Structured data is information stored in a fixed, predictable format — rows and columns in a spreadsheet or database, each one holding the same kind of value every time — so a computer system can search, sort or calculate on it directly, without first having to work out what the information means.
Statistics Canada’s Canadian Survey on Business Conditions puts a real number on how much Canadian businesses lean on exactly this kind of information. “Among businesses that reported using AI over the last 12 months (19.2%), the most commonly used applications were data analytics (36.6%), followed by text analytics (34.5%) and virtual agents or chat bots (28.2%)”. Data analytics, the single leading application, is built almost entirely on structured data — transaction records, account fields, dates, amounts, categories — rather than on free text or images.
Structure is what makes an audit trail possible in the first place. Because every value in a structured field always means the same thing in the same place, a firm can trace exactly which record fed a given result, check it against the source, and correct it, without first having to work out how a piece of free text was supposed to be read. That traceability is a large part of why data analytics leads the Canadian application list above: it is the application most able to show its own working.
A spreadsheet of mortgage applications is structured data: every row is one applicant, and every column always holds the same kind of value — income in one, requested amount in another, credit score in a third. A system can sum, filter or sort that spreadsheet in an instant precisely because it never has to guess which column is which or work out what a cell means before using it. Compare that with a folder of scanned application letters covering the same applicants: the same underlying facts are present, but nothing can be summed or filtered until someone, or something, first reads each letter and pulls the numbers out into fields of their own.
See also: what is unstructured data, what is a dataset, what is machine learning.
Deciding which structured fields a system is allowed to read, and which stay off limits, is a build decision — custom-ai-solutions covers how that access gets scoped before a system goes near real records.
