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Structured and Unstructured Data: 7 Key Differences

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Methods to collect Quantitative Data cvr
Table of Contents

Introduction

Data comes in many forms, from different sources, and can be used in many ways to solve problems. In the realm of data science, data can be categorized into two different categories: Structured data and unstructured data. To understand the difference between structured and unstructured data, you must first understand what each term means.

Structured data and unstructured data are two of the most fundamental forms of data within databases. Structured data is information that can be easily searched, indexed, and analyzed, while unstructured data lacks organization and fails to create patterns or relationships between different pieces of information.

In this article, let’s explore seven key differences between structured and unstructured data, including how they are defined and their respective uses in today’s world.

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What is structured data?

Structured data is information that has been prepared in a specific, standardized, to the point format and It is easily accessible in relational databases. Structured data makes for almost 20% of all data available on the planet. It Is managed by SQL (Structured Query Language) and can be stored in a table with rows and columns in a SQL database. It is a defined type of data. 

Structured data requires less processing by human beings to make it useful and it is easy to analyze by both humans and algorithms as it is often quantitative. Data warehouses are frequently used to store structured data. Furthermore, structured data often does not need huge storage space. 

Examples of structured data can be machine-generated structured data found in the form of sensors, weblogs, etc. Human-generated structure data is mostly the information of the customer such as Name, address, or even spreadsheets.

Advantages of Structured Data 

One of its biggest advantages is that structured data is easily found and organized. It becomes easier to analyze trends or patterns in the business with a well-organized, clean data set in place.

  • It has fewer errors than unstructured due to having a very clear set of rules defining its content and format 
  • There are no compatibility issues since everything is structured strictly by following standard protocols that make it readable across platforms.
  • Any user can open and manipulate structured data without any professional training or tools.
  • Structured data tends to be accurate, less time-consuming, and easier to comprehend.
  • Structured data provides added security because all sensitive information is encrypted before being stored in databases. 

In addition, you can also make better-informed business decisions because it’s easy to compare different sets of data from various points in time. If a certain strategy isn’t working out well as you expected, you’ll know immediately because there will be measurable data to back up your suspicions. All in all, there are many distinct advantages to using clean, organized data sets.

Limitations of Structured Data

  • Limits flexibility of the data. 
  • If any modifications are necessary, the complete structured data must be updated to meet the new requirements. This is both time-consuming and expensive.

What is Unstructured Data?

Unstructured data refers to data that has no discernible order and is not processed. It does not have a predefined format and can be found in diverse formats. According to a report 80 percent of data is unstructured. It may consist of words, images, audio, or video—anything not pre-defined by a database and stored in an organized manner, and hence this type of data can be collected quickly and easily. 

Examples include phone recordings and social media interactions (Facebook posts, tweets, Instagram photos). It is also known as free text. 

An example of unstructured data would be a bunch of photos with no titles or descriptions. Even though they aren’t in any particular order, you can still find and access them, but it may take some effort on your part. 

In business, an example would be an inventory database that isn’t segmented by region, season, etc. It’s simply a list of products and their quantities at certain locations.

Advantages of Unstructured Data 

  • No limitations on what can be included — so in theory, it’s more flexible than structured data. 
  • Potentially higher accessibility. 
  • Probably less expensive to produce.
  • Unstructured data has an advantage when it comes to scaling (the ability to handle huge amounts of information).
  • Unstructured data is easier to update and modify.
  • In cases where there’s a lack of standardization, unstructured data may be best to apply.

Limitations of Unstructured data 

Just as there are benefits to unstructured data, there are also drawbacks. Unstructured data is tough to access and mine for insights, making it hard to use in several business scenarios. Here are a few Limitations of unstructured data

  • Lacks standardization
  • Data inconsistency
  • Higher potential for errors 
  • Difficulties with retention and storage
  • Indexing an unstructured database is challenging

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7 key differences between structured and unstructured data

Structured data has been around for decades, while unstructured data (the bulk of it) is a new phenomenon, mostly because of recent technological advancements that have made capturing and storing unstructured data much easier than before. 

Here are seven significant distinctions between structured and unstructured data.

Parameters 

Structured Data

Unstructured Data 

   

Data model

Structured data can be found in a predefined format.

Unstructured data does not have a predefined format and can be found in diverse formats (Native Format).

Storage 

Needs less storage space and can be stored in data warehouses. 

Requires huge storage space and can be stored in data lakes. 

Analysis 

Structure data can be analyzed by average business users.

Unstructured data needs data science expertise.

Data Nature 

Structured data is quantitative.

Unstructured data is qualitative.

Databases 

Can be stored in a SQL 

(Relational database).

Can be stored in NoSQL (Non-relational databases). 

Format 

It is found in a few user- readable and standardized formats such as CSV and XML

It is found in original, native format such as PDF documents, Social media posts, Images. 

Characteristics

  • Easy to search 
  • Can be found in the text 
  • Difficult to search
  • Includes text, videos, images, etc.

What is semi structured data?

Semi-structured data, on the other hand, has some degree of structure but is not completely delineated. Semi-structured data is unstructured but includes metadata that defines specific qualities. The metadata allows the data to be cataloged, searched, and analyzed more effectively than completely unstructured data. Semi-structured data can be viewed as a bridge between structured and unstructured data. Semi-Structured data is typically found in documents, spreadsheets or emails

That concludes the coverage of structured data and unstructured data. In a nutshell, structured data is information that is easy to retrieve in a consistent format. Unstructured data is highly variable; it consists of documents, texts, and images. Nonetheless, both are important for businesses that focus on improving the effectiveness of business intelligence.

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