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Data is the lifeline of all online businesses and the way we interact.

Every day, we create roughly 2.5 quintillion bytes of data. That’s a lot. But what is surprising is that 90% of that data is unstructured.

It does not have any particular structure. So in order to make sense of the data, we really need to understand how to deal with unstructured data.

Let’s deep dive into unstructured data without further ado.

What is Unstructured Data?

Everything in this digital world is composed of data. Data can be of two formats, either it can follow a proper structure or it would not.

Any information that is not arranged into any sequence or scheme or any specific structure that makes it easy to read for others is called unstructured data. Unstructured data has no structure or format to make it easily recognizable. Unstructured data is highly text-based like data, facts open-ended survey responses but it also can be nontextual like images, audio, or video.

There was a time when everything was composed of unstructured data and it is the major reason that in past, data was stored in the form of bulk mass. In the modern era, the trend of saving data into paper files and bulk masses has gone, now there is modern software that takes the information and extracts data from this information. There are many examples of unstructured data, all the information in the form of texts and multimedia information, and bulk mass files are an example of unstructured data.

Structured data is crucial but given the amount of unstructured data, it makes sense for brands to collate their efforts to understand and derive insights from unstructured data.

Read more: How to extract data from PDF?

What are the examples of unstructured data?

When you think of data, think of any kind of data that does not have a repeating or recognizable pattern, and that would be unstructured data. It can be textual, nontextual, human, or machine-generated. Here are some examples of unstructured data :

Text Data

The data that is available in an email or written form is called text data. Text messages, written documents, word, PDFs, and other files, of them, are an example of unstructured data.

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Multi-media messages

One type of unstructured data is multimedia messages. Multi-media data comprises images (JPEG, PNG, GIF), audio, or video format. Multimedia messages are a mix of complex code that does not have a similar pattern. All the images, videos, or audio files can be encrypted binary codes which follow no pattern, and therefore are unstructured data. What do you see here?

Well, it is actually an image of a red car.

The images and pictures need observation to understand and their data is not completely composed, that’s why this is called the unstructured data.

Website content

All the websites are filled with any information that is available in the form of long paragraphs, scattered, and disorganized forms. This is a sort of data with valuable information but still, it is not worthy because the proper composition of data is required.

Sensor Data - IoT devices

The Internet of things is a physical device that collects information about its surrounding and sends the data back to the cloud. IoT devices send back sensitive sensor data which can be unstructured. Examples of IoT devices sending senor data could be traffic monitoring devices, music devices like Alexa, Google Home, etc.


Email is widely used by businesses as one of the primary channels to communicate. Emails can be classified as semi-structured or unstructured. There are many parsing tools available that scrape the email information to understand the details.

Business Documents

Businesses deal with documents of various types, like PDFs, emails, invoices, orders, and more. All the documents have different structures. In order to extract data from PDFs, and other paper-based documents, businesses can use intelligent document processing software like Nanonets.

Want to scrape data from PDF documents, convert PDF to XML or automate table extraction? Check out Nanonets' PDF scraper or PDF parser to convert PDFs to database entries! Check out OCR API, Invoice automation, Passport OCR, AP Automation Solution, Receipt OCR, License OCR Solution, or start your free trial by clicking below!

What is the difference between structured and unstructured data?

Big data comprises structured, semi-structured, and unstructured data. All these types of data have a lot to offer. Let’s take a look at their differences in detail.

Structured data is another kind of data that follows a particular pattern and is easy to recognize. This form of data is available in RDBMS and has many applications. There is a brief table of descriptions between both structured and unstructured data:

difference between structured and unstructured data
difference between structured and unstructured data

Data Model

The most significant characteristic of unstructured data is its data model, as the unstructured data is available in the form of large pdf, text, and multimedia files, and the structured data is available in highly precise form. With the help of this schema and defined model, the study and access to the structured data have become quite easy and reliable. As large files take a large storage capacity, it is considered that normally a high storage capacity is required due to the large file size, in the case of structured data, the file size is highly adjustable because most of the data is available in tabular form that is easy to access.

Data Analysis

The analysis of data plays a significant role in determining the relevance and accuracy of the data. The unstructured data contains highly unreliable and ambiguous knowledge too. This knowledge is very reliable in the case of trusted data because all the information is organized and adjusted. The data is present in its highly organized form in structured data. That’s why, the analysis of data, its characteristics, and its knowledge become easy to analyze in structured data as compared to unstructured data. Structured data is thus highly preferable to unstructured data.


