Finding a Data Labeling Partner

Artificial intelligence (AI) is assuming such a central place in the future of business that 75% of executives fear being edged out of the trade—and out of work—if they don’t scale AI adoption.

AI is all about data, and data is the new oil standard. Like oil, data is meaningless unless processed objectively and accurately.

To incorporate AI into your business or scale it, you must get your data labeling right. Mistakes in data labeling, like confusing a child on the road for a shadow, can be very catastrophic for an AI-guided self-driving car, for instance.

Finding a reliable data labeling partner is key to the success of your machine learning tools. Look out for the following key things to look for when considering a data labeling partner.

Type of Data to Be Labelled

Before you do anything, you should know the kind of data you’ll require for your labeling. There are different labeling tools for audio, text, image, and video files.

Additionally, data quality and storage capacity largely depends on the type of data. For instance, the storage capacity of audio data is far much higher than that which is in text format.

Also, different tools are specialized for use with different types of files. The supported file types, quality, storage, and security will differ depending on the data to be labeled.

For this reason, ensure the data labeling partner you’re considering knows the data and file type beforehand. See that they have the necessary tools and experience to handle your data type.

Accuracy of the Data Labeling Partner

Accuracy is not just about the label reflecting the ground truth but the consistency as well.

A label of a computer scan may identify an image as a tumor in 60% of the cases, while in other instances, it can identify the image as a blood clot, swelling, lump, growth, or bump.

If an AI-dependent medical diagnosis is to proceed, it may not be accurate in its conclusions.

So how do you ensure that your data labeling is accurate?

Three important factors.

Your data labelers should be very knowledgeable about your industry. Take the case where a tumor is labeled as a blood clot, for instance. You’ll need knowledgeable labelers who can identify medical results correctly. Otherwise, the inaccuracy might not only cost someone’s life, but you could also face malpractice charges.

Knowledgeable labelers understand the data context, setting, and relevance. Otherwise, for musical audio data, you’ll have “tenor” labeled as “alto.” Your business may be adversely affected by such labeling mistakes.

Excellent communication with the data labeling team. In any quality process, communication is critical. The communication should be prompt and timely. A closed feedback loop, which entails response to feedback, is highly recommended.
Quality control workflows. A well-established and broadly understood workflow ensures the instructions are understood, and the final output is accurate. As part of the quality assurance process, a double annotation process should be incorporated as well. This can significantly improve the quality of data labeling.

Finally, the way you measure quality, whether through sampling, consensus, the gold-standard method, or IoU (Intersection over Union), can be as crucial as ensuring quality.

So, choose what can work for you and vary them as often as you can.

Technological Tools and Proficiency of The Data Labeling Partner

Certain technological tools and features can facilitate the automation of data annotation. Some tools can even infer what you intend to do—just like the autocomplete function in Microsoft Word.

This can be a real game-changer.

If data labeling is automated, the speed of processing can increase up to 500%. Consequently, you’ll be paying less for more output.

There’s one more benefit.

Technological tools can also help boost accuracy. Some use specific AI models to label the data-set tentatively. Workers will therefore shift their focus from data labeling to checking the labeling accuracy suggested by these tools.

Before you engage a data labeling partner, it’s definitely in your best interest to assess their technological strengths.

Inquire into the technological tools they have and how they affect the time, budget, and quality of labeling.

Then pick a company that you’ll think will give you maximum value for your investment.

Simplicity or Complexity of Your Labeling Instructions

If you have straightforward labeling instructions, you may not have a challenge finding a labeling partner.

However, suppose your instructions are a bit complex and even require some level of customization. In that case, you’ll need to look for a labeling partner with sufficient tools and experience to navigate the complexity of your labeling instructions.

If possible, make your labeling instructions as simple as possible so that the actual labeling and annotation won’t be complex. Simplicity increases accuracy.

Other Factors to Consider When Finding a Data Labeling Partner

Social Proof: Is there a customer reviews page that you can look at? You can also look at the star ratings.
Consider reaching out to a past client and request to know their experience. There’s a reason 93% of potential clients read online reviews before deciding to buy a product.

Data Security: You may want to know about the company’s security protocols so you can be sure you’ll not have to experience hacks and data leaks.
Scalability: You’ll want to ensure that your partner can handle increasing labeling demands with respect to volume and complexity.
Humane treatment for the workers: Recently, impact outsourcing has become popular. Companies in need of outsourcing digital work offshore it to low-income areas where workers wouldn’t typically access it.
These areas are usually African countries, refugee camps, slums, or areas hit by economic problems. However, instead of creating sustainable employment, they result in African exploitation.

In addition to accuracy and data quality, ensure the data labeling partner you’re considering provides humane working conditions and proper payment to their workers.

We Can Help You With Your Data Labeling Needs

At a time when companies are heavily investing in machine learning, data labeling solutions are essential as supervised learning algorithms need labeled data.

At Impact Enterprises, we have been providing top-quality, scalable, and customizable data annotation and labeling services for over eight years. We offer a wide range of services to ensure the accuracy and quality of your machine learning tools.

Our services include:

AI data annotation (image and video annotation) and computer vision services
Lead generation and lead research
Content services

Looking for a reliable data labeling partner? Impact Enterprises can help.

This is what our customer said about us:

“Detail oriented, thorough, and fast. The team can be relied on to work independently.” ~ Andy Hathaway, Co-Founder, Avari