Structured vs Unstructured Data: Exploring an Untapped Data Reserve

Structured vs Unstructured Data: Exploring an Untapped Data Reserve

Unstructured data is information that isn’t organized in a predefined manner or data that doesn’t have a pre-defined model. It is often text heavy, but it can also contain numbers, dates and images. This data does not follow a specified format.

It’s is data we encounter and create every day, social posts, news articles, emails, audio files and images are all examples of unstructured data. It’s usually human generated and created for consumption by humans and not machines. It comes with a level of complexity and ambiguity that makes it extremely difficult for machines and computers to understand.

Structured data, on the other hand, is data that can be easily organized and referenced, it usually resides in a table or a relational database. It’s easily categorized and organized according to a predefined schema making it super easy to analyze and understand even through machine interactions.

It’s thought that about 10% of data within an enterprise or organization is structured data. So, what are we missing in that other 90%?

Consider the wealth of information and insight that’s overlooked because it’s just too difficult to analyze without having to sit down and read it.

  • Emails Presentations
  • NPS data
  • Customer Complaints and Queries
  • Images

These all contain valuable information and insights from a business point of view. However, they’re all internal data sources, we haven’t even considered external sources like social media data, photos, press releases, reviews. Which all hold as much if not more valuable information that is often left unexplored from a data analysis point of view.

While unstructured data dominates structured by 4 to 1, they often exist in tandem and the ability to analyze both from a data analytics point of view can provide far more insightful analyses. Take emails for example. There is structured data in an email that is easy to understand, categorize and reference. The email sender, the receiver, the date and time it was sent etc. But the body of the email, the actual content, isn’t so easy to understand. It can’t be categorized easily, it doesn’t fit into to a predefined model and requires manual human analysis to be understood.

How can we start extracting value from the mountain of unstructured data out there?

These data sources are often overlooked simply because, it’s too hard for humans to trawl through it and basic information retrieval tools and solutions just don’t have the power to understand this lingual and visual information. While it’s easy for a human to look at a piece of text or an image and extract information from it, it’s far from trivial for a machine to do the same.

With modern technology however, we can start making sense of the mountain of data which is primarily unexplored. Artificial Intelligence, Machine Learning, Natural Language Processing and Image Recognition developments are some of the technological advances that are helping make sense of the vast amount of unstructured data available both publicly and within the enterprise.

Machines can understand unstructured information better than ever before. We have made advancements in Artificial Intelligence that have brought human like capabilities to how machines understand unstructured information.

Understanding Lingual information



Understanding visual information



This technology has been around for quite some time but it’s only in recent years that they have been popularized and adopted as part of an advanced data management strategy.

The adoption of modern data management technologies, cheaper computing power and the media hype on the power of AI have only cemented the fact that we are on our way to being able to make sense of the majority of data out there, which has somewhat been overlooked up until now. It seems we have accepted there are powerful insights in human generated data that doesn’t sit in a database. We have embraced the power of computer intelligence to help us harness that information by adding a depth to data analysis that is impossible to achieve otherwise, but we need a shift in investment and mindset in order to truly take advantage of the “big data” out there.


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