How to select the best data annotation company
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If you’ve ever built a machine learning algorithm, you’ll know that gathering labeled datasets is a tremendous undertaking. Trying to conduct data annotation in-house only distracts teams from what they do best: building a strong AI.
Outsourcing data annotation services is a proven way for teams to boost productivity, decrease development time and stay ahead of the competition. Individuals, researchers, companies and governments are increasingly turning to data annotation companies as a viable solution to obtain both crowdsourced annotators and off-the-shelf annotation tools.
As the number of AI training data service providers grows, how do you decide which to trust? Below, we outline key steps to selecting the best data annotation company.
Step 1: Define your goals
What exactly are you looking for from an annotation company? A statement of work is a key document that states goals and expectations in terms of deliverables. Essentially, it outlines what is expected of your outsourcing partner.
Factors like project workflow, scalability requirements, and delivery commitments are typically included in a statement of work. You should also include information regarding payment, quality, and customer support. It’s important to clearly define your standards of quality and ensure that your expectations are crystal clear.
Step 2: Evaluate multiple data annotation companies
The right data annotation company depends on your project specifications. Listed below are several factors to take into consideration when researching potential service providers:
Every company strives to prove it has enough experience in their field. Client logos, testimonials, and case studies allow you to get a closer look into the client’s background, solutions and results. News, press releases, and recent blog articles can also give you an idea of their general knowledge of the industry.
After requesting materials from a salesperson or exploring the company website, consider the following questions:
- Does the vendor have an established track record of successful projects?
- Have they worked with similar types of data?
- Have they worked with other companies within your industry?
Tools & technology
One of the key benefits of working with a data annotation company is access to pre-built data annotation tools. This eases the pressure on your engineering team to create in-house tools from scratch. The company’s technology should optimize the data annotation process, saving you both time and money.
The best annotation tools are user friendly, minimize human involvement, and maximize efficiency while maintaining data quality. The platform should offer multiple functions, support a broad range of project types, and have built-in features for project management and automation.
Confidentiality is a major concern when outsourcing data labeling to a third-party. If you require secure annotation services, have a discussion with the company’s IT team to learn about their security protocols for sensitive data. Furthermore, if you require ad hoc secure facilities or on-site workers, verify whether the company is able to support those services as well.
Consider the following questions:
- Does the company have signed confidentiality agreements with their network of annotators?
- What security measures does the company take to protect your data?
The performance of your AI model is determined by data quality. Before deciding to partner with any company, ask them what kinds of quality control mechanisms they have in place to ensure the quality of the end product. This is especially critical for cases in which annotators require specialized knowledge or domain expertise.
Another critical aspect of quality deals with their crowd. How does the company source and qualify workers on their platform? It is important to find managed workforce providers that can provide trained workers with extensive experience in labeling tasks. Better annotators lead to more accurate data.
As a client, it’s helpful to know how much you can expect to pay for a product or service. Especially if you have any budget constrictions, a general idea of the price per task is required before proceeding with any annotation company. However, we recommend that you choose an agency that refrains from quoting a price before they’ve had the chance to review your data, as price can vary widely depending on the service or data type.
The best data annotation companies focus on your team’s return on investment (ROI). Hire a vendor that can honestly evaluate your project, the cost, and potential solutions in terms of ROI over time.
Step 3: Request a proof-of-concept
After you’ve narrowed down your choices, conduct a pilot project before jumping in head first. A pilot project is a bite-sized task that resembles the larger project. Getting sample data helps in cases when a project is either complicated, or if you are not sure about the vendor’s capability to deliver. This allows you to tweak project parameters or guidelines before processing the full set of production data.
There should be very strict timelines for the proof-of-concept. The vendor’s delivery record and performance in a pilot project will play a big part in the overall decision making process.
Step 4: Iterate and scale
Depending on how the pilot project goes, you’ll know whether to entrust them with more annotation work or go back to the drawing board.
Your decision will determine your project’s success as you design, test, validate, and deploy your model. As you work more and more with your partner, make sure the annotation process is progressive and iterative. A proactive partner will work closely with you to improve project design over time, making sure the tools, workforce, and process provide the agility and flexibility you need to innovate.
The sheer number of data service providers won’t intimidate you if you have clear goals and a general understanding of what you need to achieve them. Treat the partner selection process with care and respect. The data annotation company you choose can mean the difference between a long and profitable business relationship and an operational nightmare.