Explains Understanding Structured Data vs Unstructured Data: The Similarities and Differences

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April 26
08:42 2022 Explains Understanding Structured Data vs Unstructured Data: The Similarities and Differences

SeedScientific reports the amount of data generated every day will probably reach 463 exabytes within the next three years. Most people cannot wrap their heads around how much this truly is. For example, if a person were to take every word ever spoken by humans and collect them, they would only accumulate five exabytes. However, all data is not alike. People must understand Structured Data vs Unstructured Data: Differences & Examples

Structured Data

Structured data, as the name suggests, has a structure to it. This data comes highly organized in a standardized format. As a result, machine learning algorithms can decipher the data, as can humans. IBM developed the programming language known as structured query language, or SQL, almost 50 years ago. Individuals and machines use this language to manage structured data. With the help of a relational SQL database, a person can do numerous things with the structured data, including input, search, and manipulate it. Here is a knockout post explaining more about structured and unstructured data. 

The Benefits and Drawbacks of Structured Data

Business users and machine learning algorithms can easily use structured data, manipulating and querying it. The user doesn’t need an understanding of the various data types and how they work. As long as they have a basic understanding of the data’s topic, they can access and interpret the information. Thanks to the age of structured data, users have more access to tools helpful in using and analyzing this data. 

However, users find the data lacks flexibility and usability because of the defined structure. Structured data is housed in data warehouses. These warehouses come with rigid schemas that maximize the available space but make it hard to change. Any change in the data leads to all structured data being updated. Users find this takes a lot of time and resources. 

Unstructured Data

According to, unstructured data comes with no identifiable structure and lacks conformity to any data model, which is why computer programs struggle to use it. There’s no organization or pre-defined data model, so the data doesn’t work with a conventional relational database. Data lakes often preserve raw data. 

Unstructured data accounts for 80 percent of enterprise data today. Referred to as qualitative data, the material comes stored in its original form. Entities only process unstructured data when they need it. This data includes emails, social media channels, media files, satellite images, sensor data, and text messages, among others. 

Pros and Cons of Unstructured Data

This data comes with many benefits, including the ability to collect large amounts. It’s scalable and portable. Furthermore, the data collected is useful in a variety of applications. 

However, entities find it hard to store and manage this data because of the lack of structure and schema. Many people find it hard to prepare and analyze the data, and they must use specialized tools to do so. Businesses will find they spend more to hold this data and indexing it remains a challenge. 

Harnessing unstructured data continues to be a struggle. Resolute AI works with companies to achieve this goal. Learn more today about converting unstructured data to structured material so one can make the most of the information they have. 

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