5 Questions To Ask Before Getting Started With Data Annotation To Train Your Machine Learning Models

Data Annotation

Data annotation – what is it and why is it important?

They say data is the new gold.

Agreed! 

But not every byte of data generated can be processed for crucial business insights. Similar to how gold goes through a stringent process before it becomes a valuable piece of metal, data must go through a similar procedure as well.

From mining to crushing and processing, data has to undergo a series of systematic steps before it can finally become information. For the uninitiated, unstructured data make up for around 80% of the overall data generated. And for a machine learning algorithm to process and analyze, it has to be converted to a form that is easily identifiable.

This is where data annotation comes in.

“A process to annotate or tag generated data, this allows machine learning and artificial intelligence algorithms to efficiently identify each data type and decide what to learn from it and what to do with it. The more well-defined or labeled each data set is, the better the algorithms can process it for optimized results.”

And as far as data annotation is concerned, each company has its own distinct strategy. While some companies decide to have an in-house team to work on data annotation, others chose to outsource and get them done through experts. 

Let’s understand few fundamentals before getting started with the process. These essentials will help make better use of your machine learning algorithms and give you data sets that are more trainable and smart.

Five Questions To Ask Before You Go Ahead With Data Annotation

Do You Have Data?

Sounds pretty basic, right? But there’s more to it. 

It is only recently that companies and businesses across the globe are waking up to the importance of data analytics. They are gradually realizing that data science can help bring in profits, better outputs, optimized customer experience, and more with respect to their specific domains. With this being a more recent development, several businesses are slowly transitioning from obsolete tech infrastructure to more data-driven models and operations, there is a primary question about whether companies have data in the first place.

If you have had data touch-points earlier, that would be an ideal place to start compiling data. But for those who don’t have such touchpoints, a good solution would be to purchase data sets pertaining to your domain. There are also data sets that are available publicly. Crowdsourcing is a probable solution as well.

What Data Needs To Be Annotated?

Now, data is a very generic term. Consider a voice note or an abandoned cart on an eCommerce website. Both are data. So, before going ahead with the data annotation process, you need to decide what data you intend to annotate. And for that, you need to define your goal and purpose with the data and your agenda with the machine learning algorithm. Though data annotation service providers offer a myriad of services including the annotation of text, videos, audios, semantic data, content categorization, and more, it is you who should define what specific insight you are looking for. Is this to improve the holistic health of your business? Is it to study consumer behavior?

Is There Enough Data In Hand?

The next question is if you have enough data. The volume of data you possess directly influences your algorithm’s output. If your data set isn’t comprehensive enough, the insights generated will not be airtight or accurate because chances are that a lot of relevant scenarios might have been overlooked because of lack of substantial data. 

Machine learning works best when there are massive chunks of data available for processing. For instance, for a machine-learning algorithm to identify the image of a dog autonomously, it is not enough to feed it with the images of dogs alone. It should also be able to recognize what data points do not constitute a dog or what might differentiate a dog from any other similar creature in order to produce airtight results.

How Clean Is Your Data?

Machine learning algorithms are unlike anything human. Instructions have to be spoon-fed to them if we intend to get accurate results. That’s why it becomes extremely important to feed clean data to the algorithms. 

But what is unclean data anyway?

To give you a brief idea, understand that any data which is irrelevant, poorly or incorrectly annotated, incomplete, biased or misleading is considered unclean data.

With this, your algorithms will spew results that are far, far away from your requirements and expectations. Algorithms will learn wrong lessons, execute wrong calculations and deliver wrong results.

Do You Need SMEs For Data Annotation?

This totally depends on how complex your data is. For basic data sets, companies can afford to get annotations done by in-house teams and available resources. But when requirements and data sets become more specific and complex, which is usually necessary, it is recommended to seek out subject-matter experts for data annotation purposes. Streams like legal, computer vision, conversational AI, and healthcare have more complex data sets than what would be generated in retail and entertainment.

Final Thoughts

So, these are five questions that would enrich your data annotation process. More often than not, companies leave it to the professionals when you simply want annotated data at the end of the day, more often than not, companies leave it to the professionals like Shaip.

We have veterans working in our teams who would bring in their expertise and knowledge to deliver the annotated datasets perfect for your machine learning models.

Save time and leave it to us today. Get in touch to get started with the process. 

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Vatsal Ghiya

Vatsal Ghiya

Vatsal Ghiya is a serial entrepreneur with more than 20 years of experience in healthcare AI software and services. He is a CEO and co-founder of Shaip, which enables the on-demand scaling of our platform, processes, and people for companies with the most demanding machine learning and artificial intelligence initiatives.