation vs. Data Labeling

Data annotation and data labeling are terms often used interchangeably in the context of machine learning and artificial intelligence. However, there can be subtle differences in their usage and implications, depending on the context or the specific processes involved.

Data Labeling Data Annotation
Definition Data Labeling It is the process of assigning a specific classification, or label, to a piece of data or information. Data Annotation It is the process of adding labels or tags to data to provide additional context to it.
Purpose Data Labeling To train machine learning models with instructions about what each data point represents. Data Annotation To help machines comprehend and interpret various forms of data, such as text, video, images, or audio.
Objective Data Labeling It focuses on categorizing or classifying data into predefined classes, essential for supervised learning. It’s used in applications like sentiment analysis and object recognition. Data Annotation It is detailed and context-rich, suitable for complex tasks requiring nuanced understanding, such as computer vision and medical imaging. It involves tasks like drawing bounding boxes or outlining semantic segments.
When to use Data Labeling Identify and tag data samples commonly used in the context of training machine learning (ML) models. Data Annotation It is required for a variety of use cases, including computer vision, natural language processing, and speech recognition.
Capacity Data Labeling
  • Classify images
  • Recognize emotion
  • Warn about defects
  • Recognize an object
Data Annotation
  • Identify objects
  • Analyze speech
  • Estimate defects
  • Track an object
Process Data Labeling Data labeling involves categorizing images into ‘cat’ or ‘dog’, to more complex scenarios, like identifying sentiments in text data as positive, negative, or neutral. Data Annotation Data annotation involves creating labels or annotating specific features within an image, audio file, or text document.
Complexity and Detail Data Labeling Labeling is generally more straightforward, potentially amenable to automation, and focuses on assigning predefined tags to data points. Data Annotation Annotation is more complex, often requiring human judgment to capture detailed attributes within the data.
Tools used Data Labeling
  • Text Labeling: Categorizing or tagging text data for (NLP) applications used for sentiment analysis, topic classification, and entity recognition.
  • Image Labeling: Identifying and tagging visual elements in images for computer vision models used for simple classification tasks, object detection, and complex scene understanding.
  • Video Labeling: Identifying and tagging objects, actions, or events in dynamic scenes for surveillance, activity recognition, and autonomous vehicles.
  • Audio Labeling: Categorizing audio clips or tagging specific sounds, speech, or music for voice recognition, sound classification, and music recommendation systems.
  • Semantic Annotation: Linking data to concepts within a knowledge base, providing context and meaning beyond simple categorization.
Data Annotation
  • Image Annotation: Marking images using bounding boxes, polygons, landmark annotation, and semantic segmentation to identify objects, features, or concepts within them.
  • Text Annotation: Annotating text data for NLP tasks for sentiment analysis, entity recognition, categorization, and linguistic annotation.
  • Video Annotation: Adding temporal information to the labeling process and used for applications requiring motion analysis like surveillance and autonomous driving.
  • Audio Annotation: Labeling audio data with transcriptions, speaker identification, and emotional tone for voice recognition, music classification, and sentiment analysis.
  • 3D Point Cloud Annotation: Annotating three-dimensional data obtained from LiDAR and other 3D scanning technologies for autonomous vehicles, robotics, and augmented reality applications.
Tools used Data Labeling
  • SuperAnnotate
  • Encord
  • Kili
  • Dataloop
  • V7
Data Annotation
  • Labellerr
  • Labelbox
  • SuperAnnotate
  • Appen
  • V7
Market Size Data Labeling The global data labeling market size is expected to expand at a compound annual growth rate (CAGR) of 26.6% from 2021 to 2028. Data Annotation The demand for accurately annotated datasets is expected to reach USD 556.67 billion by 2026, growing at a CAGR of 39.47% from 2021 to 2026.

Key considerations for choosing between annotation and labeling

Choosing between data labeling and data annotation depends on your project’s complexity and the level of detail required by your AI or ML model. Both processes are crucial in preparing training data for machine learning algorithms, yet they cater to different needs and complexities within AI and ML projects. Here are the nine key considerations for choosing between annotation and labeling:

choose between annotation and labeling
  • Project Complexity: Data labeling is often sufficient for straightforward classification tasks in which the objective is to categorize data into predefined groups. Annotation, however, is essential for more complex projects requiring detailed analysis, such as object detection or sentiment analysis, where additional contexts or attributes are necessary.
  • Data Type and Volume: The nature and volume of your data also influence your choice. Large datasets with diverse and complex data types (images, text, audio) might necessitate a combination of both processes to achieve the desired level of model training and accuracy.
  • Model Requirements: The specific requirements of your AI or ML model play a crucial role. If your model needs to understand nuanced details within the data, such as the relationships between objects or the intricacies of language, annotation provides the depth of information required. Labeling is more suited to models that rely on simpler categorical data.
  • Volume and Scalability: Large datasets might benefit from labeling for basic categorization, while annotation can provide deeper insights for complex models. However, annotation may require more time and resources.
  • Accuracy and Precision Needs: Projects demanding high levels of accuracy and precision, especially those involving critical decisions based on model output (e.g., medical diagnosis, autonomous vehicle navigation), will benefit more from annotated data. Additional details can significantly improve model performance by reducing ambiguity.
  • Cost and Budget: Evaluate the cost-effectiveness of each approach against your budget and resources. Outsourcing can reduce up-front costs, while in-house efforts may require significant investment in tools, training, and personnel.
  • Resource Availability: Annotation is generally more time-consuming and resource-intensive than labeling due to the added complexity and detail. Assess your available resources, including time, budget, and expertise, to determine which process aligns with your project’s constraints.
  • Security and Privacy: Data privacy and security are paramount, especially for sensitive information. In-house labeling may offer better control over data security, while outsourcing requires the careful selection of partners with robust security measures.
  • Skill Requirements and Expertise: Effective data labeling requires individuals with a strong understanding of the predefined labels or categories used in the project. Data annotation demands a higher level of expertise, as annotators need to understand the specific context of the data and apply more nuanced annotations.


While data labeling and data annotation might seem interchangeable briefly, they play distinct roles in the world of AI and machine learning. Data labeling helps categorize, while data annotation provides depth and context. As the demand for more sophisticated AI models grows, the importance of understanding and effectively using both processes grows. Knowing the distinction ensures that your machine learning projects are built on a strong and accurate foundation.

Author Snehal Joshi
About Author:

 spearheads the business process management vertical at Hitech BPO, an integrated data and digital solutions company. Over the last 20 years, he has successfully built and managed a diverse portfolio spanning more than 40 solutions across data processing management, research and analysis and image intelligence. Snehal drives innovation and digitalization across functions, empowering organizations to unlock and unleash the hidden potential of their data.

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