Unstructured data extraction is unorganized knowledge. It takes too long to study some major points from any unstructured data. But it is easily possible in any structured data where users can make an easy search. Structured data is easy to search and everything present in it can be searched easily. On the other hand, unstructured data is difficult to understand and difficult to search because all the data is available in the form of large files.

The visionary analysis of the unstructured data is also very determined in data analysis form. The structure that is available in the paragraph and other structures always attract less gathering as compared to the data that is available in a very short and up-to-date form. The data analysis tools provide very valuable information for the data analysis. There is no need to spend time on your exact time for the authentication of the information of the data because structured data always take less time for the user as compared to unstructured data.

The unstructured data extraction is highly significant and it reveals very valuable information regarding the data. All the accurate information is gathered in one place that is very easy to understand and less time taking than unstructured data. There are many challenges too that users have to face with the unstructured data, some of which are explained below.

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Possible challenges of unstructured data

The unstructured data comes in highly long-form and that’s why unstructured data extraction is necessary. Many challenges are faced by the working staff while working with unstructured data. First of all, this type of data is available in a bulk text of any other form, that’s why it takes too long to do with this data. Second, if the data is available in big files, as most probably unstructured data presents, takes too much storage. The quality of the structured data is that it presents in very precise and tabular forms, that’s why extraction of the data is very easy.

Compromised relevancy

It is seen that unstructured data contains much information that is not valuable and highly inaccurate and irrelevant. The accuracy of the data should be maintained in the best possible way, that’s why the biggest challenge faced with unstructured data extraction is to maintain the quality of relevant and accurate data intact.


Since the time of digitalization of the World in the 20th century, data success comes with occupying less storage and more information. In past, data was saved in many large files, the unstructured data is taking too much storage that it has now become a challenge to deal with all these changes.

Time To Extract Information

Dealing with unstructured data is high time taking.  It took too long to extract information from unstructured data when it comes to the urgency of the data. That’s why, the data took too long and in urgency, it is very difficult to extract all the knowledge from the data.

Since the start of digitalization, many tools have come into being to deal with the challenges of unstructured data extraction. To save time, the unstructured data extraction via AI-enhanced data extraction tools like Nanonets is very reliable because it provides thorough and altogether relevant information for data. The relevancy of the data is very important because it is an important time-saving tool for the working staff and analysts. With these data strategies, one can easily interpret valuable information from the data.

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How unstructured data extraction is useful?

It is a revolutionary time to make better business decisions. All these decisions are taken at their best and as digitalization is improving the world, business growth has become easier. Today, the unstructured data extraction procedure has made it easy to process different things. The time-saving, fast processing system is doing the best job when data is required.

The data evaluation becomes easier when the unstructured data extraction happens. Unstructured data is the rough material, from which valuable information is withdrawn and this information is stored in easy storage. The tabular form of data helps to get access to the unstructured data easier.

The query data is organized into a more useful form, the user-friendly data is organized and very well-formed. There is no ambiguity in the data and anyone can read the data easily. The process of extraction help to get the data procedure easily. Among many data extraction tools, all provide their valuable services and these reserves are used for the better working of the system and the better growth of the environment.

The help of untrusted data in different industries:

Many industries cannot work accurately now if they do have how is unstructured data extraction used in different industries unstructured data extraction tool. All these tools help maintain the authenticity of the data. Banking is a famous industry that is using these data analysis tools for the better banking and growth of the business.

An unstructured data extraction is a valuable tool in scientific research and tools. All the tools that are used in scientific research shorten the data into a more precise form to get better use of it. the data can be extracted in the best possible way and whether this data is generated by human beings in this data is provided by the machines, the extraction is always helpful in providing valuable information regarding data.

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What are the advantages of using unstructured data?

Unstructured data is difficult to understand, interpret and use directly, but that’s not the only thing about it. There are many advantages of using unstructured data, as mentioned below:

No Fixed Format

Unstructured data supports data of all formats and sizes. Any kind of data that does not have a proper sequence can be classified as unstructured data. It can be useful to expand the horizon of types of data.

No Schema

As discussed above, unstructured data has no fixed sequence and it also has no fixed schema. This is what makes unstructured data extraction difficult for most of the parts.


