Structured vs. Unstructured Data: All You Need to Know

The data we create exists in various formats and can easily fit into two categories: structured and structured data. In this article, you’ll learn what they mean as well as their applications, similarities, differences, and features.

IDC predicted that the global volume of newly created data would grow 44 times to 35 Zettabytes (35 trillion gigabytes) in 2020, up from 33 Zettabytes in 2018 (1). The company believes that by 2025 new data creation will reach 175 ZB  globally.

To understand this data, here’s a quick analysis. A 175 ZB of data holds approximately 5 trillion hours worth of video recordings. And it takes about 571 million years to watch those videos. In the unlikely event that anyone ever lives up to that age, they would have seen over 19 million generations.

Let start with understanding the similarities and differences between structured and unstructured data.

Structured vs. Unstructured Data: Similarities and Differences

What Is Structured Data?

Structured data is a data type that exists in a precisely defined format. The data usually sits in tables showing a clear relationship between the rows and columns. Structured data takes less effort to analyze—it’s easily accessible, searchable, and requires lesser storage space.

The data is easy to work with, and it’s compatible with most business intelligence tools. Data scientists also refer to it as schema-on-write. The data is typically number-heavy but can also include some texts. 

Here are some examples.

  • National census data
  • Election results
  • Customers transactional data
  • Stock information
  • Geolocation data
  • Excel data
  • Staff appraisal score
  • Academic performance results
  • Close-ended surveys
  • Web traffic data

What Is Unstructured Data?

Unstructured data is data that exists in its native format. It’s largely unorganized and requires processing to make it easier to handle and understand. The data takes much effort to evaluate by hand, but advancement in AI-driven tools helps automate the tasks.

Structured data requires larger storage spaces, and data managers can’t store the data in relational and SQL databases. It’s often text-heavy, though it could include video, audio, images and numbers.  Here are examples of unstructured data:

  • Social media posts
  • Online reviews
  • Emails
  • Documents
  • Images
  • Multimedia files
  • Open-ended surveys
  • Phone calls
  • Text files
  • Opinions 

Structured and Unstructured Data Similarities

Structured and unstructured data are similar in some ways. Let’s take a look at some of them.

  • They both support texts and numbers, though structured data tends to be number-heavier while unstructured data contains more text.
  • Structured and unstructured data systems are scalable.
  • Both support cloud deployment.
  • They offer valuable insights to businesses.
  • Structured and unstructured data both have human-generated data and computer-generated data as data sources.
  • Both can exist in HTML formats. Web traffic data like Google Analytics exists online as an HTML file, while social media posts also exist in the same format.

Structured vs. Unstructured Data Differences

Here are some ways structured and unstructured data differ:

  • Structured data exist in predefined formats, while unstructured data exists in raw form and native format.
  • Structured data appears highly organized while structured data is not.
  • It’s a lot easier to search and analyze structured data than unstructured data.
  • Structured data is easier to work with, while the latter requires processing before users can work with it.
  • People can easily quantify and measure structured data, while unstructured data are generally qualitative.
  • The qualitative nature of unstructured data makes it text-heavy, while structured data are typically number-oriented.
  • Unstructured data are over four times more readily available in the world than structured data.
  • SQL databases and data warehouses are best-suited for storing structured data, while unstructured data typically resides in data lakes and non-relational databases.
  • Unstructured data provides deeper insights than structured data.
  • Machine-generated unstructured data often exists in multimedia formats, while structured data tends to contain more numbers.
  • Structured data has relatively more protection than unstructured data, which are often publicly available.
  • Structured data requires lesser storage space than unstructured data.
  • Structured data uses machine learning, while unstructured data uses natural learning processing.
  • Users can evaluate structured data using mathematical and statistical techniques, while data mining and data stacking help analyze unstructured data.

Advantages and Disadvantages

What advantages and disadvantages do structured and unstructured data offer businesses? Here, we’ll explore them.

Structured Advantages

Here are some of the advantages of structured data:

  • The data exists in defined and standard formats.
  • It’s easy to use, measure and analyze.
  • It requires less storage space.
  • The data has better security against data breaches.
  • It doesn’t require much processing to understand.
  • Structured data systems support cloud use.
  • It’s compatible with most business intelligence tools.

