Structured vs Unstructured Data – What is the Difference?

What is the difference between structured and unstructured data? Let us understand the concepts of these two types of data in this article.

The amount of generated data globally is growing day by day. An estimate suggests that only 20% of this data is structured, while the rest is unstructured. To understand the structured vs unstructured data, let’s define them first.

Content

• What is Structured Data?
• What is Unstructured Data?
• Structured Data Types
• Unstructured Data Types 
• Semi-structured data
• Differences Between Structured and Unstructured Data
• How Do Structured and Unstructured Data Work Together?
• Conclusion – Structured vs Unstructured Data

What is Structured Data?

Structured data is the information usually stored in relational databases. The structured data is arranged in records (rows) and columns (attributes).

This allows structuring in a table format. Here, each category has a title, allowing their easy identification. In most cases, these are text files.

Structural data uses SQL programming language to manage them. This allows making database queries and extracting the desired information. Common applications of relational databases with structured data include airline reservation systems, inventory control, sales transactions, and ATM records.

Since they are structured, they are easier to manage and allow better prediction. We can easily process structured data by any data mining tool.

Here are some of the examples of structured data –

• Names
• Addresses
• Credit card numbers
• Stock information
• Geolocation

What is Unstructured Data?

Unstructured data is the majority of the relevant information available. It is usually binary data that has no identifiable internal structure. They have an internal structure but have no predefined schemas or data models.

We are talking about a disorganized set of several objects. These objects have no value until we identify and store them in an organized manner.

We cannot store unstructured data in a traditional database. They need non-relational or NoSQL databases. But once organized into files, these can be categorized to get information.

Unstructured data can be textual or not, besides, it can be generated by both humans and machines.

Some of the examples of unstructured data include –

• Text files – word processing, spreadsheets, presentations, email, logs
• Social networks: Data from Facebook, Twitter, LinkedIn
• Websites: YouTube, Instagram, photo sharing sites
• Mobile data: Text messages, locations
• Communications: Chat, instant messaging, phone recordings, collaboration software
• Media: MP3, digital photos, audio and video files
• Business Applications – MS Office Documents, Productivity Applications
• Satellite images: Weather data, landforms, military movements
• Scientific Data: Terrain Exploration, Space Exploration, Seismic Imaging, Atmospheric Data
• Digital Surveillance: Surveillance photos and video
• Sensor data: Traffic, weather, oceanographic sensor data

Read More- Types of Data Every Aspiring Data Scientist Must Know About

Structured Data Types

We find the following types of structured data:

Created Data: A company generates created data to carry out market analysis. This includes customer surveys, for example.

Provoked Data: Provoked Data comes from users, mainly feedback and product reviews.

Processed Data: Transactional data correspond to processed data. For example, data collected from the online shopping history of users.

Compiled Data: It is the data collected from the different resources, especially public records. Some examples include censuses, telephone directories, the number of registered cars in an RTO, etc.

Experimental Data: When companies adopt different marketing strategies as an experiment, the generated data is experimental data. Usually such to check which ones are more effective. Experimental data can also derive from created and processed data.

Unstructured Data Types 

Unstructured data has two types:

• Semi-structured and unstructured data
• Textual and non-textual data

We have the following types: of data within the above categories –

Captured Data: Users generate captured data, passively. This data is captured through their activity and behavior. Such data include internet searches, GPS information, biometric information from smart bands, etc.

User-Generated Data: User-generated data is actively generated by users when browsing the Internet. Examples include comments and posts on social media, conversations, and comments on stories, videos on YouTube, etc.

Semi-structured data, what are they?

While we are talking about structured and unstructured data, let’s take a look at semi-structured data too. There is one last category of data, semi-structured data. It is the intermediate point between structured and unstructured data.

Semi-structured data has a certain level of structure, hierarchy, and organization. But, it lacks a fixed schema. Although it is usual that they adopt a tree form to be able to handle them more easily.

Semi-structured data has metadata. It suggests it has elements to the group and store. But the management and automation are not as simple as with structured data.

Examples of semi-structured data:

• E-mails
• XML or any markup or markup language
• Binary executables

Differences Between Structured and Unstructured Data

Let’s check structured vs unstructured data –

	Structured Data	Unstructured Data
Level of organizing	Highest level of organized data	The lowest level of organized data
Version management	Versioning over tuples, rows, tables	Versioning only over tuples or graph
Transaction management	Mature transaction	Transaction adapts from DBMS. Not mature
Storage	Structured data is stored in a relational database (RDBMS)	Unstructured data cannot be stored in predefined relational data structures (NoSQL)
Ease of analysis	Structured data offers ease of analysis to get measurable results.	Unstructured data needs more complex analytical tools
Flexibility	Structured data is very sensitive to changes	Unstructured data that remains in the Data Lake is more flexible. It allows any user to configure and reconfigure as required.
Performance	Enables users to perform structured queries which allows complex joining, leading to the highest levels of performance	Only textual queries are possible, leading to a lower level of performance

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How Do Structured and Unstructured Data Work Together?

In today’s business world, structured and unstructured data go hand in hand. For most cases, using both is a good way to develop insight. Let’s take an example of a company’s social media posts that are a part of its marketing strategy.

How to generate insights from social media marketing? Let us understand this step by step –

1. The team can use structured data to sort social posts by highest engagement.

2. Filter out unrelated hashtags, as in keeping hashtags that are in line with the marketing strategy.

3. The team can then examine the related unstructured data.

So, the above steps can help identify why the message garners engagement. This could be anything from the actual content of the post to the media type or the message tone.

This may sound like a lot of manual labor, and that was true several years ago. However, advances in machine learning and artificial intelligence have upped the game of automation. eg. NLP analysis on audio files allows analysis for keyword patterns or positive-negative messages.

These insights are expedited by cutting-edge tools as the big data is getting bigger, and most of which is unstructured.

Conclusion – Structured vs Unstructured Data

Despite their differences, both structured and unstructured data are destined to live in harmony in the business environment. They are complementary concepts. Interestingly, they can be used separately to help the business better understand the market and consumers. Based on the data, they can further plan and design the appropriate business strategy.

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