Why Data Annotation Is Important For Business Datasets

In the middle of your pajama party, you decide to order a pizza. A couple of taps, and here’s the chatbot of the nearest pizza delivery. You are trying to text and dance simultaneously: “need too Margaritas” –– you input, but the robot asks to rephrase your request.

“Two margeerita pizza” –– this time, but it doesn’t understand you. “2 Mar garita please” –– nope. Annoyed, you just go out to call another pizza shop. And the reason is poor data annotation.

What Is Data Annotation?

Let’s start the other way round. Companies collect huge amounts of various data: images, texts, video, or audio files. Those exabytes of information are also called Big Data, and businesses invest thousands of dollars (according to Yahoo! Finance up to $51.56 billion in 2021) to process them with Artificial Intelligence (AI) and Machine Learning (ML) algorithms. This is called data mining, and its outcomes help founders get various business-related insights to grow revenues, decrease costs, enhance customer relationships, etc.

But to get representative results, you have to feed relevant and prepared data to the AI model. In other words –– labeled data. In our example above, the algorithms behind the chatbot were supposed to interpret phrases with Natural Language Processing (NLP), but it was unable to identify words with typos or extra spaces in a phrase. This means that text annotation was poor.

Source: Freepik

Advantages of High-Quality Data Annotation and How It Works

Okay, accurate data annotation is essential for proper ML training and credible predictions. But how exactly does data labeling work? Here are some of the data annotation techniques.

1. Text annotation

Understanding phrases and sentences are easy for humans, but robots can get confused by hidden messages, humor, slang, and various comparisons. There are several types of text labeling. For example, Sentiment Annotation helps to classify “I love you to death” as a positive statement. Text Classification techniques allow tagging text pieces that help find information in a big document quicker. This can be useful in structuring e-docs or product categorization.

The next level of text processing is Entity Annotation, and it’s the crucial technology helping machines understand and analyze texts. AI learns to recognize names of people and companies, understand parts of speech and detect certain words and phrases.

2. Image annotation

Source: Brett Sayles: Pexels

The most common image annotation types are Bounding Boxes –– when the image is captured into a rectangular box. Polygonal Segmentation is a kind of extended application of the previous technique, while Line Annotation is used when the object is too thin for a box mark. A similar approach is used to identify one more parameter –– volume (depth) of the object. So, this method is called 3D Cuboid Labeling.

Annotators use semantic segmentation to identify several areas on one image (like sea, sky, and sand). Landmark Annotation places dots on the image, and when joined, they look like a frame of an object. This technology is used for face recognition too.

Video annotation is pretty similar to image annotation as they both are a part of Computer Vision (CV). CV tries to reproduce human visual perception, and currently, it is one of the most popular fields of AI application.

Why Proper Data Labeling Is Vital for Businesses

Business needs dictate how collected data should be annotated for further learning processes. And the more thoroughly the raw dataset is classified, the more accurate predictions such AI models will make. And, consequently, provide more competitive advantages to companies –– let’s take a look at a few examples.

– Detecting tone in social media posts and customer reviews

Text annotation helps to understand the mood and appeal of the social media posts and various reviews. And to avoid a negative impact on the brand, companies can quickly respond to annoyed, dissatisfied, or simply impolite customers.

– Offering similar products to buy

When a user enters “buy jeans jacket” into the search engine, properly labeled images of jackets available in online shops will appear on the first page. So, chances that someone buys from you rise, and this is why image annotation is essential for any retail business.

– Helping to diagnose diseases

Artificial Intelligence is not (yet?) ready to replace doctors, but it already can help them identify serious health issues. For example, machines can learn to detect cancer on an x-ray from thousands of images and become an additional “checkpoint” for doctors when determining the diagnosis.

– Verifying ID and reducing the possibility of fraud

Facial recognition is a decent way to prove your identity –– whether when unlocking your devices, withdrawing money, controlling entry to casinos, or crossing international borders. The Washington Post says that ten federal agencies plan to use more facial recognition software.

Data Services: How to Annotate Datasets

The data industry encounters several approaches to categorizing raw datasets: automated, manual, and hybrid. Though plenty of automation tools can cope with labeling huge blocks of data, often their cleaning and preparation still require a human touch.

For example, AI algorithms can be primitive in sentiment analysis. Robots can’t evaluate emotions on videos or pictures –– at least at the beginning of their learning process. But humans can do that easily, quickly, and naturally. That’s why many companies offer you to hire a professional data annotator. Such an approach ensures data labeling accuracy, naturality, and hence, more effective data mining output.

But please don’t confuse Data Labelers with Data Scientists –– as Data Scientists are IT professionals with years of experience.

To Summarize

Data annotation is crucial for getting high-quality and applicable results of data mining. Whether you need to find and block an abusive phrase in messaging, train a chatbot, or choose the right image for your blog post, it’s all about properly labeling input information. This is one of the reasons for delegating data annotation to dedicated teams as they guarantee fast, professional, and relevant classification of text, images, video, and audio files.

Now you know how Artificial Intelligence helps businesses attract potential customers, so don’t miss a chance to train your own ML models, get insights and get profits.

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