As artificial intelligence is becoming increasingly commonplace daily, handling data is a more pressing issue than ever. Yet, robust data practices are only half the battle. You want your data to be properly annotated to succeed in AI and develop viable projects. By professionals, if possible.

The data labeling market is steadily expanding, with more companies occupying this young niche. It’s expected that between 2022 and 2030, the data annotation market will increase at a CAGR of 26.6%. Thus, the choice of data labeling companies is wide enough, but how do you find the most reliable partner to whom you can trust your data?

The Soaring Demand for Labeled Data

Modern businesses increasingly favor artificial intelligence, including machine learning and deep learning techniques. The fundamental cause of this is the automation of business processes and production. According to the CNBC TEC survey, 91% of tech leaders consider machine learning the most fundamental asset to their company’s success. And that makes sense.

Along with the automation of business processes, machine learning enables predictive analytics and many other solutions that make our lives easier. However, each solution requires well-annotated data, as it helps the ML models work. Each model is fed with labeled data since this is the only way for machines to understand it and produce accurate predictions. Many companies can provide you with high-quality annotated datasets like, for example, Label Your Data. Is this the only way to annotate your data, though?

Usually, there are three ways to get annotated data:

• Creating an in-house labeling team;
• Using a data annotation platform;
• Outsourcing data labeling to third parties.

In this article, we’ll cover the most popular option: outsourcing a data annotation service. We’ll explain why this is the best way to get professionally annotated datasets and share tips on choosing the best company in the market.

The Key Steps Before Contacting a Data Labeling Partner

If you’ve decided that contracting out data annotation is a smart move for your business, it’s time to select the top service. However, you must take a few essential steps to determine your best option before contacting your outsourcing partner.

First, define your project goals and key objectives. Then, create a precise and thorough requirement that outlines every one of your final outcome goals. We recommend including the project’s timetable, budget, and general scope of annotation work.

A decent set of requirements should include:

• Data type and volume;
• Data annotation method(s);
• The need for data collection;
• The level of expertise required to label your data;
• The accuracy rate of annotations;
• Project deadlines;
• The budget limit for the project.

After you’ve taken care of the fundamental project requirements, you should evaluate the vendors you’ll be entering a contract with. Data annotation is still an evolving market, so searching for your best bet is somehow challenging.

Pro tip: See if the company has a social media presence to understand its scale and level of experience. Each provider likely has internal tools and systems. Check what they are and ask about their quality control system.

This step is as crucial as studying your project needs. When a third-party provider takes on the labeling task and works with your sensitive data, you don’t want to waste much time and money on that. The latest thing you want is a dataset that will perform poorly once fed into your model. Thus, we advise evaluating each potential company according to its expertise, quality, performance, security, and teamwork.

With that said, let’s talk about each of these factors in more detail.

Top 6 Features to Look for in a Data Labeling Partner

Many data labeling companies are fighting for their place in the sun today. They all adhere to the standard data labeling outsourcing process. You begin by defining the work and detailing the project’s requirements. You can then start looking for a reputable service provider and negotiating project contracts afterward. But keep in mind the following features:

• Security

Many businesses are skeptical about outsourcing this task because data annotation is a detailed process and often involves sensitive data. You should first pay attention to security when selecting a labeling partner. Ask about their international certifications or accreditations, like ISO 2700, when assessing the security procedures and policies of the given companies. It’s recognized internationally and can only be obtained through an independent IT system audit to confirm that the business complies with global security requirements. EU GDPR compliance and any other features are additional things to consider.

• Quality

When you contract out your data labeling project, you want access to the most talented annotators possible. The dataset’s quality and the model training process is determined by how accurately the annotator has performed the task. However, this process also requires consistency. By looking at their previous errors, the precision of annotations, and the frequency with which the annotator correctly tags each label, you can better understand the labeling company’s ability to deliver high-quality labeled data.

• Expertise

At first glance, data annotation may appear to be an easy undertaking. However, to complete the task successfully and exactly on a large scale, it takes rigorous attention to the details and a special set of skills. The amount of time that each vendor has been in business, particularly in the field of data annotation, as well as the level of competence of their teams, must be thoroughly understood. You might inquire about their years of experience, the areas they have worked with, and the various annotating methods they employ.

• Technology

We have tools that make every task easier, thanks to the modern tech-driven environment. Data labeling uses the most recent tools to finish tasks rapidly without sacrificing quality. You can either ask the annotators to use your in-house labeling software or just rely on their technologies. For a higher ROI, ensure the data labeling service provider can access all available tools and technology.

• Speed

One of the key benefits of outsourcing is the ability to focus on more important tasks and operations by saving time. Therefore, when searching for your data labeling partner, speed is fundamental. As a client, you expect all your data to be processed and labeled within a specific period. If you want your data to be annotated quickly, contact each potential vendor and discuss how much work can be completed in the required time. Also, ask about the quality control procedures if they give you a surprisingly short time for annotation. It’s always quality over speed.

• Pricing

It’s a well-known fact that labor is cheaper abroad. But, you also have fewer overhead expenses if you outsource a data annotation project. These are office space rentals, equipment purchases, and other expenses. If, following the test run, multiple providers can meet the needs of your project, it makes sense to select the one with the lowest price.

Bonus tip!

Sometimes, data labeling companies offer their clients a free pilot project. This way, they demonstrate their expertise and make a good showing. You can find one. For clients, it’s the best way to test the company’s work in practice and make important decisions about further cooperation. So grab your chance if you come across a company that offers a pilot project.

As you finally find your data annotation partner, worry no more! Their job is to find and train qualified annotators who will work on your labeling project. However, always be mindful of the quality of the labeled datasets.

Final Thoughts on Choosing a Data Annotation Partner

Each data annotation company offers different features and charges various costs. Therefore, it is necessary to thoroughly consider them. The top data annotation companies stand out from the competition thanks to a few distinctive characteristics.

When selecting your potential vendor, remember people, technology, and security. Because these are the core values and benchmarks of any data-related work in AI.