Structured Vs. Unstructured Data: What’s The Difference?

Structured Vs. Unstructured Data: What’s The Difference?

structured vs unstructured data

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All forms of data have a primary goal, which is to relay some kind of information. For instance, a group of numbers or a list of words clustered together is of no use to the reader unless there is an understanding of the purpose the data presented is to serve. 

 

Data can be presented in a structured manner or unstructured manner. Previously, structured data was the most accessible form of data which information can be gleaned from. However, with the advent of more complicated data management systems, there is a lot of information made available from interpreting unstructured data. 

 

This article will break down the different formats of data; both structured and unstructured. We will cover what exactly structured and unstructured forms of data are, give examples of each and also expound on their key differences as well.

 

This analysis will further enlighten you on which form of data can provide useful information and perhaps prepare you with all you need to take advantage of the different formats for your business or learning endeavor.

What Is Structured Data?

Simply put, Structured data is a form of data that has been presented following a model that has already been established in advance.

 

A good example is a flight ticket. A flight ticket contains information such as the flight destination, time of departure, time of arrival, and seat number. These are structures that have been predetermined to allow easy analysis of data presented on the ticket.

 

Structured data is commonly presented as tables with rows and columns. The rows and columns must however have a certain relationship to each other to be of any importance.

 

To manage this data, a programming language tool must come in handy. Structured Query Language (SQL) is a vital programming language tool developed for handling structured data 

Benefits of Structured Data

When data is given a certain model to follow, it can be advantageous for a host of reasons. Some of these are: 

 

Ease of Use

It is much easier to analyze data that is organized into pre-prepared categories. This is because there is an understanding of the relationship between various data types.  

 

Machine learning tools and humans alike can easily query data.

Availability of Tools

Since structured data has been the format that data has existed in from the early days, there have been tools created to analyze such data. These tools have been improved upon and made more accessible over the years.

Storage Advantage

Structured data is stored in data warehouses, which stores organized data from multiple sources that can be used to retrieve important information. 

 

Data warehouses are convenient because they don’t typically require huge storage capacity and data is easily accessible.

Disadvantages of Structured Data

Some of the disadvantages of structured data include:

Limited Functionality

There aren’t many uses for structured data as opposed to unstructured data because there is already a predetermined model to follow.

Inflexible Storage

Structured data is stored in databases which usually have strict structures. This can be problematic if your fixed schema requires an update.

Examples Of Structured Data

Data that is structured includes items like:

  • Date 
  • Time 
  • Phone number
  • Email address 
  • House Address 
  • Credit card information etc.

What Is Unstructured Data?

Unstructured data is data that does not have any pre-arranged format. Because of this, interpreting this form of data is quite tricky.

While structured data can be represented as the time a text message is sent, unstructured data is the content of the text message. The content of the text message is considered unstructured due to its variability. It may include numbers, text, and even images. 

The content of unstructured data is subjective and requires more advanced methods to retrieve information from the data. Therefore, programming languages with the ability to recognize natural language processing (NLP) have been developed to achieve this.

Benefits of Unstructured Data

About 80% of the data available is reported to be in an unstructured format. This leaves the potential for untapped information at our fingertips. Hence, emphasizing the benefits of unstructured data.

Unstructured data is available in its raw and original format. Because of this, there is the freedom to use this data to suit whatever purpose for which it is needed. 

Additionally, because this data is also accessible in multiple formats, this increases the potential usage of the data.

Disadvantages of Unstructured Data

The disadvantages of unstructured data include:

Difficult to Analyze

Unlike the ease of analysis characterized by structured data, unstructured data is quite difficult to analyze. A lot of energy must be put into a careful analysis of unstructured data to glean anything of importance to the searcher.

Consequently, A data science professional would therefore be needed to conduct the analysis to connect the variables and derive the relevant information for a business.

Poor Selection of Tools

There aren’t many tools available for the analysis of unstructured data due to its complex and variable formats. 

Additionally, Unstructured data has a lot of catching up to do in terms of analytical tool availability compared to structured data.

Huge Storage Space Required

Large amounts of storage space are needed to hold all the unstructured data because there is so much of it.

As a result, purchasing the high quantity of storage space needed costs a lot of money.

Examples of Unstructured Data

Unstructured data comes in a variety of different forms. Listed below are a few examples:

  • Text 
  • Books 
  • Video files 
  • Audio files 
  • Social media captions 
  • Survey responses 
Structured Vs Unstructured Data

Structured vs Unstructured Data: Key Differences

Data Formats

Structured data exists as text and numbers which can be adapted to a tabular format with rows and columns that follows a rigid, predetermined schema.

Popular examples of these formats are Excel spreadsheets, CSV and XML, and SQL databases.

As opposed to unstructured data which exist in various forms. Because unstructured data is stored in its native form, it has a wide array of formats possible. Some of these are images (JPEG or PNG),  audio (MP3 or WAV), video ( MP4 or WMV), PdF and so much more.

Tools

For structured data, some popular examples are:

PostgreSQL

PostgreSQL: This tool is an open-source database system that supports queries in SQL and JSON format. It is commonly used to store data for mobile,  web, and analytics functions.

SQLite

SQLite: SQLite employs the use of a transactional relational database engine that is serverless, and self-contained.  Simply put, it is a SQL database that has many tables, indices, triggers, and views contained in a single disk file.

