Structured vs Unstructured Data: 5 Main Differences

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Written By Haisam Abdel Malak

About: Haissam is a digital software product manager with 15 years of expertise in developing enterprise content management solutions. His core capabilities encompass digital transformation, document management, records management, business process automation, and collaboration.

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what is structured and unstructured data
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Every day, organizations deal with massive amounts of data. The capacity to appropriately gather, organize, and evaluate it will have a significant impact on its degree of success.

Data is regarded as the “new oil” that may add significant value to our everyday operations and, when correctly examined, can serve as a solid foundation for any business decision.

Business data may be found in a number of formats, ranging from structured relational databases to your most recent LinkedIn post.

There are two forms of data: structured and unstructured data. In this post, we will look at what structured data is. What exactly is unstructured data? and the difference between them (structured vs unstructured data)

Structured data is data that follows a pre-defined data model and is thus easy to analyze. It is structured or clearly identifiable, such as a spreadsheet with customer names. Unstructured data consists of information that is not easily searchable and challenging to analyze such as audio, video, and social media postings.

Check out this page for a comprehensive article on data management.

What is Data Management and Why Is it Important? (

What is Structured Data?


Structured data follows a regular sequence, corresponds to a data model, and can be readily retrieved and utilized by a human or a computer program.

It’s quantitative, well-organized, and fits into spreadsheets and relational databases with ease. It is formatted into systems that have a standard design and fit into predetermined rows, columns, and tables.

SQL (Structured Query Language) is a language developed by IBM in the 1970s that is commonly used to manage structured data stored in databases. Names, addresses, phone numbers, Social Security numbers, and other types of structured data are common examples.

SQL is used in business to alter, search, retrieve, and remove data, among other things. Data recorded in relational databases can be entered by humans or by other systems that import collected data to system databases.

Other applications are also used to store structured data, such as MS Excel, which allows for the easy manipulation of large amounts of data and may be linked to other analytical tools for further study.


By now, we should know that structured data has the following characteristics

  • Quantitative: Used to express volumes, amounts, or a range of values. For example, a cup of coffee at Starbucks costs $5.
  • Pre-defined data models: Based on a structure that specifies how data should be represented. It is more schema-dependent and less flexible.
  • Easy to search for and manipulate: Businesses utilize queries to alter data and obtain the information they want for reporting, analytics, or changes, allowing for interaction with other systems and being best suited for process automation.
  • Defined Storage: Structured data is commonly stored in relational databases, data warehouses, or simply Excel spreadsheets.
  • Created by either machines or humans: It is generally imported into databases by humans, either manually or through spreadsheets or other business programs that automatically save data in the same format.

Pros and Cons

Let’s look at the key advantages and disadvantages of working with structured data.


  • Ease of access: Data stored in a relational database may be quickly queried by other business users, systems, or automated processes and reported back in the form of a report.
  • Universally Understood: The predetermined architecture plays a vital function in allowing for an easy understanding of the schema in a relatively short period of time.
  • Data programs can easily consume it: For querying and manipulation, machine learning (ML) algorithms may simply access the structure of fields.
  • Security: It is simple to impose restrictions on who may see, alter, or delete this data.


  • Limited Storage: As we saw in this post, we only have a few options for storing structured data, such as relational databases, data warehouses, and spreadsheets.
  • Limited Usage: Pre-defined, structured data can only be utilized for the purpose intended, resulting in some inflexibility.

What is Unstructured Data?

Unstructured data is data that has not been processed and is stored in its original format. It comes in a variety of forms and formats, such as email, social media posts, presentations, videos, and images.

According to the most recent figures, unstructured data accounts for 80% of all data created in the globe.

Before organizations can harness the value of unstructured data, it must first be processed and evaluated. When correctly assessed, businesses may gain additional insights from their customer evaluations, for example, to determine how a given product is performing.


Let’s look into the characteristic of unstructured data.

  • Qualitative: Information that describes qualities or characteristics. It is gathered through the use of surveys, interviews, or observation.
  • No predefined data model: has no structure and does not correspond to a data model
  • Difficult to search
  • Native format: It is not preserved as rows and columns, but rather in its original structure.
  • Created by either machines or humans

Pros and Cons

Let’s look at the key advantages and disadvantages of working with unstructured data.


  • Easy storage: Storage for this sort of data is now simpler and less expensive.
  • More insights: Unstructured data requires more effort to process, but it typically contains more insights relevant to your business. It Identifies patterns and trends that help to understand why something is happening.
  • Flexible storage: Applications, non-relational databases, data lakes, and data warehouses can all be used to store data.


  • Harder to analyze: Unstructured data need the use of advantageous techniques and technologies in order to be analyzed. This procedure may be aided by artificial intelligence.
  • More storage size: Due to the nature of unstructured data, some of these files require significantly more space than organized data.

Structured vs Unstructured Data

It was required to go over the definitions of both categories in order to understand the difference between structured and unstructured data. Without further ado, let us discuss structured vs unstructured data.

The best way to accomplish this is to present the comparison side by side, as seen below. You can also view the YouTube video below if you like to learn by watching videos.

Structured DataUnstructured Data
Quantitative represented as numbers, dates, amounts, and stringsQualitative data that comprises text, video, audio, photos, and more
Pre-defined data modelNo pre-defined data model
Easy to searchDifficult to search
Text basedText, audio, video, image, PDFs, etc.
Stored in relational databases, data warehouses Applications, data warehouses, and data lakes
Stored as rows and columnsStored in various formats natively
Generated by humans and machinesGenerated by humans and machines
20% of enterprise data80 % of enterprise data
Requires less storageRequires more storage

Structured and Unstructured Data Examples

Now that we’ve defined the difference between structured and unstructured data, let’s look at some real-world instances.

Structured data examples: Dates, numbers, phone and social security numbers, customer names, addresses, products names, etc.

Unstructured data examples: Emails, images, social media posts, videos, data from ioT devices, audio, PDFs, and so on.

What is Semi-structured Data?

Semi-structured data serves as a link between structured and unstructured data.

It lacks a predetermined data model and is more complicated than structured data while being easier to store than unstructured data.

It keeps internal tags or metadata that identify distinct data pieces, allowing data analysts to infer information grouping and hierarchies. Metadata, in the end, allows semi-structured material to be cataloged, searched, and analyzed more effectively than unstructured data.

Examples of Semi-structured data

  • Email
  • JSON
  • XML
  • CSV

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