Few realize that algorithms guide our daily lives. Even the most inconsequential and straightforward decisions—the estimated arrival time from your GPS application to the next track or video on the queue—is filtered through machine learning and artificial intelligence-powered algorithms. And many of us have come to rely on them for reasons that include but aren’t necessarily limited to efficiency and personalization.
However, their features and abilities are dependent on a process called data annotation. This process refers to the accurate labeling of datasets for training AI to perform actions and make decisions. In other words, it’s the workhorse that drives the algorithm-driven world that we live in today.
Data annotation—what is it?
Computers are unable to process optical or visual information in the same way that the human brain does. They need to be told what exactly they’re trying to interpret and given context so that they can make the desired decisions. Through data annotations, they can make these connections. It’s a human-driven task that involves labeling published content like audio, videos, images, and text to ensure that models for machine learning can recognize them and make precise predictions.
Not only is the process an impressive feat, but a critical one when considering the rapid pace at which data is created. Research shows that data amounting to 463 exabytes will be produced daily by 2025. Moreover, the study had been done prior to the pandemic, accelerating data’s role in everyday interactions. Now, experts anticipate the tools market for data annotations to grow to almost thirty percent yearly over the following years, especially in sectors, such as healthcare, automotive, and retail.
Why is it important?
The pillars of consumer service are data. Your knowledge of both existing and potential customers is directly correlated to delivering quality experiences. As more and more brands and companies begin to collect information on their respective audiences, artificial intelligence makes the insight more actionable than it would have been otherwise. And this is important because 70% of all customer interactions have gone digital.
Interactions supported by artificial intelligence will improve interactions, voice, sentiment, text, and survey analysis. However, for virtual assistants, chatbots, and other similar technologies to build a seamless experience, it’s imperative that brands ensure that all the datasets used remain of high quality. As it stands, many data scientists tend to spend a considerable amount of time preparing data, and a portion of it is used to discard or fix odd or anomalous data pieces to keep the accuracy of the measurements.
These tasks are vital given the fact that algorithms depend on understanding the patterns for making their decisions. As a result, incorrect data may lead to biases which, in turn, might result in poor AI predictions.
Similar to how data constantly evolve, the process behind data annotations is increasingly becoming more refined and sophisticated. To put some perspective on the matter, just a few years ago, labeling no more than some points on the face and building AI prototypes with the information was enough. Nowadays, there are multiple points on a single area of the face alone. The same goes with scripted chatbots and predictions. Thanks to data annotation, algorithms will remain a permanent fixture in shaping experiences for customers in the years to come.