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Structured vs Unstructured Data – What's the Difference?

November 16, 2018

structured vs unstructured data

Data science is bringing the world together and concentrating randomly distributed information into small units.

With all the buzz about big data and the ways companies use it, you may find yourself asking, “What types of data are we referring to?”

Well, the first thing to understand is that not all data is created equal. This means the data generated from social media apps is completely different from the data generated by point-of-sales or supply chain systems.

Some data is structured, but most of it is unstructured. The way this data is collected, processed, and analyzed depends on the data format.

Once you have a basic understanding of qualitative vs. quantitative data, you can then make sense of data structures or lack-thereof.

To clarify, let's break down the unique differences between structured and unstructured data.

In addition to being sourced, collected, and scaled in different ways, structured and unstructured data will reside in entirely separate databases.

What is structured data?

Structured data is most often categorized as quantitative data, and it's the type of data most of us are used to working with. Think of data that fits neatly within fixed fields and columns in relational databases and spreadsheets.

Examples of structured data include names, dates, addresses, credit card numbers, stock information, geolocation, and more.

Structured data is highly organized and easily understood by machine language. Those working within relational databases can input, search, and manipulate structured data relatively quickly using a relational database management system (RDBMS). This is the most attractive feature of structured data.

The programming language used for managing structured data is called structured query language, also known as SQL. This language was developed by IBM in the early 1970s and is particularly useful for handling relationships in databases.

If it sounds confusing, the picture below should help visualize how structured data relate to each other within a database.

relational database example

From the top-down, we can see that UserID 1 refers to the customer Alice, who had two Order IDs of ‘1234’ and ‘5678’. Next, Alice had two ProductIDs of '765’ and ‘987’. Finally, we can see Alice purchased two packages of potatoes and one package of dried spaghetti.

Is this data useful on the surface? Not really, but running it through analytic tools can help unveil patterns and trends about a specific customer or customer base. This type of dataset is commonly seen in CRM software.

Structured data revolutionized paper-based systems that companies relied on for business intelligence decades ago. While structured data is still useful, more companies are looking to deconstruct unstructured data for future opportunities.

What is unstructured data?

Unstructured data is most often categorized as qualitative data, and it cannot be processed and analyzed using conventional data tools and methods.

Examples of unstructured data include text, video files, audio files, mobile activity, social media posts, satellite imagery, surveillance imagery – the list goes on and on.

Unstructured data is difficult to deconstruct because it has no predefined data model, meaning it cannot be organized in relational databases. Instead, non-relational or NoSQL databases are the best fit for managing unstructured data.

Another way to manage unstructured data is to have it flow into a data lake, allowing it to be in its raw, unstructured format.

95%

of businesses cite the need to manage unstructured data as a problem for their business.

Source: Techjury

Finding the insight buried within unstructured data isn’t an easy task. It requires advanced analytics and a high level of technical expertise to really make a difference. Data analysis can be an expensive shift for many companies.

Those able to harness unstructured data, however, are at a competitive advantage. While structured data gives us a birds-eye view of customers, unstructured data can provide us with a much deeper understanding of customer behavior and intent.

Structured vs unstructured data-1

For example, data mining techniques applied to unstructured data can help companies learn customer buying habits and timing, patterns in purchases, sentiment toward a specific product, and much more.

Unstructured data is also key for predictive analytics software. For example, sensor data attached to industrial machinery can alert manufacturers of strange activity ahead of time. With this information, a repair can be made before the machine suffers a costly breakdown.

Structured vs. unstructured data

There are some notable differences between structured and unstructured data to be aware of when dealing with any of the data types. The following table will help compare the two types of data based on factors such as data sources, data storage, internal structure, data format, scalability, usage, and more. 

Structured data Unstructured data
Structured data is quantitative data that consists of numbers and values.
Unstructured data is qualitative data that consists of audio, video, sensors, descriptions, and more.


Structured data is used in machine learning and drives machine learning algorithms.


Unstructured data is used in natural language processing and text mining.


Structured data is stored in tabular formats like excel sheets or SQL databases.


Stored as audio files, videos files, or NoSQL databases


Structured data has a pre-defined data model.


Unstructured data does not have a pre-defined data model.


Structured data is sourced from online forms, GPS sensors, network logs, web server logs, OLTP systems, and the like.

Unstructured data is sourced from email messages, word-processing documents, pdf files, and so on.


Structured data is stored in data warehouses


Unstructured data is stored in data lakes


Structured data requires less storage space and is highly scalable.


Unstructured data requires more storage space and is difficult to scale.

Semi-structured data

Semi-structured data is a type of structured data that lies midway between structured and unstructured data. It doesn't have a specific relational or tabular data model but includes tags and semantic markers that scale data into records and fields in a dataset.

Common examples of semi-structured data are JSON and XML. Semi-structured data is more complex than structured data but less complex than unstructured data. It's also relatively easy to store than unstructured data and bridges the gap between the two data types. 

Metadata - the master data

Metadata is often used in big data analysis and is a master dataset that describes other data types. It has preset fields that contain additional information about a specific dataset.

Metadata has a defined structure identified by a metadata markup schema that includes metadata models and metadata standards. It contains valuable details to help users better analyze a data item and make informed decisions.

For example, an online article can display metadata such as a headline, a snippet, a featured image, image alt-text, slug, and other related information. This information helps differentiate one piece of content from several other similar pieces of content on the web. Metadata is, therefore, a handy set of data that acts as the brain for all different types of data.

The future of data

The volume of big data is continuing to rise, but soon, the importance of big data storage will cease to exist.

Regardless if data is structured or unstructured, having the most accurate and relevant data sources at hand will be key for companies looking to gain an advantage over their competitors.

Utilizing the right data management will allow companies to:

  • Reduce operational costs
  • Track current metrics and create new ones
  • Understand its customers on a far deeper level
  • Unveil smarter and more targeted marketing campaigns
  • Find new product opportunities and offerings

The big data analytics market is set to reach $103 billion by 2023 and there will be roughly 2.72 million data science jobs posted over the next few years.

The more varieties of data created by data scientists will lead to new and advanced algorithms – toeing the line of GDPR compliance.

“Now when you hide a Facebook ad it asks you the reason and one option is that it knows too much.”

David Teicher
Chief Content Officer, Brand Innovators

This is a testament to just how powerful big data can be when leveraged in unique ways. At the end of the day, it’s up to the consumer to determine how comfortable they are with the ways their data is used.

New to big data analytics but want to learn more? Learn how to gain real-time insights from your data with the right big data analytics software.

big data analytics
Data trends, patterns, and anomalies

Get insights into large data sets transformed into understandable formats using big data analytics.

big data analytics
Data trends, patterns, and anomalies

Get insights into large data sets transformed into understandable formats using big data analytics.

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