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Common Misconceptions About Data Labeling and Classification

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Just like human students need teachers to help them understand educational material, machines require more than just raw data to learn. Data labeling is a key component of a successful machine learning project, but unfortunately, there are some common misconceptions about its importance, how it is done, and who can do it. 

Why is data labeling such an important task? To put it in relatable terms, if a medical student in an anatomy class learned all of the wrong terms for various parts of the body, they wouldn’t make a very good doctor. If a machine learns material based on incorrect or missing labels, the results it delivers won’t be as accurate as they would be with good data labeling. The more you understand about why quality data labeling is so critical, the more successful your machine learning project will be.

What Is Data Labeling?

Data labeling is essentially the process of adding meaningful tags to a raw data set. These tags serve as a starting point for the machine to learn. One example is object detection labeling for images. For this type of data labeling project, objects in an image are identified by humans, a bounding box is drawn around them, and they are labeled appropriately. The machine uses this information to find similar objects—for example, license plates, logos, or pedestrians—in other images.  

Because they lay the foundation for classifying future data sets, it’s critical that the labels be accurate. If errors are made during the labeling process, the machine will learn based on those mistakes, and the quality of the output will be compromised.

Common Misconceptions About Data Labeling

It’s easy to make assumptions about data labeling if you don’t have a background in data science or machine learning. On the surface, it might seem like a relatively simple task or a box that just needs to be checked in the machine learning process. However, if the importance of data labeling isn’t fully understood, or if it isn’t done correctly, it could cause unnecessary delays or more significant problems down the line. 

Be aware of these common misconceptions about data labeling as you start to wade into machine learning.

Accuracy and quality are not important.

Data labeling lays the foundation for supervised machine learning, which makes it a crucial step in the training process. Poorly labeled data or missing information will affect the end results, which could have dire consequences for some applications. For example, self-driving cars rely on data labeling to learn how to identify pedestrians or other hazards. If the training data set is inaccurate or incomplete, the car won’t have all of the information it needs to successfully reduce risk. 

When it comes to data labeling, accuracy refers to the proximity of labels. For example, in object detection labeling, a bounding box that conforms to the shape of the object being labeled is more accurate than one that has a more generic shape. The quality of a data set is defined by how consistently accurate it is. If the bounding box doesn’t fully enclose the object being labeled, or if it is offset from the object, the quality might be compromised. 

Humans are not required. 

Although software can assist with data labeling, it requires human involvement. This is especially true for supervised learning, which relies on data labeling for both the input and output data sets. Humans are necessary for ensuring the quality of data sets through training and testing. The model improves through iterations by correcting its mistakes and affirming its correct responses.

Automated data labeling is possible, but it also requires humans to write the code and provide an initial data set with accurate labels. As mentioned above, humans would also need to be involved to ensure the quality of data labeling that was done automatically. Because this is one of the most time-consuming steps in supervised machine learning, it might be tempting to try to take people out of the equation, but without some degree of human involvement, machines can’t properly learn.

Anyone can do it.

Data labeling might not feel like the most glamorous task, but as the foundation of supervised machine learning, it’s not something that can be assigned to just anybody. Fortunately, you don’t necessarily need to have a degree in data science to be successful at data labeling (especially if you use a platform that requires no code), but it is critical that the people doing the work understand its importance. This might mean assigning the task to a business analyst or somebody with enough subject matter expertise to know which features must be labeled and how to appropriately identify them. A team of collaborators who have been trained on both proper data labeling techniques and the features being labeled is another good choice.  

Privacy might also be a concern, depending on the type of data you are processing, so make sure you work with people you trust. Whether you’re working on proprietary technology or have sensitive data, you want to be sure that your data is protected. 

It doesn’t cost that much.

Data labeling is a manually performed task that can get expensive when all of the hours are added up. The average data scientist spends 60 percent of their time cleaning and organizing data, and a quarter of this time is just for data labeling. Performing this task in-house not only takes up valuable time but can also get expensive. 

However, although it’s not possible to completely eliminate human involvement, you can save time and money by automating some of the labeling and outsourcing to a trusted provider. Working with a platform that makes it easy for human collaborators to label images, text, or audio files will also ease the burden on in-house teams and reduce costs. 

Related: Download the checklist that lays out everything you should consider  before implementing a machine learning project or workflow →

It has to be done in-house.

Although it’s true that you shouldn’t trust your data labeling with just anybody, it is possible to outsource this labor-intensive task to free up time for other important work. Outsourcing data labeling is a common practice because it allows in-house teams to focus on other core tasks and adds more capacity for time-sensitive projects. 

When outsourcing data labeling, security is a legitimate concern. To protect your valuable assets, work with a provider that controls how people access the data so that it does not flow over public or unsecured networks, get processed in public places, or get potentially exposed to malware. Ensure that any outsourcer you choose has training and security protocols in place for their teams. 

How Skyl Can Help with Data Labeling

Skyl can help you build a high-quality data set with our Labelwise platform. The process uses a guided data labeling workflow that includes:

  • Configuring assets
  • Designing the data labeling job
  • Assigning collaborators
  • Providing quality checks
  • Delivering a high-quality data set

The work is performed by qualified data scientists who understand the importance of getting it right.  

When you work with Labelwise or any other Skyl.ai tool, your data never leaves the system, and it stays secure. We securely store all customer data on the client’s physical premise or using Google data centers, where no unauthorized access is allowed through the network, and we use SSL encryption for all web interactions. You have the freedom to export your data sets at any point because you own your data 100 percent of the time. 

Our labeling services include:

  • Image classification: Annotate images from the categories you provide.

  • Named entity extraction: Extract certain types of data from unstructured text.

  • Object detection: Label objects within images with a bounding box and tag.

Data labeling is just one of the many steps required for a successful machine learning project. To get a better understanding of the other key parts of the process, download our free machine learning checklist today. 

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