OLAP (online analytical processing): OLAP has the ability to analyze multi-dimensional data while still maintaining high speed.

Unstructured data, however, require more specialized tools for data analysis.

A few examples of such include:

mongoDB

MongoDB: MongoDB is a document-focused NoSQL database. It can be used for storing large volumes of data. Data is stored in different documents with varying fields that can present data in a hierarchical format and represent more complex structures with ease.

Amazon dynamoDB

Amazon DynamoDB: this tool is a serverless NoSQL database service. This tool has the advantage of recovering and storing huge amounts of data and serving any query regardless of its large volume.

Microsoft Azure

 Microsoft Azure: Microsoft azure is a cloud computing tool that helps you gain access to cloud services. These services include data storage and data transformation depending on your immediate need.

Because of the complex nature of these tools, a data scientist with proven expertise in the usage of these tools and familiarity with the related topic would prove to be a useful asset.

Storage

Structured data is stored in data warehouses. This is an organized database where data is arranged in tabular form. The rows and columns have an already established relationship which allows important information to be gathered. This form of storage requires a minimal amount of space.

On the other hand, unstructured data is stored in lake houses. Lake Houses are the exact opposite of data warehouses. In lake houses, a huge amount of data is stored in its original format without any form of organization. Because of this, larger storage is required for unstructured data.

Data Warehouse vs Data Lake

Uses

Structured and Unstructured data have a variety of uses and have been implemented into our daily lives to ease certain processes.

Structured data

  • Inventory control: different models have been created to manage data needed for inventory in different companies.

  • Accounting: Banks handle vast amounts of data daily and require a predetermined model to organize and process these data. 

  • ATMs: All the data entered into an ATM follows a fixed and pre-prepared scheme. Hence, it is a good example of how structured data has been used.

Unstructured Data

Chatbots: Natural Language Processing helps companies understand their customers’ search queries that have been submitted to the chat box. This information is then sorted and delivered to appropriate personnel to provide the right answers.

 

Data mining: Mining Unstructured data gives business owners the chance to examine customer buying patterns and habits to better serve their customers. 

 

Interestingly, text analytics and sentiment analytics allows data science analysts to assess feedback on marketing campaigns by tracking comments on social media conversations to determine positive and negative reactions. This can be used to determine the success or failure of a marketing campaign, assess the reason why and adjust to accommodate future problems.


Image recognition: shopping websites improve the shopping experience by implementing image recognition which allows shoppers to submit the image of their desired product.

Sources

Structured data can be sourced from different locations. A number of them are listed below.

  • Online forms 
  • Excel spreadsheets 
  • Relational database systems
  • Medical devices such as thermometers or blood pressure machines

 

While unstructured data sources are:

  • Images 
  • Videos 
  • Websites 
  • Surveys 
  • PDF documents 
  • Sensor data (eg. from traffic and weather sensors)
  • Scientific information 

Semi-Structured Data, A Bridge Between Both

Semi-structured data lies between structured and unstructured data. It is neither fully structured nor fully unstructured. 

 

Semi-structured utilize tags commonly referred to as ‘metadata’ that are used to define some characteristics present in the data. This helps to classify semi-structured data into a hierarchy that can be used for further analysis. 

 

Semi-structured is useful because a significant amount of data available is in a semi-structured format. This implies that an abundance of information can be retrieved from this data. 

 

When compared to unstructured data, this can be advantageous because interpreting unstructured data is a long and arduous process. With semi-structured, however, data takes less time to process, less data for storage and the process of extracting useful information is simpler.

Examples of Semi-Structured Data

Semi-structured data exist in varying forms and some examples of semi-structured data 

include:

  • Emails 
  • Web pages 
  • Machine markup languages such as XML, CSV, or JSON 
  • Zipped files 
  • Binary executables 

Semi-Structured Data Tools

Some tools can be used to store and extract information from semi-structured data.

 

  • OEM: OEM means Object exchange model. It is a graph-based model that stores and indexes data in a manner that is easy to locate. It can also be used to exchange semi-structured data.

 

  • XML: this tool stores data in a hierarchical format that allows the indexing of semi-structured data. It works by assigning tags and attributes to semi-structured data. XML can also be used to exchange semi-structured data.

 

  • RDBMS: Another tool that can be used to store semi-structured data is the Relational Database Management System. This system works by creating a relational schema for the semi-structured data that can be represented in a table.
Structured Vs Unstructured Data

The Line Between Structured And Unstructured Data

The difference between structured and unstructured data essentially lies within its structure. Simple, right?

 

Structured data follows a predetermined format that organizes the data into categories that can be easily analyzed. hence, simplifying the information extraction process.

 

On the other hand, Unstructured data follows no format laid for it beforehand. It is data that exists in its natural state. This form of data requires more effort in obtaining relevant information from it.

 

Both forms of data can prove useful in any business endeavor and a good knowledge of how to retrieve relevant information from both structured and unstructured data will set you on a great path to success.

Robert Battle CPA

Robert Battle CPA

Founder of QCKBOT and data analyst extraordinaire. Looking for trends and testing theories led this financial guru into the world of SEO. Empowered by the ability to take businesses to the next level through organic search, he's on a mission to upend traditional B2B consulting via data-driven high-impact results.

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