Unstructured data: Which Data Type can store unstructured data in a column?

In this blog, we will try to understand the unstructured data and the Which Data Type can store unstructured data in a column? while doing data analysis.

Unstructured data is often difficult to process and analyze, because there is no specific format to follow.

However, unstructured data can be incredibly valuable for businesses, because it can contain a lot of useful information that can’t be found in structured data.

What is Unstructured Data?

Unstructured data is data that does not have a pre-determined structure. This type of data is commonly found in text documents, emails, and social media posts.

Unstructured data can include text data, multimedia data, and data that doesn’t fit into any other category.

Unlike structured data, which is organized in a specific format, unstructured data is simply a collection of random information.

Unstructured data is often harder to work with than structured data, because it doesn’t have a predefined format.

However, unstructured data can be more valuable for certain types of analysis, because it contains more information than structured data.

Related Article: What is Structured VS Unstructured Data?

Which Data Type can store Unstructured data in a Column?

This is an important question for data management, as unstructured data can take up a lot of space and can be difficult to work with.

In general, there are two data types that can store unstructured data:

1. Text

2. Binary large objects (BLOBs).

What are the Different Types of Data?

There are three main types of data: quantitative, qualitative, and mixed.

  1. Quantitative data is numerical and can be measured,
  2. Qualitative data is descriptive and non-numerical, and
  3. Mixed data is a combination of quantitative and qualitative data.

Related Article: What are the Types of Data Analytics?

How to Process Unstructured Data?

1. Natural Language Processing

There are a few ways to process unstructured data. One way is to use natural language processing (NLP) to extract the useful information from the data.

NLP is a technique that uses artificial intelligence to understand human language. This can be used to extract information from text data, or to classify multimedia data.

Related Article: What Is Natural Language Processing? | Used of NLP

2. Machine Learning

Another way to process unstructured data is to use machine learning. Machine learning is a technique that allows computers to learn from data without being explicitly programmed. This can be used to learn the structure of unstructured data, or to find patterns in the data.

Unstructured data can be a valuable source of information for businesses. By using NLP or machine learning, businesses can extract valuable insights from the data that they wouldn’t be able to find in structured data.

Related Article: What is Statistical Modeling? – Use, Types, Applications

3. What are the Benefits of Storing Unstructured Data?

There are many benefits to storing unstructured data. Some of the benefits include:

  1. Increased flexibility and scalability. Unstructured data can be easily scaled up or down to meet the needs of the business.
  2. Increased storage capacity. Unstructured data takes up less space than structured data, so businesses can store more data without increasing their storage footprint.
  3. Increased processing speed. Unstructured data can be processed faster than structured data, which can speed up the decision-making process.
  4. Increased data security. Unstructured data is typically more secure than structured data, as it is more difficult to hack into and access.
  5. Increased data analytics. Unstructured data can be used to generate more detailed and accurate data analytics, which can help businesses make better decisions.

How to Store Unstructured Data in a Column Using Data Type?

There are many ways to store unstructured data.

  1. One way is to store the data in a text file.
  2. Another way is to store the data in a database.

You can store unstructured data in a column by using a text data type, This will allow you to store data in a column that is not formatted in a specific way.

You can also use a BLOB data type to store unstructured data.

In general, text data can store unstructured data more effectively than BLOBs.

This is because text data is more compressible, and it can be indexed and searched more easily. However, if you need to store a lot of unstructured data, BLOBs may be a better option.

Related Article: How to do Data Processing for Analysis?

What are the Benefits of Using Unstructured Data?

1. Processed Quickly than Structured Data

One of the biggest benefits is that it can be processed much more quickly than structured data.

This is because unstructured data doesn’t have to be sorted into specific categories, which can take a lot of time.

2. It is Easier to Understand than Structured Data

Unstructured data can also be easier to understand than structured data, which can make it easier to find specific information. Additionally, unstructured data can be used to create more accurate models and forecasts.

3. It is more Representative of the Real World

Unstructured data is often more representative of the real world, Because it is not constrained by a specific format, it can capture more of the nuances and complexities of the real world.

Working with unstructured data is a valuable exercise, It can provide a more accurate picture of the real world, and it can be a valuable source of information for data mining and analysis.

4. It Is More Voluminous than Structured Data.

This makes it a valuable resource for data mining and analysis. Third, unstructured data is often more difficult to process than structured data. However, this also makes it more valuable, as it is less likely to be contaminated by bias or distortion.

What are the Challenges of using Unstructured Data?

The first step is to identify and extract the relevant data from the unstructured source. This can be a time-consuming process, and it is often necessary to employ specialized tools and techniques.

One of the challenges of using unstructured data is that it can be difficult to derive insights from it. In order to get the most out of unstructured data, you need to have the right tools and techniques in place.

Another challenge is that unstructured data can be more difficult to manage and store than structured data.

Once the data has been extracted, it must then be cleaned and processed to make it usable. This can also be a difficult process, and it is often necessary to employ specialized software and algorithms.

How can Businesses use Unstructured Data?

The answer to this question is complicated, as businesses can use unstructured data in a variety of ways. Some common applications are sentiment analysis, market research, and customer service.

Unstructured data can be extremely valuable for understanding customer sentiment and needs.

It can also help businesses understand broader market trends and how customers are reacting to new products or services.

In customer service, unstructured data can help businesses identify and resolve customer complaints more efficiently.


There are two ways or data type can store unstructured data in a column in any databases or DataFrame using Python programming language or using SQL.

Unstructured data is important because it is the data that humans create. This data is found in emails, social media posts, videos, and other digital content.

It is not in a predefined format, which makes it difficult to process. However, this data can be very valuable because it can provide insights into customer behavior, sentiment, and other trends.