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Structured Data: Examples, Sources, and How It Works

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Data falls into two categories: structured and unstructured. Structured data is a type of information that has been organized in a way that makes it easily searchable and readable by data analysis tools, while unstructured data includes content like videos, emails, and images—data with no internal identifier to help search functions recognize it. Your business likely deals with both types of data. This article looks closely at structured data, which is the backbone of data analysis.

What is Structured Data? Types & Examples

Structured data, or quantitative data, is highly organized and readable by machine learning algorithms, making it easier to search, manipulate, and analyze. Structured data can include names, addresses, dates—fields that are recognizable and searchable by computers.

Compare this to unstructured data, which includes everything from social media posts to music files, emails, and images. It’s estimated that unstructured data makes up between 80 and 90 percent of all data generated globally.

Despite making up a much smaller percentage of existing data, structured data is considerably more valuable, as it’s much easier to handle and extract insights from.

The two types of data are not in opposition. In fact, structured data complements unstructured data, and enables you to find insights in your unstructured datasets.

For example, structured data records can hold unstructured data within them. Consider a form that offers questions with a list of answers available in a dropdown menu but also allows users to add free-form comments. The answers generated from the pick list are structured data, but the comments field yields unstructured data.

To some degree, most data is a hybrid of unstructured and structured data. Semi-structured data is a loosely defined subset of structured data, and includes the capability to add tags, keywords, and metadata to data types that were once considered unstructured data—for example, adding descriptive elements to images, emails, and word processing files. Markup languages such as XML are often used to manage semi-structured data.

How Does Structured Data Work?

You’ll typically find structured data in tables, rows, and columns, with each field containing a specific type of data corresponding to its category and value—think of a spreadsheet with specific headings for each column. This format makes it possible for search engine algorithms to read and understand data posted by individual sites.

Additionally, structured data enables both tools and individuals to quickly scan, organize, and analyze vast amounts of data for information.

Structured data can be generated in a number of ways. It can come from enterprise software, such as customer relationship management (CRM) systems, accounting programs, and other applications used in critical business operations. It can also be generated from online sources, including social media platforms and web-based surveys. Another source, while fairly limited in scope and application, is data coming from manual human input.

As for extraction from pre-existing material, structured data can be extracted from unstructured data by using a variety of business intelligence (BI) tools that rely on artificial intelligence (AI) and natural language processing (NLP) to transform massive amounts of unstructured data into structured data. You may come across structured data in various formats, depending on what makes it most accessible and easy to compare and contrast with other datasets.

The key to understanding structured data lies in its name: structure. It follows a specific format and organization, making it easier for machines to read and process data. This structure is usually predefined and consistent, meaning it uses the same format across all instances of the data.

Why Is Structured Data Important?

Structured data plays a pivotal role in numerous sectors, from business and finance to healthcare and education. It facilitates data analysis, enabling organizations to extract meaningful insights from their data. These insights can then be used to drive decision-making processes, optimize operations, and predict future trends.

Structured data can help organizations improve customer service by allowing them to easily access customer information. This can help them to quickly identify customer needs and preferences, allowing for more personalized experiences and building customer loyalty. It enables organizations to better understand their customers and target marketing efforts appropriately. Structured data also makes it easier for organizations to track performance metrics and identify areas where employees are eligible for improvement.

When it comes to search engine optimization (SEO), structured data can help you significantly boost a website’s visibility and reach. It allows search engines to better understand the content of pages, improving their chances of ranking higher in search results. Google, for instance, “uses structured data to understand the content on the page and show that content in a richer appearance in search results, which is called a rich result.” The aim is to encourage users to click the most suitable links, increasing click-through rates for websites with valuable content as a result.

Moreover, structured data is crucial for data interoperability. It ensures information is consistently formatted and easily exchangeable between different systems or applications. This is particularly important in today’s interconnected digital ecosystem, where the ability to seamlessly share and integrate data from multiple sources can streamline operations and foster deeper collaboration.

Where Does Structured Data Come From?

The two primary examples of where structured data is generated are databases and search algorithms.

The term structured data is often associated with relational database management systems, which organize data into one or more tables—also known as relations—of columns and rows. The structured query language (SQL) is used in the vast majority of relational databases.

In addition to relational databases, spreadsheets are common sources of structured data.

Whether it’s a complex SQL database or an Excel spreadsheet, because structured data depends on users to create a data model, you must plan for how you will capture, store, and access data. For example, will you be storing numeric, monetary, and/or alphabetic data?

To create a structured data standard for web-based applications, email messages, and forms of internet content, Google, Microsoft, Yahoo, and Yandex created, an open community that includes encodings such as RDFa (an HTML5 extension used in both the head and body sections of the HTML page), Microdata (an open HTML specification used to include structured data in HTML content), and JSON-LD (JavaScript Object Notation for Linked Data).

