Structured vs. Unstructured Data (Plus Examples)

By Indeed Editorial Team

Published March 25, 2022

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

When working with data, it's important to understand the differences between the two types of data filing and how they affect your capabilities with the data. Both structured and unstructured data have benefits that may make them preferable for your needs at different times or in different situations. Understanding the benefits of each format may help you choose the right form of data storage when processing information. In this article, we discuss what structured and unstructured data are, how they differ and provide examples of both types of data formats.

What is structured data?

Structured data is data that a user creates according to a predefined format and structure. Structured data includes the same information for each entry within the database, providing consistency from one entry to the next. This provides structure which makes it easy to sort.

Related: 9 Types of Data Structures in Software Engineering

What is unstructured data?

Unstructured data is data that a user creates without a defined entry storage format. Information provided in one entry in a database may be significantly different from that provided for another entry. This offers more variety in potential information gathered while making sorting and organization a more challenging task.

Structured vs. unstructured data

Key differences between structured data and unstructured data include:

Analysis

When performing analysis on a set of data, structure may help to simplify the process. Because structured data stores the same information in the same format for each piece of data within a set, it can make it easier to compare, find averages and perform other calculations. When working with unstructured data, you may first take time to identify key elements in the data and create a uniform set from that information in order to make comparisons and other analysis between entries more functional.

Related: Data Management Skills: Definition and Examples

Collection

When gathering information for a dataset, using an unstructured data approach may be quicker. Adding data to a set using a structured format requires consistency and may result in higher demands when gathering the data in order to ensure it aligns with the format. Collecting unstructured data allows you to retrieve the data however it is available, allowing you to more quickly fill your dataset.

Machine learning

Structured data is highly beneficial when working with algorithms and machine learning. Structure allows artificial intelligence to analyze the data and more easily identify what it means and make more informed extrapolations. With unstructured data, artificial intelligence may struggle to relate similar elements from different entries because they are not flagged as comparable.

Storage

Storing unstructured data provides more flexibility. Because structured data needs to maintain the same format, it requires more disciplined rules for storage. This may make it easier and quicker for you to store data in an unstructured data format than in a structured format.

Related: What Are the Different Types of Database Management?

Tool use

When providing data for automated practices, it can be beneficial to use structured data. As with artificial intelligence, the structure makes it easier for the devices receiving the data to parse it and identify key information. If you choose to use an unstructured format, an automated device may struggle with unstructured data and may require additional programming to learn to interpret the unstructured data.

Versatility

Unstructured data offers more versatility in its application. The freedom to record information in different ways may allow you to attain more comprehensive data with an unstructured approach. This can have the effect of increasing the number of ways you identify to use the data provided within your unstructured dataset.

Examples of structured data

These examples of structured data show how a company may use a structure format and the benefits it provides:

Employee records

It's common for a company to store employee records in a structured database. This ensures that the company gathers important information such as their name, position and payroll information from each employee. Keeping this information structured makes it easy for those within the company to access it as needed when checking an employee's records and facilitates more efficient daily operations by company staff.

Related: Types of Database Languages and Their Uses (Plus Examples)

Online usage statistics

Online platforms often provide detailed tracking information on their users. This allows the platform or other businesses using the platform to gain insights into the behaviors and demographics of users. The online platform is likely to store this information in a structured dataset, in order to improve its functionality and ability to sort. For example, a social media network may track user interactions along with key demographic information which clients can use to target advertising on the platform.

Sales data

A structured database of sales data can be valuable to a company seeking to improve its sales performance. Structuring information such as client information, product information and sales associate performance allows individuals within the company to sort and analyze the data in search of trends. This may help the company identify its best demographics, its most popular products and the highest-performing employees. A structured approach helps the company to effectively identify these valuable trends.

Examples of unstructured data

These examples of unstructured data can help to show the benefits offered by an unstructured approach:

Customer emails

Companies can gain valuable insight into the behavior and thoughts of their clients or customers by receiving emails. When a customer sends an email, they're free to include information as they see fit. This allows for more expansive response options for customers, which may result in more detailed information the company can use to guide decision-making.

Satellite imaging

When planning for a new development project, a developer may use satellite imaging of an area to identify areas of interest and plot the structure. The photographic images don't come with consistent data in each photo, as the types of elements contained can vary greatly by the area photographed. Although the company may choose to add structure to this data in the future, such as adding tags for key elements in different regions, the unstructured initial images provide value by showing a full accounting of the area, not just the elements which fit into specified categories.

Social media posts

Companies can gain value from accessing posts made on social media about their products or services. Because users are free to post as they see fit, social media posts do not arrive as a structured set of data. The enhanced freedom available to users may allow them to express themselves and provide unique information the company may not otherwise gain by sending out structured questionnaires or other forms of customer feedback assessment.

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