Interview: Exploring Data Science at Brandwatch with Hamish Morgan
By Olivia SwainAug 23
Marketing
Published July 31st 2019
What is Structured Data?
Structured data is an essential component of every organization’s tool kit.
In this blog post, we’ll discuss what structured data is, give some examples of structured data, explain what you can do with it, and finally talk about why it’s a good idea to bring it together with unstructured 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:
Structured data: The pros and cons
| Pros | Cons |
|---|---|
| Easy to analyze (when compared to unstructured data) as less processing is required | Life doesn't always fit into neat, uniform boxes – the types of things you can record in a structured way are limited |
| Computers can easily read and handle structured data, since this practice has been around for a long time | There is more unstructured data than structured data out there. Ignoring unstructured means missing out on a big part of the picture |
Bringing together alternative datasets
We’ve talked about why structured data is great, but we’ve not spoken at all about unstructured data.
This is the kind of data that’s not so easily searched and requires a lot more processing. But that doesn’t mean there isn’t value in it.
Click here to read more about unstructured social data, or click here to read our new guide on the benefits of bringing together structured and unstructured datasets.
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Bringing Together Structured and Unstructured Data
Here’s why and how analysts should take advantage of the benefits of both types of data.
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