What is the difference between structured and unstructured data?
Structured data is organised into rows and columns with a defined schema, like database tables or spreadsheets, and is easy to query with SQL. Unstructured data has no predefined model, text documents, PDFs, images, emails, and needs different techniques, like search or AI, to extract meaning from it.
The distinction shapes how you analyse data.
- Structured data fits neatly into tables: databases, spreadsheets, CSVs. It has a schema, defined types, and is queryable with SQL, think transactions, customers, inventory.
- Unstructured data has no fixed structure: PDFs, contracts, scanned documents, emails, images. It is rich but not directly queryable with SQL; you need search, natural-language processing or OCR to make sense of it.
- Semi-structured data (JSON, XML) sits in between, with some organising tags but no rigid table schema.
Most organisations have both, and the value often lies in combining them.
iDBQuery handles both under one plain-language interface. For structured data it writes SQL against your live databases, spreadsheets and warehouses and cites answers to source rows. For unstructured documents, PDFs and whole folders of files (with OCR for scans), it indexes the content so you can ask questions and get answers cited back to the source document. You can even ask a question that draws on both, a figure from a database alongside a clause from a contract, in the same conversation. That unification, structured and unstructured together, cited, is a key part of what iDBQuery offers.
Updated 2026-06-22