In the field of artificial intelligence, statistics show that the services provided by data labeling companies are continuously growing at 28.4% and will reach an estimated growth rate of $3.5 billion revenue in 2026. North America accounted for the most expansive growth area for data labeling in 2020 at 38%. However, the Asia Pacific region is expected to reach the highest CAGR for this period due to the number of smartphone users and increased technological developments, both in terms of actual devices and in terms of social media networks.
Up to 80% of AI project time is used on data labeling in response to the volume of data generated by businesses. Since algorithms require accuracy to study complex behavioral patterns and make human-based decisions, a lot of time is spent perfecting these models.
The quality of your AI model is only as good as your training dataset. Having properly labeled data allows machine learning to understand and respond to consumer decisions more accurately. Investing in outsourcing companies that offer data labeling services helps companies who are heavily dependent on data output to become more efficient and organized. This provides a designated overseer of the annotation process.
Related: Human-in-the-Loop Machine Learning: How Humans Keep AI Models in Check
If you have a need for a large volume of annotated data that requires advanced machine learning algorithms, you can either build an in-house team or outsource a data labeling company.
How data labeling companies support AI development
Data labeling is a critical stage of AI development, as models require structured training datasets to learn from. Whether you need data for computer vision or natural language processing, labeling large scale data requires operational experience and close attention to detail.
Data labeling companies reduce the burden on teams looking to build AI models by taking care of this step. Outsourcing to a data labeling company allows engineering teams to focus on core functions such as research, development, and analysis. Many firms rely on these companies to get annotation projects done on time and within budget.
Not only this, data labeling companies offer quick turnaround and high accuracy when it comes to handling complex projects, such as when a high volume of data needs to be labeled in a short period of time.
Benefits of Outsourcing Data Labeling
Building your own data labeling team in-house can help you oversee your labeling processes and data security. Security is one of the top concerns of many organizations, given the amount of sensitive information transmitted online every day.
However, building an in-house team is a huge undertaking because it is expensive to implement the needed technology, people, and processes. Not only that, it is also time-consuming and difficult to scale. Data labeling outsourcing enables cost and time savings while delivering impeccable precision and scalability across AI projects of all magnitudes.
Let's delve deeper into the advantages and significance of data labeling outsourcing:
Cost-Effectiveness
Reduce costs associated with hiring, training an in-house team, and providing infrastructure and workspace.
Access to Expertise
Outsourcing data labeling provides access to experienced professionals with technical expertise relevant to project needs.
Scalability
When project needs change, an outsourcing partner can easily adapt resources, tools, and technology.
Data Security
Improve security via third-party vendors who have proper protocols and certifications for sensitive data.
Quality assurance
With robust quality control processes in place, your data is in better hands with an experienced service provider.
Global Talent Pool
You can get access to a diverse talent pool from around the world that can be valuable for labeling tasks that require global resources.
Updated tools & technology
Outsourcing data needs can provide you access to state-of-the-art labeling tools, avoiding expensive internal investments.
Choosing the Right Partner
Now that you understand the advantages and disadvantages of outsourcing your data labeling, don’t rush to reach out to different vendors just yet. Instead, read this step-by-step guide to help you choose the right partner for your project.
Step 1: Understand your project requirements
There are a lot of data labeling outsourcing companies to choose from, which can be overwhelming at times. Thus, it is essential to set your expectations and desired output to avoid disappointment.
First, you will need to create a Request for Proposal (RFP) for your target outsourcing companies to better understand their service offerings and capabilities. By taking the time to fully scope your project’s needs, your team can clearly state your project objectives, timelines, quality metrics, and other key requirements for potential partners.
Here are some of the questions that can guide your team on what to include in the proposal request:
- What type of data are you working with? (file format, type of annotation service, languages if applicable, among others)
- How much data do you need?
- Will any special domain knowledge be required to label your data?
- What is the objective of this training data?
- What are your data quality requirements?
