Manual vs. Automated Data Annotation: Which Is Better?

The AI revolution is here and surging in popularity due to the many advantages it offers to businesses. While there may be some existing computer vision models in the market, the general agreement is that custom ones are best suited for individual businesses.

You need to opt for data labeling to gain custom models, either through an in-house team or by outsourcing data annotation services to third-party agents. But that is easier said than done due to the complex nature of the task and determining whether manual or automated annotation will work better.

This article demonstrates what goes into performing data annotation manually and how it stacks up against its automated counterpart. It is intended to help you decide the best course of action when developing AI models for your business’s various needs.

Manual Data Annotation

Getting data annotated by experts is an inevitable option for any business. They know the procedure thoroughly and can gauge what data to tag for the model you need to develop. But it’s not all smooth sailing for manual data annotation. The pros and cons are listed below for a more in-depth understanding.

The Pros

It Is Meticulous

Data annotation experts are well-versed in the particulars of their work. They come with years of training and experience that cannot be matched by an algorithm. With this skill level, they can probe the sample/training data for the minutest details relevant to the development and single it out for labeling. This sort of awareness is lacking in automated annotation as the algorithm only repeats what it knows to do.

It Is Adaptable

Business objectives can be fickle, changing on a whim based on various influential factors like market demands, internal company policy, and new business models. When this happens, the annotation project will likely have to change course to match. This detouring is not something that automated annotation can achieve with ease. Unless you have such an algorithm already present, you’ll be out of luck.

The manual annotation doesn’t struggle with such issues. Whenever there is a sudden change in annotation requirements, annotators can quickly learn the new requirements and adapt their methods accordingly. If you struggle to achieve this with your in-house staff, you can easily outsource data annotation services to a professional agency since they can even swap specialists in the team handling your project.

The quick change enables you to keep up with market demands and not lose too much money or time, two of the biggest victims of company restructuring. Your time-to-market will remain manageable, helping you not lose much market standing.

Niche No Bar

Following the previous point, the demand for annotation may be in a niche new to the industry. Perhaps it is a research project that your company is working on, and it requires an AI model for certain tasks. And the available annotation procedures won’t deliver those requirements. In such moments, an automated annotation algorithm will fail, and you must rely on human annotators to complete the job.

Due to their ability to adapt quickly to new demands and learn new skills that they are unfamiliar with, manual annotators can easily complete the project. Their adaptability also gives you room to make changes on the fly, which is useful when venturing into uncharted territory.

An example of this is the early days of image and video annotation when there weren’t set methods to perform it. The niche was turned into a commonplace annotation process by manual annotators who were experts, and many availed their professional image annotation services for various projects.

Easy Ownership Claims

While a lot of annotation relies on open-source data, there are also instances when it requires exclusive enterprise data. If an open-source algorithm is used for annotation in such instances, especially with a mix of open-source and proprietary data, it may be difficult to claim full rights over the output. It may even be difficult to claim the same for the models developed. With manual annotation, you can bypass it all and conduct exclusive annotation, which eases your claim to the rights of that data and even the models.

The Cons

It Is Slow

While experts in the data annotation field can quickly label data, they are still human. This means they will always be slower than an algorithm that can perform the same task. This lack of a fast pace may drastically affect the performance of image annotation companies that take up labeling tasks in bulk.

Further, it can hinder the progress of the project and the company at large. In extreme cases, it can make an effort worthless as the window of opportunity will be closed when the project is finished. This problem is exacerbated when the project is large with many data samples. It can also become impractical when the samples go into the thousands and beyond in number.

It Is Expensive

The subscription charges for an annotation algorithm will be lower than what you have to pay to hire and maintain a data annotation expert. Outsourcing can mitigate some of this burden as the agencies prioritize cost-effectiveness. But it still doesn’t match the cost savings you’d experience via the automation route. Then there are additional costs like equipment, office space, supplementary income payment, and training-related expenses.

Lapse in Data Security

There is always the possibility of data harm when there are people involved. It could be due to negligence in following appropriate data security and privacy protocols or from purposeful actions toward that goal. Data leaks and breaches can severely impact your company, especially if the data is sensitive. The problem can worsen when you outsource data annotation services since you’ll share sensitive information with third-party agents.

It Is Error Prone

“We are all human” is a common phrase used when a mistake or error occurs due to the failings of being human. The phrase also applies to data annotation. No matter their experience, support system, ease of work, or other influential factors, annotation experts are prone to making errors.

The problem is worsened when issues like high workloads and various workplace stress factors are at play. This is something that automation will never experience.

Automated Data Annotation

Despite this being a relatively new field, sufficient progress has already been made in developing algorithms that can perform annotation without human intervention.

An example is Deep Learning, where a hierarchy of ML algorithms is created to annotate data easily using automation. The output of one layer of the algorithm acts as the input to the proceeding layer. This increases accuracy while improving outcomes on many factors.

There are more advantages to automating your annotation project, along with some drawbacks too.

The Pros

It Is Fast

Speed is a characteristic of computation using machines, and annotation is no different. It is one of the biggest factors driving the adoption of automation. This is the primary reason why image annotation companies prefer automated annotation. Image datasets contain a large number of samples, making automation of annotation an inevitability. It is very helpful for companies with quick time-to-market requirements.

It Is Cheap

You don’t have to pay for an algorithm every time it performs a task or by the hour like you do a human. You may have to only pay a subscription charge for using it if it’s made by another company, but it still pales compared to the amount you’d have to spend to get equivalent performance from human annotators.

It Is Reliable

Automation is synonymous with the consistency of output, so you can rest assured that your project will turn out as you intended. There won’t be an issue of it trying to add new inputs to the project the way people do with their suggestions. It won’t make mistakes and cause unwanted problems as a consequence. There won’t be any variations in the output regardless of its demand.

High Data Security

If an annotation algorithm is programmed with the best data security and privacy measures available, then you don’t have to worry about it leaking your sensitive data anywhere. This outstrips any contract-based data security norms provided by data annotation service agencies that you could hire.

The Cons

It Is Rigid

An algorithm is trained to do specific tasks well, so it can quickly find itself out of depth if requirements stray from those specifics. It cannot be altered easily to adapt to the new demands placed on it, which could turn it into a dead investment if your data annotation project’s criteria change.

It Is Imperfect

Being that ML is still an immature creation, any algorithm that is used for data annotation can struggle to accurately do the job. False labeling is a common occurrence in annotation via automation, especially if the data samples are not clear and the subject is beyond the algorithm’s scope of operation. You will find this happening often when an algorithm renders annotation on unfamiliar and unclear data samples.

It Makes Ownership Claims Difficult

Some open-source annotation algorithms come with the clause that any output from them should also be treated as open-sourced material. This can be a problem when you have sensitive data that needs protection.


The world is quickly transforming with the use of AI, and your business will get left behind if it doesn’t also involve itself in this transformation. Data annotation is the way to achieve this peer parity, with custom models being developed that can fulfil your need. Data annotation provided by both professionals and algorithms is the best way forward, as it helps give you the best of both worlds. With that advantage, you can surge ahead in the market and keep up with technological advances.

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