Unstructured Advantages

Here are some of the advantages of structured data:

  • It contains deep insights that can help businesses improve customer service, grow profits, reduce costs and more.
  • It’s effortless to scale and less expensive.
  • Structured data is easier to accumulate, and this makes it overwhelmingly abundant. 
  • Structured data exists in its native format and raw form, making it adaptable and available for extensive use cases.
  • It supports cloud deployment.

Structured Disadvantages

Here are the key things we don’t like about structured data:

  • It’s sometimes difficult to scale structured data systems without compromising performance.
  • Structured data in a database exists in a defined format, making it less adaptable and available for large use cases.
  • It provides limited insight.
  • Structured data is not easy to accumulate.

Unstructured Disadvantages

Here are the key disadvantages of unstructured data:

  • It is not easily searchable, and the data requires much effort to analyze.
  • Unstructured data takes up ample storage space.
  • It’s not compatible with most business intelligence tools
  • The data is more susceptible to data breaches and unauthorized access. 
  • It requires much to understand.

Structured vs. Unstructured Data: Side-by-Side Comparison

Structured and unstructured data share some similarities and also differ in many areas. But how do they compare side-by-side?

Here’s what we found.

Data Value

What values do the data present their information?

Structured Data Value

Structured data is typically quantitative. 

Quantitative data is a data type where each data set has a unique numerical value associated with it. The data is measurable, and the data users can evaluate it using mathematical and statistical techniques.

Structured data answers questions like:

  • How many?
  • How often?
  • How much?

Unstructured Data Value

The values of unstructured data are often qualitative.

Qualitative data is a non-numerical data type; hence, users cannot process or evaluate it using conventional methods. The data approximates, describes and characterizes events.

The data users often categorize qualitative data according to attributes, qualities and properties.

The data answers questions like:

  • Who (for example, who are the customers?)
  • What (what do they need?)
  • Where (where do they live?)
  • When (when do they need it?

The Key Takeaway

Structured data values are typically quantifiable and measurable, while unstructured data is not, but often descriptive.

Data Availability

How abundant are they? What volume of the global data sphere is available as structured or unstructured data?

Structured Data Availability

About ten to 20 percent of all globally generated data is available as structured data.

Unstructured Data Availability

According to most estimates, unstructured data accounts for 80 to 90 percent (2) of the overall global data sphere, and it’s growing at the rate of 62 percent yearly (3).

IDG believes unstructured data will account for 93 percent of all data in 2022 (4).

The intimidating volume of unstructured data discourages companies from attempting to mine it for valuable information — most companies only analyze 12 percent (5) of the data they have, leaving the other 88 percent laying waste.

Gartner calls those 88 percent ‘dark data’ —  “the information assets organizations collect, process and store during regular business activities, but generally fail to use for other purposes.”

The Key Takeaway

Unstructured data is the most abundant data type globally, while structured data is a small and declining business information.

Data Storage and Management

What storage architecture works better for storing and managing structured and unstructured data?

Structured Data Storage and Management

Structured data generally resides in SQL databases and data warehouses.

A data warehouse is a technology that electronically stores large information by integrating data from multiple heterogeneous sources. It runs queries and analysis of historical data, supports analytical reporting and aids decision making.

A database is a repository of an organized collection of data stored electronically on a computer. It often uses structured query language (SQL) for writing and querying data.

Structured data requires less storage space and has the potential for cloud use.

Here are some of the different tools for managing structured data:

  • Oracle Relational Database Management System
  • Microsoft SQL Server
  • MySQL
  • PostgreSQL

Unstructured Data Storage and Management

NoSQL or non-relational databases and data lakes form the core of unstructured data storage architecture.

A data lake is a repository for storing extensive unrefined data. James Dixon (6), who originally coined the word, described data lake to be akin to a natural water body in its original state. Data from smaller water bodies (data sources) flow to the lake, and users have unrestricted access to the lake.

Data lakes, unlike data houses, don’t profile data before accepting them. It loads all data from the source systems into the lake without turning any away.

Here are the other vital things that define a data lake:

  • A data lake stores the data in unrefined formats
  • It supports all data types
  • Data lakes are easily scalable
  • They support all users

Unstructured data requires bigger storage spaces. It also supports cloud use. These technologies work best for storing and managing them:

  • Apache Hadoop
  • Amazon DynamoDB
  • MongoDB
  • Microsoft Azure
  • IBM Spectrum Scale
  • Cloud drives, for examples, Google Drive, Microsoft OneDrive

The Key Takeaway

The refined nature of structured data makes it best suited for relational databases and data warehouses. While unstructured data best resides in a data lake and non-relational databases.

