According to Gartner, by 2022, 70% of customer interactions are expected to filter through technologies like machine learning (ML) applications, chatbots and mobile messaging.
As brands gather more and more insight on their customers, AI can help make the data collected actionable. How well you know your clients directly impacts the quality of their experiences. Why does it matter?ĭata is the backbone of the customer experience. Now, the global data annotation tools market is projected to grow nearly 30% annually over the next six years, according to GM Insights, especially in the automotive, retail and healthcare sectors. By 2025, an estimated 463 exabytes of data will be created globally on a daily basis, according to The Visual Capitalist - and that research was done before the COVID-19 pandemic accelerated the role of data in daily interactions. It's the human-led task of labeling content such as text, audio, images and video so it can be recognized by machine learning models and used to make predictions.ĭata annotation is both a critical and impressive feat when you consider the current rate of data creation. What is data annotation?Ĭomputers can't process visual information the way human brains do: A computer needs to be told what it's interpreting and provided context in order to make decisions. Data annotation is the workhorse behind our algorithm-driven world. But their ability to deliver on these promises is dependent on data annotation: the process of accurately labeling datasets to train artificial intelligence to make future decisions. We rely on these algorithms for a number of different reasons which include personalization and efficiency.
Even the simplest decisions - an estimated time of arrival from a GPS app or the next song in the streaming queue - can filter through artificial intelligence and machine learning algorithms.
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