What is an Unstructured Data?

Most of the information organizations actually hold is unstructured: support tickets, contracts, meeting transcripts, scanned invoices, chat logs. Making it usable typically requires an extraction step, such as OCR for scanned documents, natural-language parsing for free text, or embedding models that convert content into vectors for semantic search, that pulls out structured facts or a searchable representation before the data can be queried, filtered, or fed into downstream automation. Unstructured data is exactly the kind of input large language models are good at handling directly, since they can read and reason over free text without a rigid schema, which is why LLM-based pipelines have become a common way to turn unstructured sources into structured, actionable data, such as extracting line items from an invoice or summarizing a long contract, rather than relying solely on brittle rule-based parsers.