Data Types and Applications: Structured vs Unstructured Data

Data Types and Applications: Structured vs Unstructured Data

Hanna Kleinings


Customer Operations Manager

December 2, 2022

Identifying insights within your company’s data is no easy task.

Most companies use data analytics tools to make business decisions, but these are often limited to structured data, such as spreadsheets or SVG files. If you are not pulling insights from your unstructured data - emails, comments, documents, images, etc.-  you are missing out on key insights, as most data generated by businesses falls under this category.

But with next-generation data analysis tools, you can immediately start uncovering insights that were hidden under large volumes of unstructured data.

In this article, we’re going to explain the differences between structured, unstructured, and semi-structured data, and show you how each of these is used in AI tools to provide value for your business.


Structured vs Unstructured vs Semi-structured data overview, differences and key characteristics
Structured Data vs Unstructured Data vs Semi-Structured Data

Structured, unstructured, and semi-structured data 

Let’s start off by clarifying how unstructured data differs from structured data.

What is structured data?

Structured data is organized, easy-to-understand information that follows a set data model. It's often stored in databases like Excel files or SQL so it can be accessed and understood by both humans and programs. 

Even though it only comprises around 20% of the data worldwide, it is the foundation of the widely known ‘Big Data’.

Structured data examples

CRM (customer relationship management) systems store customer information such as name, address, email, mobile number, and date of birth - all examples of structured data. Each of these pieces of data is formatted in an organized and consistent way from one customer to another meaning the systems are designed to work with predetermined information structure.

Standard systems used for CRM, inventory control, order management, and others, also handle structured datasets.


Structured data example. Structured data often comes in rows and columns
Table databases are an example of structured data


The purpose of a database, such as this table structure, is to store information in an organized way so that it can be easily accessed, updated, or deleted. These databases also offer a domain-specific language – the famous SQL - that facilitates data manipulation. 

What is unstructured data?

In contrast to structured data, unstructured data is information that doesn’t have a predetermined data model and can’t be stored in a traditional relational database. 

Long text, images, and videos are generally categorized as unstructured data. Emails, social media content, chat messages, and web forum content are some of the most common sources of unstructured data today’s companies have to deal with. Business documents like legal contracts and customer survey questionnaires contain huge amounts of this type of data.

Unfortunately, because it doesn't follow a designated structure, unstructured data can be hard to analyze if tackled manually. This often leads businesses to simply ignore this type of data, missing out on valuable insights. 

However, thanks to the development of AI-powered tools like Levity, it is now possible to classify unstructured data faster and more efficiently than ever before. These tools are redefining the way we process this type of data and allow us to get the most out of unorganized data with minimal effort. 

Unstructured data examples

Let’s look at two unstructured data examples.

A standard email has a sender, one or more receivers, sent time, and a message body, Sometimes, it includes one or more attachments as well. The senders, receivers, and time sent, fit into a structured data model but the message body contains unstructured information. If we don't read the entire email, it's impossible to determine its meaning and context.

Social media content is another example of both structured and unstructured data. Some information such as username and time of activity is structured data, but analyzing this information will give limited actionable insights. To gain a real understanding, we have to tap into the content which includes free-form text, pictures, and often videos. This type of information is inherently unstructured and does not follow a structured data model.

Unlike structured data, unstructured data cannot be arranged into tables with fixed columns and rows quite so neatly. It is generally stored in so-called ‘data lakes’ as raw information in their native format.

Unstructured data example
Online reviews are an example of unstructured data

Structured vs unstructured data in next-gen tools

Classifying unstructured data manually is a time-and-resource-consuming activity. Traditional data analytics tools are not capable of handling the volume and complexity of this data and businesses were left with no scalable solutions

next-generation data analytics tools are changing the game on this front. Built on AI and ML, these tools can analyze unstructured data and provide valuable insights. One such tool is Levity. Let’s take a closer look.

Classify your unstructured data with Levity!

With Levity, even complex data such as images, documents, and free-form text can be automated. Connect your existing systems to AI without writing a single line of code.

Get started for free!

Classify your unstructured data with Levity!

With Levity, even complex data such as images, documents, and free-form text can be automated. Connect your existing systems to AI without writing a single line of code.

Get started for free!

Social listening

There are about twenty major social media platforms and hundreds of smaller players, and chances are, your customers are actively discussing your products on more than one of them.

Due to the sheer volume and unstructured nature of social media data, manual data analysis is almost useless. AI social listening tools can track and analyze conversations on social media platforms. By doing this, businesses gain insights into what customers care about, what problems they’re experiencing, and what solutions they’re looking for. As well as this social listening can help businesses identify potential brand advocates and influencers.

AI-powered tools, like Levity, can automatically uncover the most important insights from social media data, saving businesses time and effort. Automated analysis of social media data can also help identify trends and patterns that would otherwise be difficult to spot manually.

Social listening with AI- powered tools
Automate your social listening with Levity

Content moderation

If you're a business that relies on interaction from users, you know that content moderation is essential. Unethical user behavior, such as harassment, discrimination, and hate speech can ruin the experience for other users and damage your brand.

Thankfully, AI-powered tools can automatically identify this type of content and block it from being seen by other users. This helps protect your brand and keeps the user experience positive, as well as improving brand loyalty among legitimate users. 


Levity helps you classify and moderate content on your website
Automate your content moderation with Levity

Categorizing support tickets

More and more users tend to interact with businesses via online mediums. Unstructured data analytics can help you build a scalable online customer support system by automating the categorization of support tickets.

Using a system like this, you can easily identify which type of problem your customer is facing and route the ticket to the appropriate department. 

With Levity, you can train your AI Block and set up a workflow to classify all of your incoming support tickets according to the custom categories that fit your needs best, as well as being able to send them to the right agent through your CRM of choice.

next-gen AI powered tools help you take control of your support tickets management
Categorize your incoming support tickets with Levity

Tag email attachments

Tagging email attachments can be troublesome and tedious. 

Document classification can categorize emails by attachment types, such as CVs, invoices, or spreadsheets, and routes these to the appropriate team or department - meaning documents get to the right department the first time around.



Automatically tag your email attachments with Levity


Classify email campaign responses

Every day, workers receive dozens of emails - all of them containing unstructured data. You’ll get even more if you send out marketing campaigns regularly. Classifying responses to these campaigns can take hours of your day.

It is impossible to effectively process responses to outbound emails without explicit prioritization. Manually responding to uninterested prospects takes away time from more important leads. If answers aren't classified correctly, it's hard to figure out if email marketing is effective.

Tools like Levity use Machine Learning to tag emails depending on their content, such as 'priority,' 'confidential,' 'personal;' or, by customer segments and teams that should respond to them.


Automatically categoize your email responses with Levity

Start classifying your unstructured data and reach the next level of productivity

In the digital era, most organizations agree that successful business revolves around making data-informed decisions. But that doesn’t mean that all companies know how to do it effectively.

Some businesses are still using traditional data analysis tools, which have limited capabilities. If companies are incapable of digging into unstructured content like customer reviews, comments, images, or videos, they miss out on crucial business insights.

So, what’s the solution? Using a next-gen tool powered by AI that analyzes all three data types – structured, unstructured, and semi-structured.

Looking for more possibilities? Take a look at our full list of use cases.

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