Before You Outsource: Know The Data Annotation Outsourcing Pros and Cons

Learn the pros and cons of outsourcing data annotation, plus qualities in choosing the best partner for your next AI/ML project.

Published on November 26, 2022
Last Updated on August 1, 2023

Artificial intelligence (AI) and machine learning (ML) models are unable to function without data; however, simply feeding them with large amounts of raw data won't work. Data annotation is a critical step in training and building AI and ML models.1 Without diverse and accurately processed data, AI and ML models simply cannot function, much less function well.

That's not all. Even seemingly minor errors during annotation could cause the ML model to make false predictions and impact the performance of the algorithm.2 This is why many industries are now relying on experienced data annotation outsourcing companies and service providers to manage their data needs.

Data annotation outsourcing can be daunting. After all, outsourcing is a rigorous process, especially for those doing it the first time. Let’s dive in to learn more about data annotation and the pros and cons of outsourcing data annotation services. 

What are data annotation services?

Data annotation is identifying and labeling relevant data to train machine learning models. Data annotation services, typically performed by data annotation companies, include tagging for various formats of data such as image, video, and text. 

With the rise of AI, data annotation services have become indispensable for numerous global industries such as healthcare, autonomous vehicles, retail, and consumer technology, to create precise and error-free training data sets for various computer vision and natural language processing models. 

Different types of data annotation services

Many types of data annotation services are available for different data formats depending on the bespoke and particular needs of the machine learning model and the use case:

Annotating training data for ML models filters out data that may impact the quality of the output. Only high-quality data should be used to train AI and ML models, especially within the realm of retail and eCommerce.

Pros and cons of outsourcing data annotation services?

Nowadays, many companies outsource their data needs to third-party vendors (including dedicated data annotation companies) who specialize in customizing data annotation services for projects of all levels and complexities. 

Here are some pros and cons of outsourcing data annotation services to an experienced service provider:

Pros Cons
  • Experienced annotators
    Data annotation outsourcing partners are experienced annotators. They have the tools, knowledge, and skill sets that you need to ensure the quality and consistency of your data. 
  • Reduced costs
    Data annotation outsourcing can help you save money. This is because you'll be able to hire someone who already has a team, tools, and skills with the systems and processes of data annotation, which means you will only pay for their services. 
  • Professional tools and software
    Partnering with a data annotation company means also benefiting from the professional tools and software they are equipped with—ensuring the quality and efficiency of the annotation process.
  • Scalability as per project needs
    Not all annotation projects are for the long term. By outsourcing data annotation services, it will be easy for you to scale up or down your staffing requirements based on your data annotation projects.
  • Strict quality controls and data security measures
    When you outsource data annotation, you'll be working with people who take security seriously and have systems in place to protect your data, information, and business.
  • Lesser control
    Data annotation outsourcing companies manage their teams and will only communicate with you through a representative. You will not be able to see or monitor the work of your data annotation team, and you will only be notified of results.
  • Impact on internal teams
    Even in sectors as big as outsourcing, the industry will still face significant pushback. In this case, some internal employees might feel demotivated and underappreciated when they hear that the company they work for started to outsource projects. They might feel that outsourcing will take them out of their positions.3

    It’s essential to address these issues to ensure that the workplace values and welcomes change. A positive work environment starts with happy and secure employees.

It’s essential to address these issues to ensure that the workplace values and welcomes change. A positive work environment starts with happy and secure employees.

As the use of AI continues to expand across industries, the demand for data annotation services will soar in areas like national security, R&D, manufacturing, and energy and utilities. Currently, manual data labeling dominates annotation services, but its costliness and scalability challenges are pushing the industry towards automated annotation. It is projected that automated annotation will grow at an impressive 18% CAGR by 2030, streamlining labeling processes and boosting ML model efficiency.

Furthermore, data annotation services will focus on multimodal data annotation to cater to the increasing complexity of AI models. To ensure data quality, companies will adopt human-in-the-loop (HITL) approaches, combining human expertise with AI automation to validate and refine annotations for greater accuracy and consistency.

Tips for outsourcing data annotation services

Finding the perfect data annotation service provider requires in-depth research and thorough consideration. . You have to be meticulous and consider several  factors before making a decision.

Here are some tips to keep in mind when choosing the right data annotation partner:

  • Industry experience: Look for a service provider that has an experienced and scalable workforce related to your data annotation projects.
  • Scope of work: Always define work clearly when signing a contract with the vendor to ensure you get the results required for your project.
  • Cost-effectiveness: One of the major factors to consider outsourcing is to reduce costs, therefore, you should evaluate the vendors based on what they are bringing to the table for the incurred costs.
  • Flexibility: Project needs usually change over time, so the service provider should be able to scale up or down based on the changing requirements. 
  • Regular monitoring: Your partner should provide regular updates and timely deliveries so that you can monitor the quality of the tagged data and request changes when needed.4

While numerous third-party vendors are experts in data annotation, not all are suitable for your needs. Finding your best partner isn't always about the lowest cost or what other benefits they can offer. It's all about the value they add to your project and your company as a whole.

Outsource Data Annotation Services to TaskUs

As the world's fastest-growing BPO company, we pride ourselves on delivering Ridiculously Good AI services to our clients and partners—all thanks to our dedicated and highly skilled workforce.

We have a pool of 48,700 full-time Teammates and over 70,000 Taskers in our crowdsourcing platform, TaskVerse, who work with Us to create and perform AI and ML solutions specifically designed to cater to our clients’ needs. 

From natural language processing to computer vision, we comply with gold-standard processes and data security measures. We've worked hard over the years to create processes that ensure the highest standards for both security and accuracy. Quality is the name of our game.

One of our clients called Us to provide our world-class data annotation services—specifically object mapping—in furthering the development of self-driving autonomous vehicles, making them safer for pedestrians, passengers, and drivers alike.

Through our partnership, these goals were met:

Interested in learning more about our data annotation services?


Haruka Kimura
Senior Manager of Sales, AI Services
Haruka has over 10 years experience helping local and international companies expand their presence in the Japan market in the advertizing, localization, and AI data spaces. At TaskUs, she is focused on expanding AI data services to Japanese customers.