Structured vs Unstructured Data: What’s the Difference?

Data files fall into two main types—structured and unstructured. Understanding the differences between these types of data is essential for data professionals because how and where they source, collect, and store it depends on the type of data they are working with.

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

Spend some time learning the answers to these questions and many more. Find out who works with structured and unstructured data in the real world and what tools are available to manage them.

Structured vs unstructured data

The main difference between structured and unstructured data is the fact that structured data has a definition and is searchable. This includes data like dates, phone numbers, and product SKUs. Unstructured data is everything else, which makes it more difficult to categorise or search. Photos, videos, podcasts, social media posts, and emails fall under this category. In fact, most of the data in the world falls under the unstructured data category. The following chart highlights the key differences.

What is structured data?

Structured data is typically quantitative data that you can organise and easily search. To work with the data, you can use the programming language Structured Query Language (SQL) in a relational database to “query” to input and search within it.

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, you can use structured data for a wide variety of applications and purposes across different fields and industries.

• 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 goes into a database for storage.

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

• Tracking inventory: You can use details like physical dimensions, colour, and style characteristics to organise inventory. This streamlines inventory monitoring so you know what products you have.

Pros and cons of structured data

You can find 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:

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 have 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 semi-structured data.

What is unstructured data?

Unstructured data includes every other type of data—anything that is not structured. Approximately 80 per cent of worldwide 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, artificial intelligence algorithms can process this information and deliver huge value for organisations.

Examples of unstructured data

Although you may find structured data easier to work with, unstructured data provides interesting insights you can use in the real world. Some examples of uses for unstructured data include the following:

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

• Market predictions: You can manoeuvre data to predict changes in the stock market so that analysts can adjust their calculations and investment decisions.

• Product marketing: You can monitor customer buying habits and searches to predict products and services that may interest them.

Pros and cons of unstructured data

Just as with structured data, using unstructured data has numerous pros and cons. The chart below highlights some of the advantages and disadvantages.

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

• Mapreduce

• Azure

Data-focused professions

In many data-related careers, you will typically work with either structured or unstructured data. Here are a few common roles that work with data:

• Data engineer: Data engineers design and build systems for collecting and analysing 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 organisation’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 analyse an organisation'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.

Next steps

Data can come in two main types: structured and unstructured, each with distinct characteristics and storage methods. Understanding their differences and applications is crucial for data professionals to effectively manage and utilise data in various industries.

Enrol in Google’s Data Analytics Professional Certificate and learn how to process and analyse data, use key analysis tools, and create visualisations 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.