Structured vs. Unstructured Data: What’s the Difference?

Written by Coursera Staff • Updated on

There are several types of data within the world of big data. Here’s a guide to structured and unstructured data.

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When it comes to data, files can come in many different forms. There are two main types of data—structured and unstructured. Each is sourced and collected in different ways, living on different types of databases, so their differences are important for data professionals.

But, how do they differ from one another exactly? And, why would you want to use one over the other?

In this article, you'll learn the answers to these questions and many more, including how they're used in the real world, what tools you'll use to manage them, and what professions work with them every day. Let's dive into it.

Structured vs. unstructured data

The main difference is that structured data is defined and searchable. This includes data like dates, phone numbers, and product SKUs. Unstructured data is everything else, which is more difficult to categorize or search, like photos, videos, podcasts, social media posts, and emails. Most of the data in the world is unstructured data.

Structured dataUnstructured data
Main characteristicsSearchable
Usually text format
Difficult to search
Many data formats
StorageRelational databases
Data warehouses
Data lakes
Non-relational databases
Data warehouses
NoSQL databases
Used forInventory control
CRM systems
ERP systems
Presentation or word processing software
Tools for viewing or editing media
ExamplesDates, phone numbers, bank account numbers, product SKUsEmails, songs, videos, photos, reports, presentations

What is structured data?

Structured data is typically quantitative data that is organized and easily searchable. The programming language Structured Query Language (SQL) is used in a relational database to “query” to input and search within structured data. 

Common types of structured data include names, addresses, credit card numbers, telephone numbers, star ratings from customers, bank information, and other data that can be easily searched using SQL. 

This video from Google's Data Analytics Professional Certificate will give you a quick introduction to structured data:

Structured data examples

In the real world, structured data could be used for things like:

  • Booking a flight: Flight and reservation data, such as dates, prices, and destinations, fit neatly within the Excel spreadsheet format. When you book a flight, this information is stored in a database.

  • Customer relationship management (CRM): CRM software such as Salesforce runs structured data through analytical tools to create new data sets for businesses to analyze customer behavior and preferences.

Pros and cons of structured data

There are numerous benefits – and a handful of drawbacks – to using structured data. To help you get a better idea of whether structured data is right for your own project goals, consider the following advantages and disadvantages:

It’s easily searchable and used for machine learning algorithms.It’s limited in usage, meaning it can only be used for its intended purpose.
It’s accessible to businesses and organizations for interpreting data.It’s limited in storage options because it’s stored in systems like data warehouses with rigid schemas.
There are more tools available for analyzing structured data than unstructured.It requires tabular formats that require rigid schema consisting of predefined fields.

Structured data tools

Structured data is typically stored and used with relational databases and data warehouses supported by SQL. Some examples of tools used to work with structured data include:

  • OLAP

  • MySQL

  • PostgreSQL

  • Oracle Database

What is semi-structured data?

So, what’s in between? Semi-structured data is a mix of both types of data. A photo taken on your iPhone is unstructured, but it might be accompanied by a timestamp and a geotagged location. Some phones will tag photos based on faces or objects, adding another element of structured data. With these classifiers, this photo is considered semi-structured data.


What is unstructured data?

Unstructured data is every other type of data that is not structured. Approximately 80-90% of data is unstructured, meaning it has huge potential for competitive advantage if companies find ways to leverage it [1]. Unstructured data includes a variety of formats such as emails, images, video files, audio files, social media posts, PDFs, and much more.

Unstructured data is typically stored in data lakes, NoSQL databases, data warehouses, and applications. Today, this information can be processed by artificial intelligence algorithms and delivers huge value for organizations.

Read more: Data Lake vs. Data Warehouse: What’s the Difference?

Examples of unstructured data

In the real world, unstructured data could be used for things like:

  • Chatbots: Chatbots are programmed to perform text analysis to answer customer questions and provide the right information.

  • Market predictions: Data can be maneuvered to predict changes in the stock market so that analysts can adjust their calculations and investment decisions.

Pros and cons of unstructured data

Just as with structured data, there are numerous pros and cons to using unstructured data. Some of the advantages and disadvantages to using unstructured data include:

It remains undefined until it’s needed, making it adaptable for data professionals to take only what they need for a specific query while storing most data in massive data lakes.It requires data scientists to have expertise in preparing and analyzing the data, which could restrict other employees in the organization from accessing it.
Within definitions, unstructured data can be collected quickly and easily.Special tools are needed to deal with unstructured data, further contributing to its lack of accessibility.

Unstructured data tools

Unstructured data is typically supported by flexible NoSQL-friendly data lakes and non-relational databases. As a result, some of the tools you might use to manage unstructured data include:

  • MongoDB

  • Hadoop

  • Azure

Read more: NoSQL vs. SQL Databases: Understand the Differences and When to Use

Data-focused professions

Jobs that would typically work with either structured or unstructured data include most types of data-related careers. Here are a few common roles that work with data:.

  • Data engineer: Data engineers design and build systems for collecting and analyzing data. They typically use SQL to query relational databases to manage the data, as well as look out for inconsistencies or patterns that may positively or negatively affect an organization’s goals. 

  • Data analyst: Data analysts take data sets from relational databases to clean and interpret them to solve a business question or problem. They can work in industries as varied as business, finance, science, and government.

  • Machine learning engineer: Machine learning engineers (and AI engineers) research, build, and design artificial intelligence responsible for machine learning and maintaining or improving existing AI systems.

  • Database administrator: Database administrators act as technical support for databases, ensuring optimal performance by performing backups, data migrations, and load balancing.

  • Data architect: Data architects analyze an organization's data infrastructure to plan or implement databases and database management systems that improve workflow efficiency.

  • Data scientist: Data scientists take those data sets to find patterns and trends, and then create algorithms and data models to forecast outcomes. They might use machine learning techniques to improve the quality of data or product offerings.

Read more: Your Guide to Data Science Careers (+ How to Get Started)

Build your skills in data analytics

Data analytics can help you in nearly every career field, but it can take you far in data science. Enroll in Google’s Data Analytics Professional Certificate and learn how to process and analyze data, use key analysis tools, and create visualizations that can inform key business decisions.

In IBM's Data Science Professional Certificate, meanwhile, you'll learn the tools, languages, and libraries used by professional data scientists, including Python and SQL, in as little as five months.

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