Structured data

Structured data is information held in a defined schema, usually database rows and columns. Every value sits in a named field with a declared type, which is what lets a query find it without anything being described separately. Most of an organization’s data, however, is unstructured: files such as documents, images, and video.

Key takeaways

Structured data follows a schema: the same fields for every record.
It lives in databases, spreadsheets, and business applications.
Most of an organization’s data is unstructured and held as files, so it has to be described through its metadata.

Why does the difference between structured and unstructured data matter?

Because the tools, budgets, and rules built for one rarely work for the other. Databases describe their own contents, so structured data comes with search, reporting, and access control built in. Files do not, so unstructured data needs its own inventory before it can be searched, governed, or used for AI. Treating the two the same way usually means the larger and faster-growing part, the files, goes unmanaged.

What is the difference between structured and unstructured data?

Structured data fits into rows and columns, like a database table of customer records or a spreadsheet of transactions. Every field has a defined type and a defined place, which is what makes it straightforward to query. Unstructured data has no such arrangement. Video files, images, research output, design files, logs, and documents all carry meaning, but that meaning lives inside the file rather than in a schema. Most organizations hold far more of the second kind than the first, and it is the kind that grows fastest. The practical consequence is that a database query can answer a question about structured data, while answering the same question about unstructured data means reading file attributes from every storage system into one catalog first.

What is the difference between structured, semi-structured, and unstructured data?

The three terms describe how much structure travels with the data. Structured data lives in a defined schema, usually database rows and columns. Semi-structured data carries its own internal markers, as JSON and XML do. Unstructured data is held as files, where the storage system records attributes such as path, size, owner, and dates, and anything about the contents has to be described separately.

	Structured data	Semi-structured data	Unstructured data
How it is held	Rows and columns in a defined schema	Records that carry their own internal markers	Files in a file system or object store
What describes it	The schema itself	Markers inside the data, such as JSON keys or XML tags	File metadata kept by the storage system
Common examples	Database tables	JSON and XML files	Documents, images, video, and instrument output
How a value is found	A query against a named field	A program that reads the markers	A search of file metadata in a catalog

What are examples of structured data?

Customer lists, sales transactions, inventory tables, and sensor readings stored in a database. A hospital, for example, keeps patient appointments in a database: name, date, time, and department, the same fields for every visit. The same hospital also stores scans, PDF lab reports, and research images. Those files are unstructured, and they usually take up far more storage.

Is a spreadsheet structured data?

Yes, when each column holds one kind of value and each row is one record. The spreadsheet file itself is still stored as a file, next to your other unstructured data.

How does structured data relate to file metadata?

File metadata, such as name, size, owner, and last access date, is structured information about unstructured files. Indexing it into a metadata catalog gives you a searchable view of files that have no structure of their own.

How does Diskover help?

Diskover gives your unstructured files the structure they lack. It indexes their metadata into one searchable catalog, so you can find, analyze, and organize files across every storage system.

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