What Is Structured Data?

Key takeaways

Structured data is quantitative, meaning it consists of data that you can measure or count, and is easily searchable.

• Structured data is organized and is typically stored in tables such as those you’d find in an Excel file.

• Examples of structured data include customer names and email addresses, phone numbers, dates and times, transaction information, and more.

• You can use structured data to develop analytical insights into behavior trends, identify patterns to drive informed decision-making, maintain payroll processes, or process financial transactions.

Learn more about structured data, including how companies use it and how it differs from semi-structured data. To deepen your understanding of data analytics, consider enrolling in the Google Data Analytics Professional Certificate, where you’ll have the opportunity to learn analytical skills and tools such as data cleaning, visualization, spreadsheets, SQL, Python, and Tableau. 

What is structured data?

Structured data is quantitative data, meaning it includes data that you can measure or count. It is organized, typically formatted into tables, and easily searchable. This makes structured data ideal for performing analysis and combining with other data sets for storage in a relational database. 

Within a relational database, you can manage structured data using structured query language (SQL). SQL allows you to perform a variety of tasks with structured data, including inputting new data, modifying or deleting data, and querying to retrieve data. Sources of structured data include customer relationship and enterprise resource planning systems, online forms, web server logs, medical devices, and point-of-sale software.

Thanks to its highly organized nature, structured data is favorable for inputting, querying, and manipulating through machine learning algorithms. While structured data lacks flexibility and has its limits in terms of usability in comparison to unstructured data, more tools that don’t require advanced expertise exist for analyzing structured data. 

Structured data has a number of different uses. Running structured data through customer relationship management (CRM) software enables you to develop analytic insights into customer behavior trends and patterns to drive informed decision-making. When used in accounting, departments have the ability to use structured data to record and process financial transactions or maintain payroll processes.

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

How do companies use structured data? 

Companies use structured data as an efficient way to store information. Structured data is uncomplicated to understand and offers qualitative insight, which allows you to use machine learning algorithms when analyzing this type of data. For example, you can look at your transaction information to understand how many customers or sales your company had last week. 

Structured data examples

Structured data has predefined types, allowing for storage in tables with relationships between rows and columns. For example, this is commonly seen in an Excel file. Here are some other examples of structured data:

• Customer names and email addresses

• Product directories

• Phone numbers

• Dates and times

• Transaction information

• Inventory control

What is semi-structured data vs. structured data?

Semi-structured is a type of structured data that features characteristics of both structured and unstructured data. Semi-structured data has some of the same quantitative properties as structured data, but it also encompasses qualitative properties, which involve descriptive or categorical attributes. This makes semi-structured data more difficult to analyze; however, it offers greater flexibility in the type of information it contains. Examples of semi-structured data include the contents of an email, JSON and CSV documents, and NoSQL databases.

Related terms

• Relational database

• Data wrangling

• Metadata

• Unstructured data

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