Data Academy

The Basics of Data Labeling

How Data Labeling Works, Benefits and Challenges, and Different Types Of Data Labeling Tools and Services

What Is Data Labeling?

In the simplest sense, data labeling is the process of adding tags to data to easily identify and classify data points in a file. Data labeling is often applied in the artificial intelligence and machine learning (AI/ML) sphere, where labeled data is used to train a machine learning model to understand new, unlabeled data it encounters. Once the model’s algorithms successfully identify objects, the computer can output a decision or outcome using computer vision, natural language processing, named entity resolution, or other computer processes.


Data labeling transforms raw, unstructured data into structured data based on an ontology, or classification system, that is appropriate for the machine learning model. For example, security cameras provide enormous volumes of unstructured video data. Data labeling can transform this raw data into structured data by identifying objects such as persons and shopping items, then the relationships and events that apply to those objects. With this structure applied, the data can be used to train a theft detection model.

How Is Labeled Data Used?

As seen in the above example, data labeling can be used in retail security, as well as nearly every other industry. The top 10 data labeling industries are tech startups, automotive, aerospace, retail and e-commerce, public sector, finance and banking, insurance, autonomous systems, healthcare, and robotics. These industries alone are expected to spend 814 million on data labeling in 2022 according to a report by Cognilytica. 

Another data labeling use case is when a car insurance company trains a model to recognize the difference between cars, humans, and street objects in street camera footage in order to determine liability for an accident. When new footage from a car accident is fed into the model, the computer can then identify relevant objects and output a decision based on the model. 

Other use cases of data labeling include:

  • Training a home security camera to tell the difference between a pet versus a human
  • Running sentiment analysis—whether a review is positive, negative, or neutral—by scanning the text of customer reviews
  • Reporting the outcome of a soccer game by assessing game footage and identifying possession, passing, and goal scoring

Identifying riders and horses
In this sports use case, bounding boxes identify riders and horses while data points allow horse body part relationships within the identified horse figure


Benefits and Challenges of Data Labeling

Labeled data provides a strong foundation on which a machine learning model can understand any new information it is given and produce accurate results. The process of data labeling transforms potentially unreliable data into high-quality ML data that, together with robust data gathering and curation, can help train a model to provide high-confidence and representative outputs. When the trained model is in production, edge cases can be identified and accurately labeled to drive continuous model improvement.

Data labeling can be time-consuming and thus can be expensive to do with in-house data science teams. The best machine learning models are trained on tons of data, which means that data labelers, or annotators, must label large volumes of text, images, and especially video. Depending on the software that annotators are using and the scope of the project, this can take tens of thousands of hours. However, some of these challenges can be mitigated by different types of data labeling tools and services below.

Types of Data Labeling Tools and Services 

There are many approaches to data labeling. Depending on the amount of labeled data that is required for your machine learning model, here are some options:


When you need to keep costs down, you can use in-house resources (data annotators) in combination with open-source or commercial software to scope, structure, and execute the project.


When you want to start scaling your project, you can use the options presented in option one, but with the addition of crowdsourced resources. This means hiring and managing additional contracted workers to scale up your project. 


When you need to use a more customized data labeling solution, you can work with a managed platform partner to structure the project (curating data, creating data ontology, and creating validation workflows), but still own the sourcing and management of the labor force. The labor force may be internal, a third-party partner, crowdsourced.


The final approach to data labeling is by working with a fully managed labeling service provider who can fully manage the technical project solution and as well as identify, train, and manage a qualified workforce. Fully managed labeling services typically use proprietary labeling software with built-in efficiencies like automatically identifying similar objects in following video frames and human-validated consensus algorithms within the software to ensure accuracy.


It’s useful to look out for a data labeling software that has pre-labeling, or the ability to provide a reliable hypothesis of what set of objects are, to reduce annotation labor. For example, pre-labeling can automatically apply optical character recognition (OCR) labels to images of receipts. This means a human annotator simply has to verify the label instead of identifying and labeling the section of text from scratch. Another use of pre-labeling could be identifying sections of video footage that do not contain any relevant objects to the model—and therefore should not be annotated—to reduce the labeling load for annotators.

A data labeling partner that can create synthetic data is helpful when it is difficult to obtain enough unlabeled data from real-life video, images, or documents. Edge cases and other difficult-to-obtain examples can be generated rather than collected from real-world sources, allowing your machine learning model to produce accurate, unbiased results.

Work With the Right Data Labeling Service Provider

Alegion is a data labeling and data services solutions provider with the tools, service expertise, and experience to guarantee the delivery of high-quality ML training data. We provide proof of concepts before starting work on the project, drilling down into the question, “What problem are we trying to solve?” We offer four tiers of service, ranging from licensing of our intuitive, AI-enabled labeling software to a fully managed labeling service.

Our technology covers the basics and more: object localization and segmentation using bounding boxes, polygons, point, lines, and other specialized shape types; object relationships; classification ontologies; object tracking; interpolation; pre-labeling; pose estimation visualization; and sophisticated tooling for quality assurance checks. 

We work with professionals from Fortune 500 companies and startups alike to provide quality data, the ability to scale, and transparent pricing. Whether you need us to manage your annotation workforce or train your team on using the software, we have a customer success team to back your project at every stage. Learn more about Alegion’s data annotation solutions or request a demo.

 Listen to VP of Product Ben Schneider speak about Alegion’s data labeling capabilities

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