What is Data Annotation and Why is It So Important?

Category: AI Insights

Published date: 18.03.2021

Read time: 9 min

AI and machine learning are some of the fastest-growing technologies that offer incredible innovations that benefit various sectors of the global economy. However, in order to create such systems, a lot of training data is required to allow the machines to recognize things we want them to find. This training data needs to be annotated by human workers to prepare the raw data to be consumed by machines. This, in essence, is what data annotation is. As we will see later on, data annotation is required for AI projects in any industry and it is an essential aspect of any machine learning project. Let’s start by looking at some of the industries that rely on machine learning and AI and what data annotation is required for such projects. 

The Various Types of Data Annotations 

There are several classifications of annotations: 

There are also many types of data annotations. Some of the most widely used include: 

  • Tagging – This is simply labeling all of the people or objects 
  • Semantic Segmentation – One of the most detailed types of annotation where you classify each pixel in an image 
  • Lines and Splines – This is where all of the boundaries of the objects are contoured 
  • 2D Bounding Box – This is simply drawing a square or a rectangle around an object in an image
  • 3D Bounding Boxes – If you need to annotate the dimensions of the object such as length width and height, 3D Bounding Boxes would be used
  • Landmark Annotation – This form of annotation is used to place key points along peoples’ facial features so the system can recognize them
  • Timestamps – This is when certain events happen in the video or audio and you need to place a timestamp when this event happened
  • Optical Character Recognition – This is when you need the system to recognize the characters printed or handwritten on the page and transform them into editable text formats
  • Transcription – If during an audio recording needs to be written down, this s the form of annotation that will be used
  • Sentiment Analysis – You would use this type of annotation to help the machine understand whether the tone of voice is positive or negative. 

The Importance of Quality and Accuracy in Data Annotation 

Even though data annotation is very tedious and time-consuming work, it is necessary to the overall success of the project. In fact, the accuracy of the data annotation will play a big role in whether or not the system will function correctly if any biases exist if it is able to recognize the needed items in its surroundings, and a lot of other important outcomes. Companies developing AI and machine learning projects understand the importance of data annotation, but they do not have time to do such work internally. This is why they choose to outsource all data annotation tasks to a service provider. 

When you are looking at possible data annotation companies to outsource your work to, it is important that they have a rigorous QA process in place. Here is how Mindy Support ensures the accuracy of all the data annotation work performed: 

  • Initial Assessment – We review all of the project documentation and interview key people and technical staff to make sure we correctly understand the project requirements. 
  • Verification and Validation – Throughout the duration of the project we test a representative sample of all of the work that has been done so far to ensure that the needed level of accuracy is maintained. 
  • Quality Review – We methodically examine key project factors to determine the accuracy percentage of all the work that has been completed. 
  • Repeated Quality Training – If a percentage of the work was not done correctly, we assign additional training to the data annotator(s) to make sure they understand the requirements. 


How Can Mindy Support Help You With Your Data Annotation Project

Mindy Support is one of the largest BPO providers in Eastern Europe with six offices all over Ukraine. Given our size and location, we can assemble even the most sizable teams within a short time frame. In addition to this, we offer our clients the following benefits: 

  • 95-99% quality delivery for data annotation.
  • 2000+ agents, so we can easily scale up & down
  • Deep expertise – 8 years on the market, 500 completed DA projects
  • Effective project launch in tight deadlines
  • Only dedicated teams with a dedicated PM

When you outsource to Mindy Support, you can have peace of mind that your data will remain secure since we are ISO 27001 certified and we are in the process of receiving SOC 2 attestation. Contact us today to learn more about how we can help you. 

The Need for Data Annotation in AI By Industry 


One of the most popular applications of AI is in the automotive industry with autonomous vehicles. You have most likely heard about companies like Tesla, Waymo, and many other developing cars that can drive by themselves. In order to train the machine learning algorithms that power self-driving cars, a lot of video and image annotation is required to allow the system to recognize things like other cars, street signs, pedestrians, and many other things. This is usually done via labeling, 2D/3D boxes, semantic segmentation, LiDAR, and other types of annotations. This is something we will look closer at in the next section. 


The healthcare industry is also actively relying on AI especially given the disruptions caused by the recent pandemic. AI systems can take a lot of work off the shoulders of human doctors allowing them to devote more time to patients. A lot of companies are developing AI products that can analyze medical images like X-rays, CT scans, mammograms, and many others and provide a diagnosis. There is still a big role human doctors need to play in providing quality healthcare since their expertise is required to annotate the medical images that train AI systems. Also, they still need to confirm the diagnosis provided by the machines and they are the ones working directly with patients. 


Last, but not least, we would like to take a look at the agriculture industry which relies on various robotics and drones to grow greater amounts of healthier crops. This includes robots that can harvest ripe crops by themselves, fertilize the soil, provide aerial surveillance of the field, and analyze crop growth, and many other applications. Although robotics is a separate industry in its own right, robots are allowing farmers to save a lot of money since they can replace human labor in performing routine tasks. Such robots use LiDAR technology that produces a 3D Point Cloud, which is a representation of how they see the physical world. This 3D Point Cloud needs to be annotated to allow the robot to recognize all of the objects in their surroundings and their proximity to those objects. 

How We Can Help

Mindy Support successfully works with the companies that adopt AI developments and provides them with the most accurately annotated data. We have 2000+ agents experienced in annotating all possible types of data: image, text, video, audio, 3D point cloud annotation. Contact us today to learn about how we can help you.


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