What is Structured Data?

Defining structured data

In our new guide on bringing together structured and unstructured data, we define structured data like so:

Structured data refers to data that is formatted in such a way that extracting insights is simple, often sitting in a database. It’s already laid out in an orderly way which makes analysis easier.

Formatting is a key part of structured data – each field adheres to particular rules (for example, is it a date? A currency? A percentage?).

For a long time, structured data was the dominant way in which data was stored –since it was most easily readable by the technology available. But as time goes on unstructured data is becoming a bigger part of the analyses performed by organizations, and more and more software is emerging to help with this.

Structured data examples

Here are some examples of what structured data might look like:

• The output of multiple choice questionnaires
• Sales data that’s been collected in a uniform way
• The output of data being entered into a form on a website
• Customer contact information (if collected in a uniform way)
• Pre-existing datasets that are already formatted consistently

Working with structured data

The best thing about structured data is that it’s ready to analyze, and analysis is pretty easy compared to unstructured data.

As the name suggests, it comes in a uniform, neat package that an analyst can easily search and manipulate.

For example, it’s easy to sort structured data numerically or alphabetically or by date, because you know all of the data in a particular section conforms to that format. This is not the case with unstructured data, since a field could have all kinds of data types (numbers, letters, emojis, pictures, or a mix of all of those).

Here’s a brief run down of the pros and cons: