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Comparing Structured and Unstructured Data

In today’s data-centric world, it is necessary to understand what the difference between structured and unstructured data is and what that means to us.

So, let’s first start out with highlighting what structured data and unstructured data are. Generally, structured data resides in relational databases (RDBMS). Length-delineated data such as phone numbers and Social Security numbers is stored in this type of database. This structured data can be searched through and for via human generated queries or sophisticated algorithms.

Unstructured data on the other hand, is all the other data. It can be stored with a non-relational database and may be textual or non-textual. Human-generated unstructured data includes: media, mobile data (text messages, locations), text files (email, documents) and social media (data from Twitter, Instagram, LinkedIn, Facebook). Machine-generated unstructured data includes: sensor data, scientific data, digital surveillance and satellite imagery.

So given the general definitions of both structured and unstructured data, what does this mean specifically? There are a couple main differences between the two that will draw more significance for businesses. The main difference is that the ease of actually working with the data depends on whether it is stored in a relational database or outside of one. Textual unstructured data, for example, can be utilized to run a simple search and pull content. However, this does not automatically create value for the party pulling the data, even if the source has great potential to drive opportunities. Another significant difference is that there is just simply more unstructured data out there. Out of enterprise data, unstructured data makes up roughly 80% of it. There is a growing need for companies to be able to leverage this data and use it productively in business practices.  

This point, however, introduces a challenge that can occur with unstructured data specifically. The potential is there to create intelligence from the data, but companies across the globe would be doing so right now if only it were that easy. It is typical for structured data to be worked with using machine language or used to pull information from an organized source, but this is not the case with unstructured data. For both types of data, there is also the potential to simply have too much data to work with. It takes a lot for companies to organize their data. Many companies are facing the issue of having 20+ years of unorganized data and also understanding that most data has a shelf life.

The amount of money and resources that companies would need to put towards sifting through unstructured data in order to extract intelligence is a common problem that companies are facing today. Now, there are many players on the market that are keen on solving this problem. Companies, such as Expert System, offer intelligent technology that can be used for keyword extraction in order for companies to utilize their data to improve and increase business.

Technology and data are certainly not going anywhere. Advancements will have to continue to improve at a great speed in order to keep up with other areas of development. The more that our digital age evolves, the more data we will have to work with.