Step 2: Evaluate a Shortlist of Data Labeling Companies
After defining your project goals and particulars, the next important step to consider is to evaluate data labeling providers. Below are the suggested requirements to take into consideration when crowdsourcing data labeling companies:
Technology
Proper tooling software is necessary to execute data labeling tasks quickly and at scale. You can provide your existing software for annotators to work with or rely on third-party tooling to prepare training data. This is why it is essential to look into the tech capabilities of each potential outsourcing company as they will be able to advise on the proper software tools to help drive ROI in the long run.
Given the standards of your business, the suggested factors to consider when choosing software are its features, flexibility, built-in quality control, collaboration features, and affordability.
Proper tooling software is necessary to execute data labeling tasks quickly and at scale.
Quality
Quality assurance is a critical component of outsourcing your data labeling. To ensure all of your expectations will be met, you must make sure that workers are knowledgeable, well-trained, and properly integrated in the domain that your data services.
Hire employees that can prove that your data is in good hands. They must be able to respond quickly and flexibly to your demands in workflow changes, be transparent, and properly communicate with you through a closed feedback loop. Direct communication with your data labeling team will allow you to get firsthand insights and suggestions from the people working on your data.
Hire employees that can prove that your data is in good hands.
Experience
When hiring, it is essential to be aware of applicants' credibility and background in the data labeling services industry. Aside from conducting a background check on the company and verifying their experience with data labeling, ask for the company’s previous projects, security certifications, domain expertise, and even the types of languages that they support.
Many businesses underestimate the needed expertise or skill in providing data labeling services because they think this is a simple task. However, this skill requires accuracy and a great amount of attention to detail to avoid human error—a common mistake that could accumulate and lead to severe consequences in the long run. Inexperienced vendors may even cause costly delays since they lack the resource quality and appropriate tools needed to label your data properly.
Data Security
Large amounts of data that need to be labeled are given to outsourcing companies via third-party software. This means you must trust your provider to maintain a safe environment that is free from data security breaches. It is important to find a company that values data protection since systems with poor encryption protocols are prone to hackers.
However, keep in mind that your data is within your control and that you have the choice to decide on who to make it accessible to. It is crucial to perform a background check on the in-person team handling your data since most data breaches are due to human error. It is also recommended to let each worker sign an NDA and other security compliance forms that guarantee data safety.
You must trust your provider to maintain a safe environment.
Ethical Considerations
The importance of diversity and inclusion is essential to providing equal opportunities for small companies to grow. By considering a potential partner’s culture and how they embrace an inclusive, working environment, it promotes a diverse representation in machine learning that makes your AI model more unbiased and ethical.
Human interaction is a key factor in annotating ML tools since this requires skill and extensive training. Many data labeling companies are notorious for underpaying workers despite their vital yet stressful responsibility. It is important to consider humanization and labor laws upon hiring an outsourcer. Doing a background check on the company’s ability to follow ethical treatment of workers is important to avoid any future problems.
Inexperienced vendors may even cause costly delays since they lack the resource quality and appropriate tools needed to label your data properly.
Data Labeling Services by TaskUs
Still have questions? We’ve got you covered!
TaskUs offers a wide range of data labeling services to help you build better-performing machine learning models. We have been a trusted partner of some of the global brands and fastest-growing companies. We have more than 10 years of experience in data labeling and we support 120+ clients powered by human-annotated training data.
Our subject matter experts will align with you to understand your data needs and model development. We will set up the tooling environment, quality control mechanisms, testing and training protocols, and the timelines and milestones.
What makes Us exceptional?
- We have a People-First culture. Above anything else, the welfare of our people is our priority.
- We custom-build our teams and ensure gold standard processes.
- Our average QA score in all data-related operations is greater than 98%.
- Our Ridiculously Good Teammates proactively monitor the quality of data and calibrate the annotation process, if necessary.
- We are PCI, SOC II, and ISO-certified and HIPAA, GDPR-compliant.
- We have LDAP and SSO access protocols.
- We are a member of the Vendor Security Alliance.
Learn more about TaskUs data labeling capabilities and how we provide high-quality data for your AI and machine learning.
