The Ultimate Guide to Data Labeling Outsourcing in 2024

Machine learning (ML) algorithms help businesses make optimal decisions. For supervised ML algorithms, businesses need labeled data, which are meaningful or informative tags added to raw data samples such as images, audio, and text.

It is possible to perform data labeling activities in-house, outsourced, or crowdsourced, each of which has its own advantages and disadvantages. In this article, we will focus on outsourcing options in depth.

Why is data labeling outsourcing important?

Effective ML models depend on high-quality data. Despite the need for effective data, McKinsey argues that labeling and tagging data is the biggest challenge in building ML models. Therefore, many companies prefer to work with third parties who are experts in the data labeling process. Outsourcing can enable businesses to benefit from their ML models effectively.

What are the pros and cons of outsourcing data labeling?

To understand whether outsourcing the data labeling process is a good strategic move for your company or not, we need to compare it with other common data labeling processes, namely in-house data labeling, and crowdsourced data labeling.

In short, in-house data labeling uses the company’s own data scientists and facilities for data labeling. Crowdsourcing, on the other hand, uses internet users as data labelers. The most famous example of crowdsourced data labeling is reCAPTCHA.

We compare outsourcing with other options on four dimensions:

Time required

Outsourcing data labeling saves companies’ time compared to in-house labeling because training a team and building the necessary facilities for the data labeling process are time-consuming activities. On the other hand, it is slower than crowdsourcing because crowdsourcing allows companies to reach a large number of data labelers thanks to web-based distribution.

Price

A similar pattern exists for time spent. Outsourcing performed better than in-house data labeling because companies invest less in hardware and hire fewer data scientists to focus on the labeling process. On the other hand, outsourcing is a more expensive data labeling solution than crowdsourcing. 

Quality of data labeling

In general, the quality of data labeling is better in outsourcing and in-house data labeling than in crowdsourcing, because specialized data labelers work in these two cases. On the other hand, it is difficult to compare outsourcing and in-house alternatives because outsourcing companies may have different areas of specialization for data labeling. Nevertheless, a company that wants to outsource the data labeling process can find a data labeling vendor that offers a good quality of service.

Security

In terms of security, outsourcing data labeling provides a lower level of security than in-house, but a higher level than crowdsourcing. When a company does the data labeling itself, the data is not shared with third parties. Therefore, this is the most secure labeling strategy for any company. On the other hand, outsourcing companies have certifications and some security controls that reduce the possibility of data misuse compared to crowdsourcing strategies. Since crowdsourcing employees are usually not required to adhere to any security or privacy standards, there is no way to prevent them from sharing your data.

Overall

The following diagram compares and summarize outsourcing, in-house, and crowdsourcing options for data labeling.

Outsourcing data labeling is an appropriate strategy for an organization when dealing with data that is not extremely private and requires high-quality data labeling that promotes an effective ML model. 

How to choose a data labeling outsourcing provider?

If you’ve decided that outsourcing data labeling is a good option for your business, then it’s time to choose the best possible provider. Different providers offer different features and charge different prices. Therefore, it is advisable to evaluate them carefully. We list several points to consider when choosing a vendor:

1. Determine the business need and schedule: The first step is to know why you need data labeling and how much time your business has for the data labeling process. This simple self-knowledge will help your company to eliminate many vendors.
2. Type of data: Different vendors specialize in labeling different types of data such as videos, images, text, and audio. Therefore, it is better to work with a provider who has experience in labeling the type of data your organization is creating.
3. File formats: Make sure your company uses the same file formatting as your chosen provider.
4. Quality and accuracy: It is important to check whether the candidate vendors’ data labeling accuracy is matching with your company’s requirements or not. If they do not meet this criteria, you can remove them from the shortlist.
5. Data security: It is advisable to compare the security certificates of providers. Data security is a sensitive issue and can cost a lot.
6. Test run: Running a trial of the outsourcing service is the best way to determine whether the provider meets your needs or not.
7. Price: After the test run, if there are several vendors that all meet your company’s requirements, it makes sense to select the lowest-priced vendor.

To select the best option that suits your business needs, Check out our sortable/filterable data annotation vendors, services, and tools lists:

• Open-source data labeling platforms
• Data annotation services
• Medical data annotation tools
• Video annotation tools

Further reading

• data labeling
• data annotation
• video annotation
• video annotation tools

If you need further assistance in selecting providers for data labeling, you can contact us.