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 exist for analyzing structured data that don’t require advanced expertise. 

Structured data has a number of different uses. Running structured data through customer relationship management  (CRM) software enables you to develop analytic insights to 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.

Examples of structured data

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

Structured data vs. semi-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

Learn more about structured data with Coursera

If you’re ready to start a career in data analytics, consider earning a Google Data Analytics Professional Certificate on Coursera. With this program, you have the opportunity to earn an employer-recognized certificate and develop job-ready skills in a number of areas, such as SQL, data cleaning and visualization, R programming, and more.