What Are Common Sources of Structured Data?

Unlike unstructured data, which will grow organically—often uncontrollably—and come from a wide range of sources, structured data is often created in controlled spaces and through planned methods. Usually, those can be divided into two categories: hardware and software.

Some of the most common sources include:


Databases are a common source of structured data. They store data in tables, rows, and columns, making it easy to search and analyze data.


Spreadsheets are another common source of structured data. They allow you to organize data in a grid of cells, with each cell containing a specific piece of information. This is often the data format that software and hardware products are programmed to produce, creating a wealth of structured data from scratch.


Sensors produce data, such as temperature readings or GPS coordinates, which are types of structured data. Sensors collect data in a structured manner, making it easy to analyze and interpret the data. These sensors may cover global networks, such as ones for weather forecasting, but there’s also smaller and individual sensors, like the ones used in logistics and transportation to track items.

5 Structured Data Examples

Not all data can be organized into a neat and easy-to-comprehend system; unstructured data can often be hard to quantify. Meanwhile, structured data is often numerical data or clear-cut strings of information, such as:

1. Dates and Times

Dates and times follow a specific format, making it easy for machines to read and analyze them. For instance, a date can be structured as YYYY-MM-DD, while a time can be structured as HH:MM:SS. Both can be transformed into different iterations of the same format so they become accessible to data scientists from different cultural and linguistic backgrounds.

2. Customer Names and Contact Information

When you sign up for a service or purchase a product online, your name, email address, phone number, and other contact information are collected and stored in a structured manner. This allows businesses to easily manage and analyze customer data, thereby enhancing their customer relationship management (CRM) efforts.

3. Financial Transactions

Financial transactions, such as credit card transactions, bank deposits, and wire transfers, are all examples of structured data. Each transaction comes with specific information in the form of a serial number, a transaction date, the amount, and the parties involved. This information is structured and stored in databases, enabling banks and financial institutions to track and analyze financial activities.

4. Stock Information

Stock information, such as share prices, trading volumes, and market capitalization, is another example of structured data. This information is systematically organized and updated in real time. It enables investors and traders to make informed decisions based on the latest versions of data collected from the market.

5. Geolocation

Geolocation data, such as GPS coordinates and IP addresses, is often used in various applications, from navigation systems to location-based marketing campaigns. This data helps businesses understand where their customers are located, thereby helping them tailor their services or products to specific geographical areas.

Advantages of Structured Data

Despite making up a small percentage of all data generated globally, structured data is highly sought-after due to its value and importance for business decisions. Some of its advantages include:

Simplifies Search and Analysis

One of the main advantages of structured data is that it’s easy to search and analyze. Its organized nature allows data analysis tools to quickly scan and interpret the data, thereby speeding up the data analysis process.

Enhances SEO

Structured data can enhance SEO efforts and enable search engines to better understand the content of webpages, potentially leading to higher search rankings and improved visibility.

Facilitates Data Integration

Structured data facilitates data interoperability, ensuring information is consistently formatted and easily exchangeable between different systems or applications.

Disadvantages of Structured Data

It’s important to also be aware of the various disadvantages and limitations of structured data in order to work around them and prepare for any shortcomings you may encounter.

Limited Flexibility

One of the main disadvantages of structured data is its limited flexibility. Since it follows a specific format and structure, it can be challenging to accommodate data that doesn’t fit into these predefined categories, therefore, limiting the data’s growth potential.

Time-Consuming to Set Up

Setting up a structured data system can be time-consuming and requires a significant amount of planning and coordination. You need to define the structure of the data beforehand, which can be a complex task, especially for large datasets.

Risk of Data Silos

There’s a risk of creating data silos with structured data, especially in large organizations where different departments may use different systems to store and manage data. This can make it difficult to share and integrate data across the organization.

3 Structured Data Characteristics

It’s often a fine line between structured and unstructured data, depending on its source, organization method, and the software and expertise you have on hand to handle it. However, there are a number of characteristics that are unique to structured data, such as the following:

Organized and Categorized

Structured data is organized. It follows a specific format and structure, making it easy for machines to read and process the data.


Structured data is consistent. It uses the same format across all instances of the data, ensuring data is consistently formatted and easily exchangeable.

Easily Searchable

Structured data is searchable. Its organized nature allows data analysis tools to quickly scan and interpret the data, thereby speeding up the data analysis process.

Bottom Line: Structured Data

Structured data is a crucial component of any company’s big data landscape. It’s organized, searchable, and easy to analyze, making it an incredibly valuable asset for businesses, organizations, and individuals alike. Understanding structured data is just the first step toward being able to use it to the fullest. The real value lies in how you adopt this data to drive decision-making, optimize operations, and enhance customer and client experiences.

Read next: Structured vs. Unstructured Data: Key Differences Explained

Featured Partners: Data Visualization Software

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