What’s the Difference Between Structured and Unstructured Data?
The world is filled with information. Information can come in all kinds of different forms, from an order number to a timestamp to a smile. All of the world’s information can be classified into different types of data, with the two most distinct types being structured and unstructured data.
First, there is structured data, which is data that is comprised of a clearly defined, uniform format. Structured data is typically made up of letters, numbers, and symbols. This data can be, and most often is, stored and organized in databases, which make it easily searchable and easily analyzed. An example of structured data is a database of customer records. In this database, you might find customer first and last name, their customer ID, address, type of contract, etc. These are all pieces of data that are uniform in format and can be organized in a way where a person using the database can easily access and analyze the information for practical use. An example that might feel more at home to those of you familiar with school database systems is your SIS.
Next, we have unstructured data. Unstructured data is made up of types of information that don’t have a basic text format. Instead, unstructured data comes in many different file types, including videos, audio files, even MRI scan results. A rule of thumb when classifying data is that any information that is not structured data is unstructured data. These types of data are much more complex than basic text and numerical data, and therefore take up much more storage. The challenge with unstructured data is that because of the diversity of its many formats, the data can be overly complicated to search through and analyze in comparison to structured data. Mature analytics tools exist for structured data, but analytics tools for mining unstructured data are nascent and developing. These tools will continue to develop and improve, however, because of how rich, authentic, and significant unstructured data can be. And with the increasing number of emerging technologies and social platforms, there is simply much more unstructured data than structured. In fact, unstructured data makes up 80% and more of enterprise data, and is growing at the rate of 55% and 65% per year. Without the tools to analyze all this data, organizations are leaving vast amounts of valuable data on the business intelligence table.
Between structured and unstructured data, there are two other classifications of data that combine to make up around 10% of the world’s information: Semi-structured and quasi-structured. Semi-structured data is made up of textual data files with an apparent pattern, enabling analysis. Two examples of semi-structured data are emails and XML files, a markup language for encoding documents in a format that is both human-readable and machine-readable. One step removed from semi-structured data is quasi-structured data, textual data with even more erratic formats that can be reformatted through software tools to a more practical, easier analyzed file type. While semi and quasi structured data comprise just a fraction of the world’s information, they can be significant information to businesses.
Big Data and Beyond
Many businesses and organizations around the world utilize as much of the information available as they can to make informed business decisions. A term I’m sure you’re familiar with is “Big Data,” a notion that describes ways to analyze, systematically extract information from, or otherwise deal with large data sets to inform business decisions. Businesses will continue to leverage big data in the long run, especially as more methods of collecting data materialize and chunks of unstructured data can be converted to structured data. While using big data does what the name implies – analyzing millions upon millions of data points – the data points are almost exclusively structured data. That means there is a large chunk of the world’s data that has yet to be completely analyzed and put to use. You guessed it; unstructured data.
The ability to “react” to different content on social media and text messaging platforms is an interesting feature that has recently come about. I’ve seen it on Facebook, LinkedIn, and iMessage. These platforms allow users to put their emotion into a form of structured data that can be analyzed by the owners of that data. Let’s take Facebook, for example. Facebook introduced the “like” button and made it iconic, inspiring copycat functionality on nearly every other platform. Nowadays, you can react to a Facebook post with a “like,” “love,” “haha,” “wow,” “sad,” and “angry.” It’s a very interesting set of reactions, and a very limiting set as well. But through this capability, Facebook can add structure to how a piece of content made users feel, giving the company more insight into how certain posts resonate with people. It’s a very interesting example of turning unstructured data into structured data through new ideas and technology. With so many new ways to express emotions, provide feedback, and broadcast opinions, unstructured data is growing exponentially. While the different types of data continue to expand, so will the capabilities of analyzing this unstructured data, converting it into searchable, relevant, and practical structured data.