Both data types have the potential for cloud deployments.


Structured data and unstructured ones are both valuable to data managers. But which of them provide much deeper insights?

Structured Data Insight

Structured data provides bird-eyes view insights on data.

Birds-eyes view is the ability to look at things from a great height to see a large area. But the setback is seeing things from that elevation makes them look smaller and less detailed.

For instance, a sales record shows the amount customers are spending on a particular product and the frequency of the purchases.

But it might not show why they are buying the products or why they prefer product A to B.

Unstructured Data Insight

Unstructured data holds an overwhelming volume of data in its raw forms, making it more susceptible to providing more profound insights to make informed and data-driven decisions.

Customers’ social media posts, feedback forms and blog comments contain a vast volume of data businesses can mine for opinions and sentiment analysis.

The Key Takeaway

Unstructured data provides more value to businesses. 

The data offer much deeper insights than the statistics and numbers from structured data can rival, and it’s more helpful for finding growth opportunities, trends and identifying risks.

But the data is difficult to extract, categorize and analyze.

Data Sources: Machine-Generated Data

Machine-generated data refers to the kind of data produced by mechanical and electronic devices without human interventions. Both structured and unstructured data have MGD as part of their data sources.

Let’s take a quick look at some of their data sources.

Machine-Generated Structured Data

Here are some sources of machine-generated structured data:

  • Radio-frequency
  • Geolocation data
  • Smart meters
  • Medical devices
  • Point-of-sale data
  • Weblog data

Machine-Generated Unstructured Data

Here are some sources of machine-generated unstructured data:

  • Satellite images
  • CCTV recordings
  • Radar and sonar data
  • Remote sensing camera
  • Surveillance drones
  • Remote sensor alarm

The Key Takeaway

Machine-generated structured data are often number-heavy, while the unstructured equivalents are typically in images, video, audio or a combination of them.

Machine-generated data is often in real-time.

Data Sources: Human-Generated Data

Human-generated data refers to the kind of data produced by humans, often with the help of machines.

Let’s explore some sources.

Human-Generated Structured Data

Here are some sources of human-generated structured data:

  • Excel files
  • Manually-inputted data forms
  • Web traffic data

Human-Generated Unstructured Data

Businesses generate about two quintillions (18 zeros) bytes of data (7) daily across all industries from our seemingly endless activities.

 About 80 to 90 percent of the data exists in a raw and unstructured format. And here are some of the ways we generate a part of the data; of course, most times with the help of machines:

  • Documents
  • Emails
  • Open-ended surveys
  • Social media posts
  • Web content
  • SMS
  • Social media chats

The Key Takeaway

Human-generated unstructured data closely relates to internet-enabled devices and our relationship with them.

Data Storage Format

Data comes in a wide variety of formats. What formats are best suited for saving structured and unstructured data?

Structured Data Storage Format

Data managers can store and export structured data into some of these formats:

  • SQL
  • XML
  • DOCX (for Word Document tables)
  • PDF
  • CSV

Unstructured Data Storage Format

Unstructured data is often in its native format. Here are some of the formats for storing them:

  • PNG
  • JPEG
  • MP3
  • MP4
  • GIF
  • MWV
  • DOCX (for business documents)
  • PDF (unstructured data is easily convertible to a PDF file)
  • HTML (social media posts, blog comments and web content exist as HTML files)

The Key Takeaway

Unstructured data often exist in multimedia formats.

Data Access and Security

Data breaches in the first half of 2020 exposed about 36 billion records (8). About 86 percent of data branches (9) were financially motivated, while ten percent by espionage. 

Both structured and unstructured data are vulnerable to data breaches. But which of them is better protected?

Structured Data Access and Security

Structured data sits in databases and data warehouses, making them relatively secure and protected.

Data stored in data houses conform with the data integrity constraints defined in the database. The data houses also come with access control that restricts unauthorized access to the data and ensures that the correct data is accessible to only assigned users.

Structured data systems are also hardened with firewalls to protect them against external threats, and the activity logs help data administrators maintain real-time surveillance on insider threats. 

Data managers can back up and restore structured data at will.

Unstructured Data Access and Security

Unstructured data spread across the organization, making it accessible to everybody without restrictions, and hence, more susceptible to breaches.

