Structured vs Unstructured Data: What’s the Difference

Structured vs Unstructured Data: What’s the Difference-feature image
Reviewed By: Rajesh Patidar  | 

We are in an age where data is overloading- everything from regional databases to your last Instagram story, every piece of information has become like a lifeblood for many businesses. However, not all data is created equal, and each data format is categorized into two different types broadly: Structured and unstructured data. 

In this article, I will walk you through structured vs unstructured data, explore the differences between these two types of information, and check their examples for data-driven decision-making.

Let’s get to it! 

What is Structured Data?

Structured data is the type of big data that is highly organized and easily interpreted by machine learning algorithms. All the information is organized in rows and columns, just like spreadsheets. These types of data are managed by Sequel Query Language (SQL). Structured data often includes quantitative data; such as age, contact details, address, etc. 

Pros and Cons of Structured Data 

  • Requires less processing and is easy to manage
  • Easy to understand for machine learning algorithms
  • Compatible with a wide range of analytics tools
  • Structured data is space efficient- it requires less storage
  • Limited versatility
  • Manual data entry requires a lot of time
  • It can be expensive to maintain and set up structured data types

Examples of Structured Data

Because structured data is quantitative in nature, it is super easy for big data applications to collect and sort these data types. Some examples of structured data are: 

  • SQL databases 
  • Excel files 
  • SEO tags 
  • Point of sales (POS) data, and more

Top Analytics Software for Structured Data

Also Read: 7 Best Free SQL Software for Windows and Mac

What Is Unstructured Data?

Unstructured data is categorized as qualitative data, and it can’t be directly analyzed by conventional data software or methods. This type of data is available in various forms, such as emails, social media posts, images, videos, audio files, and documents.

Pros and Cons of Unstructured Data 

  • Unstructured data remains in its native format, which makes it highly flexible
  • These data types are very portable and can be stored as data lake unstructured data
  • It has the potential to provide great insights into business decisions
  • It can be stored on-premises or in cloud
  • Demands extensive storage space
  • Challenges in update, delete, and search operations
  • Higher storage costs compared to structured data

Examples of Unstructured Data 

Some of the examples of unstructured data are:

  • Social Media
  • Business Documents 
  • Emails
  • Webpages 
  • Customer Feedback, and more

Top Analytics Software for Unstructured Data

Difference Between Structured and Unstructured Data

Now that you have understood what is structured and unstructured data, let’s talk about their differences. I have also provided a chart for Structured versus Unstructured data. 

Structured vs Unstructured Data: Organization and Format

  • Structured Data: Structured data is highly organized, and it is formatted in a tabular structure, which is typically found in relational databases.  
  • Unstructured Data: It lacks a predefined data model and does not have a specific organizational structure. Unstructured data can include text documents, images, videos, audio files, and more. 

Structured Data vs Unstructured Data: Sources 

  • Structured Data: Structured data is generally sourced from online forms, web server logs, network logs, OLTP systems, GPS sensors, etc.
  • Unstructured Data: These data sources include word processing files, email messages, PDF files, images, etc. 

Structured Versus Unstructured Data: Storage Requirements

  • Structured Data: As we know, structured data is stored in tabular forms like SQL database or excel sheets, and it requires only a small amount of storage. Furthermore, these data can easily be stored in data warehouses and are highly scalable as well. 
  • Unstructured Data: On the other hand, unstructured data is stored as NoSQL databases or media files, and it requires more space. This data type is generally stored in data lakes which makes scaling difficult. 

Structured Data vs Unstructured data: Analysis Methods

  • Structured Data: Analysis methods used for structured data are data clustering, classification and regression. 
  • Unstructured Data: Data mining and data stacking methods are used for the analysis of unstructured data.

Unstructured vs Structured data: In Terms of Flexibility 

  • Structured Data: It is less flexible because the schema and data types are predefined. So, any changes to the structure can be time-consuming. 
  • Unstructured Data: These data types are highly flexible as there are no predefined schemas. You can easily add new types of data without the need to modify the underlying structure. This makes it suitable for handling evolving data types. 

Structured vs Unstructured Data Examples 

  • Structured Data: Some examples of structured data are employee databases, transactions, financial statements, credit and debit card information, etc. 
  • Unstructured Data: A few examples of unstructured data are social media posts, audio or video recordings, images, etc. 

Now, let’s take a look at the comparison chart between structured and unstructured data. Here, we will measure the difference between both data types based on characteristics. 

Characteristics Structured Data Unstructured Data 
Nature Quantitative in nature Qualitative in nature 
Format Fixed and predefined format No predefined format or organization 
Technology It is based on relational database Based on binary and character data 
Processing speed Faster processing due to organized data  Slower processing as it requires advanced algorithms for analysis 
Use cases Online booking, inventory control, CRM, etc. Sentiment analysis, social media analysis, OCR, etc. 
Ease of analysis Easy and straightforward with standard querying (e.g., SQL) Challenging as it requires advanced techniques (NLP, ML) 
Examples Databases (customer info, financial records) Text documents, images, videos, social media posts 

What Is Semi-Structured Data?

Apart from structured data and unstructured data, there is another data type called semi-structured data. This data type is not completely structured or unstructured and includes the characteristics of structured data, and also contains unstructured information that does not follow any specific format or schema. Semi-structured data includes inherited information like location, time, email address, or device ID stamp. 

How to Add Structured Data to Your Website? 

To add structured data to your website, follow the steps below: 

  • Choose your page and select your structured data.
  • Open Google’s Structured Data Markup Helper to add it to your website.
  • Test your structured data and done.

Key Takeaways 

As we are about to conclude our topic on the difference between structured and unstructured data, here are few points to consider: 


  1. Is structured data quantitative?

    Yes, structured data is quantitative. It's often displayed as numbers, dates, values, and strings. 

  2. What is semi-structured data? 

    Semi-structured data are data types that do not comply with a data model but it have some structure. 

  3. What are two examples of unstructured data? 

    The two examples of unstructured data XML files, images, emails etc. 

  4. Where do you get unstructured data? 

    Unstructured data is a type of raw data and it can be found in file systems or data lakes. 

  5. How do you store unstructured data? 

    You can store unstructured data in applications, data lakes, NoSQL databases, and data warehouses. 

Published On: November 30, 2023

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