Given unstructured data has no structure, it can have any format. This makes it fluid in terms of structure.

Portable & Scalable

Unstructured data is more portable and scalable as compared to semi-structured and structured data.

Lots of Business Applications

Given that 80% of the enterprise, company data is unstructured, there are a lot of applications for this data. Unstructured enterprise data is used for a variety of business analytics use cases. For example, presentations, company videos, understanding customer profiles, etc.

How do convert unstructured data into structured data?

While working with big and bulky data can be a hectic task. To save time and to maintain the originality and accuracy of the data, it should be shortened to such an extent that only necessary information remains left. The unstructured data extraction has different methods and its significance is very much shown by all the information provided above. The difference between the structured and unstructured give important clues about the data. You can use the following steps to convert unstructured data into structured data.

Step 1: Have a Clear Goal in mind

No project should ever start without having a set of measurable goals. With a clear idea of the end goal of what insights you want to obtain, it becomes easier to finalize the next steps.

Step 2: Finalize the data sources

Data is everywhere. But, to start with the conversion, you need to identify the data sources to draw your unstructured data. Data extraction strategies would be different for different data sources. Nanonets allow users to collect data from multiple sources like Gmail, drop box, outlook, desktop, etc.

The data can be extracted from the big pdf files, images, and other text forms.

Step 3: Standardization of Data

The third step is to know what to do with unstructured data extraction. The analyst should have an idea about the final result of the unstructured data.

If you have selected the data, the next step is to finalize the outcome of the data. If the data is in any variable form, the analyst needs to standardize it before any analysis can be performed. This particular step involves cleaning and standardizing the data formats for the next steps.

Step 4: Selecting the data extraction technology:

After understanding the data sources and the method of standardizing the data, it is important to finalize the software that you want to use for implementing these steps. IDP platforms like Nanonets help organizations to connect, extract data and standardize it for further analysis.

The data will be taken by different software, the next step is to find the technology by which the data will be transferred to the software. For this purpose, a rational database management system (RDBMS) is used. This software and technology help to get straightforward technology use.

Step 5: Selecting the data storage system

The data storage system is selected based on the type of technology that you are looking for, it should have high availability, high-velocity time, and other features.  All these features along with the real-time storage capacity make the high storage system.

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How is unstructured data extraction used in different industries?

Businesses across industries are using unstructured data extraction techniques to make sense of their business documents and add an extra layer of intelligence to their analytics. The figure below shows the advent of the use of unstructured data in different industries.

How is unstructured data used throughout industries?

[Source: TCS Study]

Here are some examples of how different industries are using intelligent document processing platforms like Nanonets for unstructured data extraction and enhancing their productivity.


Banks use IDP platforms to extract insights from unstructured data sources like claims, customer forms, KYC documents, call records, financial reports, and more.

Read more: RPA in Banking and Banking Automation


Insurance is a heavily regulated industry. It needs to perform document verification and identity verification at every step of insurance claims processes. Insurance firms use automated document processing platforms to automate claims processes, risk management, and other functions which are rule-based. The insurance claims process contains a lot of unstructured data. Unstructured data extraction by using AI-enhanced platforms like Nanonets makes the insurance claims process easy as it allows for selective data extraction from images, PDFs, videos, audios, etc.

Read more: Insurance Automation, Insurance OCR, and RPA in Insurance


Providing exceptional patient experience revolves around providing better service, reducing patient wait times, and ensuring staff aren’t overworked. Using IDP platform to extract insights from unstructured data sources like the voice of customer data, patient surveys, EHRs, customer complaints, regulatory websites, and literature review helps Healthcare to ensure a better patient experience.

Read more: Healthcare automation and AI in healthcare

Real Estate

Real estate companies deal with multiple people at the same time like customers, builders, tenants, vendors, competitors, and property owners. Using automated document processing software can help real estate institutions to create rich profiles of mentioned stakeholders and streamline the data extraction from unstructured data sources like rent leases, contracts, property valuation papers, etc.


Data is the new oil. The business that masters unstructured data extraction can unlock the full potential of enterprise data. Nanonets allow enterprises to automate their document processing and can smartly extract data from any kind of document.

Nanonets online OCR & OCR API have many interesting use cases that could optimize your business performance, save costs and boost growth. Find out how Nanonets' use cases can apply to your product.

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