Data Annotation for Enhancing the Customer Experience
In today’s digital world, there’s a lot of chatter around Artificial Intelligence and Machine Learning models creating a more seamless user experience and data-driven decision making. Data Annotation takes Machine Learning models to the next level by increasing quality, accuracy, and speed throughout the entire process.
Data Annotation is changing the way consumers interact with brands, resulting in an improved customer experience.
First off, what is Data Annotation?
Data Annotation, also known as Digital Annotation, is essentially the process of labeling and tagging available data in various formats such as text, video or images, to help a Machine Learning (ML) model understand it.
An Artificial Intelligence (AI) model is only as smart as the data it’s trained with, and the more data you use to train the model, the smarter it becomes. There are many software options available when it comes to real time transcription and labeling, but without involving humans in the process, there are certain elements missing.
The reality is, computers don’t process information the same way humans do, which is why human annotators are needed to provide accurate annotation for training the AI/ML. If we want these models to be able to think and act like a human, we have to train them to identify patterns and understand context in order to make decisions. Humans providing data annotation solutions are essentially powering the world of AI algorithms.
Types of Data Annotation
Annotation can come in many forms, but the primary types of annotation are text annotation, image annotation, and video annotation.
By combining human intelligence and annotation tools to analyze text, we can pick out elements such as sentiment, emotion, and opinion, and can recognize modern slang and humor in order to properly annotate different pieces of text. This can really come in handy for brands who are interested in reviewing the sentiment or emotion carried in customer reviews, social media posts, and public forums. Once the ML model has experienced enough data, it will be able to pick out and understand the underlying context of the text without requiring human assistance. This annotation process results in more structured data to drive decision making.
Labeling images is important for making objects recognizable to the ML models. As a basic example, if the AI labels dogs and cats the same way, an annotator could help train the AI by labeling a large data set as either cats or dogs to help the ML model understand the difference between the two. Image annotation can be especially useful when developing facial recognition tools, where the ML model needs to identify and track different facial attributes to determine if they recognize the face or not.
Annotators reviewing videos can help analyze object tracking, speed, and other movement-based analytics. Video annotation is used in a variety of contexts including sports, traffic movement, various human activities, and more.
Why Data Annotation?
Do you want your consumers to see relevant product recommendations when they’re shopping on your site? If they are using a search engine, do you want your brand to be recommended in the top results based on their preferences and previous search behavior? You can leverage Data Annotation for that.
Data is a supporting pillar in creating a positive customer experience. With the help of AI and human annotators, customer data can become more actionable in effectively customizing the user experience.
Data Annotation is for Everyone
Just because you don’t consider yourself a “tech” company, does not mean Data Annotation isn’t for you. As tech continues to evolve and advance across all industries, annotation will be a key component to ensuring the quality of data, systems, and customer experience for years to come.