![]() ![]() How is textual data related to dark data? The advantages and disadvantages of textual data They have to process using a machine learning-powered (ML) tool that can do this for them. For instance, a customer support agent can extract real-time insights into their customers' wants by analyzing email tickets, chatbot conversations, and social media feedback. In short, it's any data that has been expressed in words.Īs more people communicate online, the amount of textual data available grows exponentially-offering unprecedented opportunities for businesses and researchers alike. It can be anything from emails to blog posts to social media posts and online forum comments. Textual data is information that is stored and written in a text format. In this article, we’ll discuss the concept of textual data and how you can use it to extract valuable insights for your customer support operations. Also, when you consider that 80% to 90% of data is unstructured, there’s so much potential waiting to be unlocked. Too often, we get stuck in the DRIP syndrome where we're happily generating data-but can't generate meaningful insights from them. “The syndrome of Data-Rich Information-Poor (DRIP) is often encountered when a business tracks a lot of Key Performance Indicators (KPIs), and so, in theory, they have a lot of data on the operations of the business, but in practice, this KPI tracking does not lead to sustainable process improvements.” says Daniel Shapiro, CTO & co-founder of Lemay.ai.Īnd he's right. If you’re in a data-driven company that relies on such data sources to make critical decisions to optimize and improve your customer experience processes, keep reading. What's worse is that since it's in text format, you know this process can be made easier, but you're unsure how to do it. Manually analyzing hundreds of unstructured text-based data sources is tedious and time-consuming.
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