Analyzing the Unstructured Data

We know data analytics is the process of analyzing data to get useful insights. To analyze using data analytics, the data should be in a format to be analyzed. That is the data should be structured. In today’s world, with the explosion of data production, finding structured data is difficult.

Unstructured data is now the new norm. Data are not stored in a conventional relational database now but it is generated without any form. Data from the web pages, social media, images, reports, etc are examples of unstructured data. They don’t conform to any model and presents a difficult situation to do analytics. But they have valuable information hidden with them that should be recovered.

How do we analyze the data that cannot be analyzed?

how to analyze unstructured data?

Enter Unstructured Data Analytics

Let me get over the definition first. Unstructured data analytics is the process of analyzing the data that doesn’t follow a pre-defined structure or is unorganized. Unstructured data has many forms based on the way data are stored. They are fully unstructured, semi-unstructured, and incompatibly structured. Before delving into that, let me finish what I asked.

“how do we analyze the unstructured data?”

The answer is simple. Convert the unstructured data into a structured one. The answer is simple but the process is not.

There are various tools and algorithms available to do that. Let’s say that you want to analyze the sentiment on twitter post’s which is unstructured data. There are many Natural Language Processing algorithms available to convert the unstructured twitter data into a structured one. One way of analyzing the data is to convert the twitter post into a token. That is separate each word from the post, categorize it and store it in a file. Then, create your keyword file for different sentiments like ‘angry’ for negative, ‘happy’ for positive, etc. Match the keyword against the tokens generated from the twitter post to analyze whether the post is positive or negative.

As I said, this is just one way of structuring the data and analyzing it. As the unstructured data has no format, it can be structured in many different ways to analyze. But this process is difficult compared to analyzing the readily available structured data.

Types of Unstructured Data

Fully Unstructured Data

These are the data from websites, reports, social media, video & audio files, etc. These data are the most difficult to analyze. But there are many tools and algorithms developed to handle this type of data. Hadoop is one such tool that is popular in analyzing the unstructured data.

Hadoop has its own data storage system called HDFS(Hadoop File System). It supports importing all forms of unstructured data into it and makes it easy to analyze. Hadoop also gives support to write customized code which is useful for analyzing unstructured data with complex algorithms.

Learning about Hadoop technology is a must for aspiring data analysts as it is becoming the go-to tool for analyzing the unstructured data.

Semi-Unstructured data

The data here has some structure in it but requires some additional processing to uncover the insights. Data present in the log files, error files are some examples of it. It has the data present with a limited structure like the time, source, and type in it. But still presents some difficulty to analyze.

These data are converted into a specific structure by writing customized code. There are frameworks in python, and R to help in this process but requires the knowledge of coding and the data to convert it.

Incompatibly Structured Data

These data are technically structured but are not in the format to be analyzed. XML file, BLOBs(Binary Large Objects), JSON files, etc are examples of this type. The data are stored in these files with a structure, but they cannot be analyzed readily.

Take BLOB’S. They can store texts, video, audio files in it with a structure. But to analyze it, they need to be converted into another form. There are many tools available to do this process. Hadoop has a specific tool called Hive to process this type of structured data.


Unstructured data is vast. Studies are claiming that more than 80% of data are unstructured. With technology growing rapidly, the unstructured data generation is going to grow exponentially. There are many tools and algorithms available to process this kind of data but still, the insights recovered from this data are not as good as from structured data. So, unstructured data analytics presents many complexities and opportunities for an aspiring data analyst.

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