What is structured data?
Structured data is information stored in a fixed format of fields with defined types, usually as rows and columns in a database or spreadsheet. Each column holds one kind of value, such as a customer number, an invoice date, or a quantity shipped. Because the shape is known in advance, software can filter, join, sort, and total the data with Structured Query Language (SQL).
Most of the records a business runs on are structured: general ledger entries, purchase orders, inventory balances, and the tables behind an enterprise resource planning (ERP) or customer relationship management (CRM) system. The rules that define the shape are called a schema, which is what makes the data dependable to compute on.
Why structured data matters
Structured data is the cheapest data to analyze and the easiest to trust. A distributor can answer which products sold below target margin last quarter with one query against its order tables, and two analysts running that query get the same answer. It also feeds most classic machine learning models, which expect a table of features.
The limits appear at the edges. Fields get repurposed, codes drift between systems, and the same customer appears under three spellings. Master data management and data quality work exist to keep structured data honest. Large language models have raised the stakes: text-to-SQL tools let a manager ask a question in plain English and have the model write the query, so a clean schema and a maintained data dictionary matter more than before.