The nature of the data makes it impossible or hard to :

  • Control access to the data
  • Identify who accessed it and using the data
  • Track the flow of the data through an audit trail
  • Implement access control
  • Put up firewalls or encrypted

Companies’ social media data are usually public, and competitors can dive into them for competitive insights. A company can mine data on a competitor’s social media brand mentions to serve the customers better offers.

Also, companies can suffer data breaches if the social media platform gets hacked.

The Key Takeaway

Structured data offer better protection than unstructured data. Data security analysts can easily harden the data against all typically known threats and establish control and authentications for data access.

And they can also back up the data with ease.

Data Scalability

Data scalability is the ability to scale up a data storage system to hold more volume of data without sacrificing performances. 

With the sheer volume of data available to businesses from multiple sources and growing astronomically, companies often question whether their data systems can handle the large influx of data.

Luckily, both structured and unstructured data systems are scalable, but which of them is more straightforward and less expensive?

Structured Data Scalability

Most relational database management systems run on single servers. When the size of the database grows, the organization may experience performance issues.

Some organizations typically resolve this issue by scaling the server. And if the growth continues, the next might be to migrate to a higher performance database.

This migration comes with six major setbacks.

  • Migrating to a more extensive database technology is expensive.
  • For organizations that are self-hosting, the migration might require hiring expert help.
  • Migration could lead to extended or unplanned downtime.
  • A poorly managed transition process can lead to data breach or loss.
  • Managing the server transition process could be a daunting task.
  • It requires a massive expenditure of resources and time.

However, a hybrid cloud strategy could help them sidestep these challenges. It’s easily scalable, and organizations only scale based on their needs.

Unstructured Data Scalability

Unstructured data is easily scalable and less expensive.

Billions of connected devices create data daily, and the scalability of unstructured data probably doesn’t pose a considerable challenge to some organizations, and here are the reasons.

  • Social media posts and files reside on the social media company’s data centers, and the responsibility for scaling the servers falls squarely on them.
  • Many free video and audio hosting and sharing platforms exist; young, and growing organizations can easily store and manage their multimedia files on those platforms.
  • Google Drive offers 15 GB of cloud storage space for free, and it’s easily scalable whenever the organization’s storage need grows.

For bigger organizations that rely on big data for decision-making, hybrid cloud data storage systems offer them smart scalability options. 

It gives them the ability to scale as needed and save cost.

The Key Takeaway

Structured data is more difficult and expensive to scale. However, hybrid cloud storage makes it much easier and cheaper.

Ease of Analysis

Structured and unstructured data are only helpful if adequately analyzed. How easily can users analyze them?

Here’s what we found.

Structured Data Ease of Analysis

Structured data is easily searchable and takes less effort to process and analyze both for humans and algorithms. The data is also easy to interpret and can provide insights at a glance.

Some of the common methods for the data analysis include:

  • Regression
  • Classification
  • Declustering 
  • Frequency distribution
  • Spread and dispersion analysis
  • Mode and median
  • Charts and graphs

Unstructured Data Ease of Analysis

Unstructured data’s lack of predefined models and disorganized internal structure makes it hard to search and intrinsically tricky to analyze. The data requires processing to be understandable and workable.

Users can analyze unstructured data using:

  • Data stacking
  • Data mining
  • Keyword extractions
  • Word cloud
  • Sentiment analysis
  • Survey feedback classifier
  • Survey analysis
  • Natural language processing (the ability of machines to read a text like humans)
  • Audio to text processing

The Key Takeaway

Structured data is easily analyzable and understood, while unstructured data requires processing to gain meaning, and processing the data could be challenging.

But advancement in technology and AI-driven software is enabling it to gain a lot of ground.

Structured Data vs. Unstructured Data: Wrapping It Up

Data will continue to transform our lives for the foreseeable future. Business owners need data to make critical decisions, marketers need it to sell, product developers need to build and maintain their products, even consumers rely on data to make buying decisions. There’s no end to the use and importance of data.

In the coming years, more businesses will rely on data to make quick and agile decisions, uncover patterns, gain valuable insights, and much more—including helping us connect with people and share information.

We create data from thousands of sources daily, and about 80 to 90 percent of the data exists as unstructured data while the other 20 to 10 percent exist in structured formats. As you might expect, the need to understand and use unstructured data will grow more and more important in the coming years.


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