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How to Choose the Best Data Labeling Company


Finding the best data labeling company to scale artificial intelligence and machine learning models has become critical for AI and ML companies. Companies that do not leverage AI and ML are at a significant competitive disadvantage. AI adoption to optimize cost and efficiency of backend processes through automation is inevitable and essential for survival in this and coming decades.

AI models to give out precise results demand more volumes of relevant and contextual datasets. But adequate and accurate data labeling is not attained overnight as it’s a long-term process which can continue for weeks and months together.

If Gartner is to be believed, 80% of AI or ML projects never reach deployment, and those that do are only profitable about 60% of the time. All these are due to negligence towards selecting a capable and high performing data labeling company.

80% of AI project time is used on data labeling in response to the volume of data generated by businesses.
It is nearly impossible for AI and ML companies to have readymade datasets to train their AI models, unless their internal systems are robust and agile.

Most companies, thus, rely on the best data labeling company, who are equipped with the infrastructure and facilities that can deliver accurately labeled training data.

Maybe this is the reason services provided by data labeling companies are continuously growing at 28% and will reach an estimated growth rate of $3.5 billion in revenue in 2026. Source

However, choosing the best data labeling company isn’t that simple. With a lot of subpar companies claiming to be the best at data labeling, AI and ML companies should be extra cautious when collaborating with them.

Partnering with an incompetent data labeling company could push back your AI model’s deployment indefinitely or lead you to lose dollars.

This article will help you choose a data labeling or data annotation company that is capable of fast tracking your AI and ML deployments.

How data labeling companies support AI and ML development

Correct data labeling has become an imperative with the increasing demand in technologies like artificial intelligence, machine learning, computer vision, deep learning, and image processing.

For AI and ML companies to reach a point where their AI models become scalable and deliver seamless and accurate results is a foremost challenge. It can be data for computer vision or natural language processing (NLP); labeling large-scale data is no walk in the park and requires operational experience and close attention to detail.

Data labeling companies take care of this critical activity to help our AI and ML companies. Taking help from data labeling services empowers in-house data scientists to focus on the core functions of research, development, and analysis. Data labeling companies helps them in getting annotation projects done within time, budget, and with precision.

How to choose the best data labeling company to scale up your AI and ML projects?

Now you know why data labeling is critical to the success of your AI and ML models. You are now aware of the benefits of working with a data labeling company to accelerate your AI and ML projects. But that does not mean you can rush to reach out to different third-party data labeling service providers. Do you know of how to choose the right data labeling partner for your AI and ML projects?

Here are seven things you should consider, and the questions you should ask the data labeling company before outsourcing your data annotation project:

1. Understand your data annotation requirements

Data labeling companies popping up like mushroom tops is overwhelming. It makes it crucial for you to do correct expectation setting, and discuss desired output with them to avoid disappointments.

  • Start by creating a Request for Proposal (RFP) for outsourcing companies to gauge their capabilities and the services they offer.
  • Spend time with your in-house team to fully scope your project requirements, inclusive of project objectives, timelines, quality metrics, and other key requirements.
  • Ask and answer these questions to conclude what to include in the proposal:
    • What are the different type of data you are working on?
    • What is the aim of labeling the training data?
    • How much data do you intend to label/ annotate?
    • Which type of data annotation will be required for the data type?
    • Which data annotation technique will be leveraged to attain the goal?
    • What are the data quality requirements?

Now that you have defined the project details and goals; it’s time to evaluate data labeling companies. Here are some attributes to be checked when looking out for a data labeling company.

2. Assess the technology required to annotate your data

Adequate labeling tools and technology are a pre-requisite for executing a data labeling project quickly and at scale. You can offer them the data annotation software/tool that you are using or can rely on the tool that they are hands on for preparing training data.
Given the project requirements you charted out – don’t forget to check out software features, flexibility, built-in quality control, collaboration features, and of course the affordability.
Assessing the tech capabilities of the third-party data labeling company will help you drive ROI in the long run.

3. Quality assurance if non-negotiable

In order to ensure that all your project expectations are met, make sure that human labelers are well-trained, and have adequate knowledge of the domain you operate in. Try to assess if they can respond quickly through a closed feedback loop. Find out if they are flexible to project demands in workflow changes, are transparent, and can properly communicate challenges – if any.
Direct communication with your data labeling team will empower you to get firsthand insights and suggestions from the people annotating your data.

4. Experience of data labeling projects counts the most

Before signing the contract with any of the data labeling company, do not forget to check out their credibility and background in the data labeling services industry. Apart from this, verify their experience with the type of data labeling your data needs. Also, ask for details and learning from previously completed projects, security certifications, domain expertise, and even the languages that they support.

Underestimating the skill set, expertise, and experience required for data labeling can prove to be a huge pitfall. Data labeling is not a simple task and a human error, or a small mistake, can accumulate and lead to severe consequences to the training data sets.

Inexperienced data labeling company may cause your project delay and cost you a lot of dollars due to the lack of quality resources, tools, and technologies.

5. Data security is prime

Transferring voluminous data to an outsourcing company via third party software means you have to trust the data labeling company. It also means that you believe they will maintain the data in an environment free of security breaches. Hence, pinning down a company that values data protection is critical. Don’t forget hackers are out on the hunt and poor encryption protocols may lead you directly into their trap.

Understand. It is your data, and it should be well within your control to decide whom you wish to give access to. Perform a robust background check about the person handling your data. Remember, most data breaches happen due to human error. Getting an NDA signed by the company and the employees guarantee data safety.

6. Ethical considerations have deeper roots

Human interaction is a key factor in data labeling since it requires skill and extensive training. A lot of outsourcing companies are not good pay masters and workers suffer despite their vital yet stressful responsibility. Consider humanization and labor laws when hiring data labeling services. Also, check out the culture and how the data labeling company embraces an inclusive working environment. Because diversity and inclusion are essential, training datasets they create will be more unbiased and ethical. Assessing their ability to follow ethical treatment of workers is important to avoid any future project problems.

7. Request a proof-of-concept

You have comprehended your project requirements and shortlisted a few of the data labeling companies. Does this mean you took take the next step of partnering with them? No. There is one last step you should take as a precautionary measure. Before jumping straight in, try to get a pilot project by the service provider. It will help you find out their services before you enter a long-term commitment with them.

This is so critical because you risk costly delays due to lack of resource quality and tools needed to label your data properly.


Finding the right data labeling company is challenging. It’s not a smart move to ask for sample sets from all of them, then compare vendors and test their services with quick projects before committing. And, after all these – even if you find the one that suits your project needs; you will be occupied for another 2-3 months preparing them to label your data.

It will be a wise decision to eliminate all these instances and reaching out to that stage of collaboration and start receiving quality datasets for your projects. Get in touch with the most tenured and experienced data labeling company for impeccable training data quality. The data labeling company should be the one that exceeds all the attributes mentioned above and ensures the partnership is profitable for both.

About Author

Snehal Joshi spearheads the business process management vertical at HitechDigital, 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.

I am a coffee lover, marketer, tech geek, movie enthusiast, and blogger. Totally in love with animals, swimming, music, books, gadgets, and writing about technology. Email: Website: Facebook: Linkedin: Twitter:


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