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

Written by Coursera • Updated on

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

[Featured image] Two data scientists examine graphs and charts on a large white board

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.

This article will guide you through structured and unstructured data and their differences.

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
Quantitative
Difficult to search
Many data formats
Qualitative
StorageRelational databases
Data warehouses
Data lakes
Non-relational databases
Data warehouses
NoSQL databases
Applications
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. 

Examples 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:

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Understanding Structured Data

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

Three main benefits of structured data are:

  • It’s easily searchable and used for machine learning algorithms.

  • It’s accessible to businesses and organizations for interpreting data.

  • There are more tools available for analyzing structured data than unstructured. 

Some drawbacks include:

  • It’s limited in usage, meaning it can only be used for its intended purpose.

  • It’s limited in storage options because it’s stored in systems like data warehouses with rigid schemas.

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.

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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 content such as emails, images, videos, 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 analysts can adjust their calculations and investment decisions.

Pros and cons of unstructured data

These are some benefits of unstructured data:

  • 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.

  • Within definitions, unstructured data can be collected quickly and easily.

These are the drawbacks of unstructured data:

  • It requires data scientists to have expertise in preparing and analyzing the data, which could restrict other employees in the organization from accessing it.

  • Special tools are needed to deal with unstructured data, further contributing to its lack of accessibility.

Structured and unstructured data tools

Structured data is typically stored and used with relational databases and data warehouses supported by SQL, which includes OLAP, MySQL, PostgreSQL, Oracle Database, and more.

Unstructured data is typically supported by flexible NoSQL-friendly data lakes and non-relational databases, such as MongoDB, Hadoop, Azure, and more.

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

Related careers

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.

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.

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Written by Coursera • Updated on

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