Textual data refers to information that exists in written language form, often unstructured or semi-structured in nature. This includes documents, emails, customer reviews, social media posts, product descriptions, support tickets, and other forms of natural language content. Unlike structured data, which fits neatly into rows and columns, textual data is free-form and requires processing to extract meaningful insights.
For data professionals, textual data represents both a challenge and an opportunity. It often contains high-value signals—such as customer sentiment, intent, or emerging issues—that aren’t captured in traditional metrics. Processing textual data typically involves natural language processing (NLP), text mining, tokenization, and vectorization to convert words into analyzable formats. As AI and machine learning capabilities advance, organizations are increasingly turning to textual data as a key resource for driving competitive insight, automation, and personalization.