Unstructured data is an often overlooked source of insights that can help all kinds of organizations make better decisions.
That said, the technology is emerging that can help us make sense of all the unstructured data around us, whether that be Instagram posts, call logs, or interview transcripts.
In this blog post we’ll discuss what unstructured data is, give some examples of unstructured data, explain what you can do with it, and finally talk about why it’s a good idea to bring it together with structured data.
Defining unstructured data
In our new guide on bringing together structured and unstructured data, we define unstructured data like so:
Unstructured data refers to data that isn’t so easily searched (compared to structured data) – more processing is required, as it’s not organized or set out in neat fields.
It’s tempting to call it ‘messy’ data, since it comes in all kinds of formats that aren’t always easy to sort or analyze in the ways structured data can be.
Examples of unstructured data
Sources that are likely to give you unstructured data might be:
- Social media posts
- Call logs from customer service conversations
- Open ended questionnaires
- Audio recordings
Working with unstructured data
One of the coolest things about unstructured data is just how much there is out there.
Think about all the emails in your customer services inbox, or all the web chats your customers have had with your representatives – with the right tools, you can bring structure to this kind of data and learn from it.
But it’s not always easy and, unlike structured data, the tools that are needed to process it are only now coming to the forefront. For example, image analysis software that can find a company’s logo appearing in thousands of social media posts hasn’t been around for long in comparison to, say, databases containing ID numbers.