What are the disadvantages of unstructured data
What is unstructured data?
Unstructured data is often overlooked as a source of insights, even though it can help companies make better decisions.
Technologies in this area are evolving and can help us uncover insights into this unstructured data, whether it's posts on Instagram, call logs, or interview notes.
In this blog post we show what unstructured data is, present some examples, explain what you can do with it and finally why it is a good idea to combine this with structured data.
Define unstructured data
In our new guide, we bring structured and unstructured data together. We define unstructured data like this:
Unstructured data refers to data that is not easily searchable. More processing steps are required because the data is not organized or arranged in proper areas.
Examples of unstructured data
Sources that are likely to contain unstructured data can be:
- Social media posts
- Call logs of customer service calls
- Open questionnaires
- Audio recordings
Work with unstructured data
One of the best things about unstructured data is the sheer amount of it that is out there.
Think of all the emails in your customer service inbox or all the web chats your customers had with your employees. With the right tools, you can structure and learn from this type of data.
But it is not always that simple and, unlike structured data, the tools required for the process are still relatively new on the market. For example, there is image analysis software that companies can use to find their logo on thousands of social media posts, compared to databases of identification numbers that have not been around for that long.
Here's a quick rundown of the pros and cons:
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