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

Key takeaways

Structured and unstructured data are the two primary data types used today. Each is sourced and collected in different ways, living on different types of databases, so their differences are important for data professionals. At a glance, here's what you need to know:

• Structured data is typically quantitative data, like transaction records, sales revenue, or test scores, that can be easily organized and searched.

• Unstructured data is generally qualitative data, such as text, medical images, video, and sound files, that may contain insights but isn't easily organized.

• Data can be used for a wide range of different purposes, including for data modeling, data analysis, and business intelligence purposes, among many others.

Read on to learn more about these two foundational data types, 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. Afterward, if you want to develop foundational data skills, consider enrolling in the Google Data Analytics Professional certificate.

What is 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.

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” 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:

What is an example of 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.

Key differences: Structured data vs. machine-readable data

Machine-readable data formats like CSV, RDF, and JSON are designed for use by devices and machines, making them difficult for humans to interpret. In contrast, structured data is more accessible and can be understood without extensive knowledge of data types.

Pros and cons of structured data

Structured data offers plenty of benefits, but it’s not without limitations. To help you get a better idea of whether structured data is right for your own project goals, consider the following advantages and disadvantages:

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

• Amazon DynamoDB

• Apache Spark

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 to 90 percent 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, data warehouses, NoSQL databases, and applications. Today, this information can be processed by artificial intelligence algorithms and delivers huge value for organizations.

What is an example 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 manipulated 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, you’ll find numerous pros and cons to using unstructured data. Some of the advantages and disadvantages of using unstructured data include:

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

• Amazon S3

• Apache Spark

Data-focused professions

Jobs that would typically work with either structured or unstructured data include most types of data-related careers. Below 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 data analytics skills today

Data analytics can help you in nearly every career field, but it can take you far in data science. Learn more about data and build toward your career goals with these resources from Coursera:

• Watch on YouTube: Data Analytics Careers – 7 Roles to Explore

• Take the mini quiz: Which Data Analysis Course Should You Take? Find Out in 1 Minute

• Bookmark for later: Data Analysis Terms & Definitions

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