Structured data is information that is carefully organized according to well-defined schemas, often stored in databases with rows and columns. Organizations invest in governance practices such as catalogs, metadata tagging, accuracy checks, and lineage tracking to ensure reliability. Examples include financial records, CRM entries, transaction logs, and operational metrics.

‍Why it matters:Structured data has been the cornerstone of enterprise analytics for decades because it is predictable, consistent, and easy to query. It supports reporting, forecasting, simulations, and traditional machine learning models. However, it does not capture the depth of context and nuance found in unstructured data, which is critical for AI-native applications and LLM-driven workflows.