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4 Reasons Why Businesses Should Outsource Data Labeling

Data Labeling Services

Training Machine Learning (ML) models to act like a human, even in tiny capacities, is a difficult endeavor. But, these models are crucial for modern-day business decision-making, so accurate training must be accomplished. This is made possible through supervised data labeling services, ensuring accurate data labeling of raw data samples (like audio, video, images, text, etc.). 

Basically, explanatory labels (like marketing cars, pedestrians, poles, hydrants, dogs, bikes, and birds in moving traffic or a sidewalk) are tagged to dataset. This tagged dataset is fed to the ML model, so it can learn about the object and how they look. 

But, manually labeling data is a time-consuming, tedious, and complex task that becomes downright impractical with a greater number of samples. A lot of pressure is put on the in-house team to complete annotation tasks on time, possibly resulting in poor quality output. The better way to go about such annotation is to outsource the task to a data labeling services provider. They will have the resources to deal with the challenges that come with the task and scale with ease while maintaining accuracy and speed. 

Read on to learn more about the advantages of outsourcing data labeling, the labeling process, and its impact on the AI industry. 

What Is Data Labeling?

In simple terms, it is the process of reviewing data samples in-depth and attaching meaningful and informative tags to their various constituent elements. The data samples can be in an image, audio, video, textual format, or any combination of them. The applied label helps the ML model distinguish between the various constituent elements and gain the context of the intended/target subject concerning the entire sample. This is the first step towards a model’s development/training. 

Labels are added by the establishment of boundaries around the target object/subject in a given data sample. Various types of boundaries are created based on many boundary conditions. The use of a particular boundary type depends on the type of the project, its goal, the type of data sample in question, and other such factors.

Since a machine is incapable of any task initially, a human must manually label data samples to begin the training process. This collection of samples serves as the blueprint that the model uses to continue the process by itself. After that, it becomes automated data labeling, which is necessary for training the model at scale and improving its predictive and target identification accuracy. The process of manually labeling data is called annotation in the industry. 

The Impact Of Data Labeling On The AI Industry

The world is automating everything it can and to the maximum extent possible. This is due to its many advantages, like cost reduction, quick task completion, data security, etc. The global investment by businesses in AI reached $94 Billion in 2021, signifying the importance of the technology. Such massive shifts require model training on immense scales while maintaining utmost accuracy. This is simply not possible by manual data labeling alone.

Thus, it wouldn’t be wrong to say that the combination of manual and automated labeling of sample data is at the heart of the AI revolution and is keeping the AI industry going ahead.

It is estimated that around 80% of the time spent on AI/ML development is used for data labeling and sorting through training data. Moreover, with each accurately annotated data sample, a model improves its perceptive capabilities. It builds on that capability to better identify targets in new, more complex datasets. Its ability to anticipate future events also improves when it comes to video-based training. 

So, it’s safe to say that data labeling is indirectly driving the business decisions of not just the executives of AI companies but also those in other industries. 

The Need To Outsource Data Labeling Services

The need for high accuracy, data sample sets of choice, confidentiality, and such may compel you to choose an in-house team to perform annotation for AI/ML model training requirements. The control you will have over the process can be a tempting reason for going with this option, but its drawbacks are far too many to ignore.

  • High initial costs for equipment, personnel, proprietary data sets, etc.
  • Lack of expertise, especially with newer developments in the field
  • Inability to scale on-demand
  • Possible need to train employees exclusively for the project
  • Setting up adequate, effective, and efficient project management practices, etc.

These issues compound as your labeling project progresses. However, outsourcing it to professional data labeling service providers serves as the antidote to these challenges. The following advantages of Data Labeling Services are behind it:

  • Low Prices

Outsourcing agencies introduce economies of scale since they have numerous personnel working on various projects simultaneously. They will also have the infrastructure to support them, preventing you from having to invest in the same for your in-house teams. Currency exchange rates are another crucial factor that helps to bring down the cost of your labeling project significantly. 

  • Quick Turnaround Time

Everything is ready to go from the moment you outsource your project to a data labeling service provider.

Firstly, you get the required number of personnel and necessary infrastructure,

Secondly, a high level of expertise, efficient management practices, and

Lastly, other time-saving measures that cut down your project completion times.

If the service provider has facilities across the globe, they can overcome the limits of time zone and get your project worked on constantly to give quick results. 

  • Instant Access To Expertise

With outsourcing, you don’t have to waste precious resources looking for experts to label your data. They are available from the start, thus preventing delays. Labeling agencies also ensure that their experts are up to date with the latest techniques and technologies in the field. Hence, assuring you of good service. The experts may even guide you with your project requirements if you have difficulties determining any aspect of it. 

  • Data Security

Data labeling service providers are aware that since they hold data from many companies, they are prime targets for cybercriminals. Thus, they take appropriate measures to ensure that the client’s data is safe and secure. You needn’t be concerned with additional data security and privacy measures once you outsource the project. You can set the terms for the same during the contract discussion phase, including the type of encryption to be used, who can access the data and for how long, protocols to be followed, etc. 

Advancing technology means professional data labeling agencies will continue to add more services and advantages to their kit. Further making a strong case for outsourcing relevant work to them. 

In Conclusion

You can achieve the desired level of AI competency with accurate, timely, and cost-effective data labeling. The advantages offered by outsourcing labeling projects make it the most viable choice in this regard, giving you the returns you seek with your dataset and AI-assisted business decisions.


Lisa Ross is a content strategist & a technology enthusiast working at SunTec.AI, a leading data annotation company. She has extensive experience writing about various transforming and advanced technologies like artificial intelligence and machine learning. In her spare time, she loves to explore and learn about new tools and technologies shaping the various industries- data science, eCommerce, robotics, and healthcare, among others. She keeps herself updated on all the new trends, innovations, and advancements happening around AI/ML technologies and pens down her knowledge to present well-researched and informative articles to help businesses leverage these technologies for their advantage. Other than writing, Lisa loves to read new books and travel in her free time.

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