Introduction
Data labelling companies can make or break your AI project. When it comes to precise outputs and results, the quality of datasets doesn’t matter. The data annotation you use to train your AI modules have a significant impact on your outcomes.
That’s why it’s crucial to choose and use the most functional and appropriate data labelling company for your business or project. What are the best qualities a good data labeling company should have? Here is a list of the top 5 qualities to check out before finalizing a data labelling company.
1) Ability of workforce management:
Tools and a project management platform are essential for a data labelling company because these tools shall integrate with your workflow and process to maximize your productivity and effectiveness. Furthermore, the tool must have a minimal learning curve, as data annotation is a time-consuming process in and of itself.
Time spent learning a tool is in vain and should be avoided at all costs. For this reason, it should be easy for anyone to get started. It also defines your vendor annotation team abilities as it’s the tool that shall define their capabilities because they are the ones who shall execute your project.
2) Expertise and experiences:
While data labelling may appear to be a simple task, it requires a high level of attention to detail and a unique set of skills to execute on a large scale efficiently and accurately. It would be best if you learned how long each company has been working specifically in the data annotation space, as well as how experienced their annotators are.
You can assess this by asking the vendor about their years of experience, the domains they’ve worked in, and the types of annotations they’ve worked with. Consider the following scenario as you may proceed:
● How many years of data annotation experience do the vendors have?
● Have they ever worked on a project that required specialized domain knowledge?
3) Quality Assessment
Data scientists frequently use the precision with which the labels are placed to determine the quality of datasets for model training. It is not enough to label correctly one or two times; accurate labelling must be done consistently. You can determine whether or not a company is capable of providing high-quality labelled data by looking at:
● Their previous annotation projects error rates.
● How well were the labels placed?
● How many times did the annotator tag each label correctly?
4) Data Safety checks
As you’re working with data, safety should be a top priority. Your work may involve working with sensitive information, such as personal information or intellectual property. As a result, your tool must provide impenetrable security in terms of data storage and distribution. As a result, the labelling company you hire must provide tools that limit access to team members, prevent unauthorized downloads, and more. Apart from that, security standards and protocols must be followed strictly.
5) Teamwork
Understanding the capabilities of your vendor annotation team is critical because they are the ones who will be directly responsible for the project’s execution. The vendor should guarantee that you will receive a well-trained team.
Furthermore, if you want to label text, you must determine whether or not the labelling team is fluent in the language. Also, check with the data labelling company to see if they’re willing to scale up or down the annotation team in a hurry. Even if you estimate the amount of data to be labelled, the size of your project may change over time.
Wrap up!
Keep in mind to provide a detailed guideline for the demo so that you can properly evaluate the data labelling company. Finally, inquire about how you can monitor the demo test’s progress. As a result, you’ll be able to find the most suitable partner.