The High Cost Of Working With Cheap Data Annotation Partners

Artificial Intelligence and machine learning is revamping the way technology is being utilized to aid an increasing range of real-world problems. Obviously, it is important that these solutions consistently deliver accurate, logical, and useful outcomes. Companies that work on AI/ML products and applications know this needs crucial training of their solutions in the collection of many datasets, categorizing a large amount of data and using it appropriately.

While building these solutions or while preparing to use such smart solutions, companies do not want to spend hours arranging, segregating, and ordering the data to make it usable. Neither is it acceptable to them that the AI be built on an inadequate foundation that could deliver inappropriate results.  

This is exactly why data annotation is seen as a critically important foundation for such solutions. Getting this foundation wrong isn’t even an option.

Data annotation helps in comprehensively identifying, categorizing, labeling, and organizing the data captured in various formats including images, videos, and text. This is used to “train” the AI or ML algorithms on an ongoing basis. Data annotation ushers in the required accuracy to the algorithm and improves end-user outcomes.

This is often an extremely detail-focused, unavoidably manual, rigorous, and time-consuming process. Understandably, many companies don’t want to invest in creating in-house data annotation teams. They want to focus on their core product/app development. In this scenario, partnering with the right data annotation partner is the best option.

When the decision of choosing the right data annotation partner is to be made, the options can become overwhelming. There are many factors to consider. There’s a choice to be made between crowdsourced annotators and professional implementation partners. An (unfortunately) common choice companies make is to go with a cheaper option. These are crowdsourced solutions, off-the-shelf annotation tools, and small partners. The argument being it’s expensive enough building the AI product, why not save a bit when you can? But here’s why that isn’t a great option. In fact, the cheaper choice usually turns out to be expensive in the long run.  

Why a cheap data annotation partner is expensive in the long run -

1.   Non-productive hours calculated instead of actual productive hours

The most obvious parameter impacting the overall cost in data annotation projects is the number of hours that the partner team invests in the data annotation tasks. An inexperienced partner may have lower per hour rates but will almost certainly spend many more hours on getting the project delivered at the required quality. Even a lower per hour rate spread over a significantly larger number of hours will make the project cost balloon out of control very fast. The key would be to focus on the productive hours when the actual results were delivered. With professional, reputed, and experienced vendors, you end up paying only for what really matters to you.

 Of course, the hours discussion also has another facet. This suggests that when you evaluate the experience of a vendor, it’s important to differentiate between the productive hours and the actual hours that are put in. The cheaper data annotation might be claiming more hours but that doesn’t equate to the performance you can bet your product’s future on.

 In the context of the crowdsourced platforms, they might occasionally have access to freelance talent, but the delivery process is latent with a built-in lag, again impacting the productive hours put in.

2.   Low-quality control

 When you choose a professional vendor for your data annotation needs, you are assigned a team that is available to you 24/7, processes are established, communications are smoother, and the handovers are cleaner. Setting the standards for accuracy and quality control is easy, and the partners can be held accountable for what they deliver. Your product, application, or solution gets the data foundation that is essential to its build quality. There is less rework, translating into hours saved. You can go-to-market faster and your solution can start delivering value immediately. That’s all cash lost when you choose to work with a less capable vendor. 

 3.   Data security and compliance

 When the data annotation tasks are delivered by smaller companies, ensuring the security of the data is next to impossible. It’s no secret that many of these companies can only afford to offer low rates by cutting corners. They take risks, share resources with other companies, skip mandatory government registrations, compliances and mandates, and avoid costly investments in areas like securing the labs with physical and digital security, employee training or reference checks. And of course, these data annotation companies have access to large amounts of your data that is essential to keep confidential. No need to state the obvious that should a data breach occur, the costs of the legal implications or data recovery will be huge.

4.   No processes

Any company that is involved in AI and ML related products also makes use of modern project management tools and has formal processes to track project progress, maintain quality control, and also has multiple checks for security and compliance. With the cheaper data annotation partners or the crowdsourced option, there’s often a clear lack of processes. Something as critical as having all the workers involved to sign the non-disclosure agreements is often absent. This makes it hard for you to work with them (your effort = money) and delays project performance (more hours = money). That apart, scaling the project when your requirement changes is incredibly complex and requires a lot of investment of time and resources, adding to your overall costs.

5.   No formal training leading to poor accuracy

The cheaper data annotation partners hire whomever they can. Apart from a handful of people, the others have little to no formal training for the actual tasks. Or to put it another way, they learn while working on your project so you pay for their training! It is well-known that for specialized projects, the data annotation needs to be executed by domain experts and workers with specialized knowledge. A lack of expertise inevitably leads to poor accuracy with the end-result. With the quality control already being inadequate, the overall accuracy is heavily impacted. Is it even necessary to repeat that accurately annotated data is critical for your product or application to be successful and having inaccurate data annotation basically means you’ve to spend a lot of time and resources in fixing the errors?

 The market for data annotation outsourcing is attractive and there are several partners available to do the job cheaply. But you know that there’s no free lunch here. At the end of the day, you want to rest assured that your data is in safe hands, is annotated to perfection and you are creating the right base for your AI projects. Remember “Good work ain’t cheap and cheap work ain’t good”, especially with data annotation.

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