What is Unstructured Data?

Summary

• Data comes in two main types: structured and unstructured.
• Unstructured data refers to data that doesn’t fit neatly into tables or spreadsheets like structured data.
• It includes things like images, emails, social media posts, and videos.
• Despite its challenges, unstructured data offers rich insights into customer behavior.
• Around 90% of today’s data is unstructured, making it valuable for businesses.
• Types of unstructured data range from textual to multimedia and sensor data.
• Storing unstructured data requires different tools like NoSQL databases and data lakes.
• Cloud storage services offer scalability and accessibility for managing unstructured data.
• Effective organization and metadata usage are crucial for navigating unstructured data.
• Despite its complexity, effectively managing unstructured data can provide significant strategic advantages for businesses.

The sheer volume and variety of data generated by individuals and businesses have grown tremendously in today’s digital age. By 2025, we will produce a little over 180 zettabytes of data. Data is indeed the king. 

But here’s the catch: not all data plays by the rules. Think about our everyday lives. We send so many messages over WhatsApp everyday, leave comments under YouTube videos, spend hours scrolling through Instagram reels – did you know these activities are all data? Ever thought how these are stored? Such data fall under the category of unstructured data.

So, what exactly is unstructured data?

In this article we will explain what unstructured data is, how it is becoming increasingly important yet remains challenging to manage, and how it is advantageous or disadvantageous. By the end of this article, you will also learn the innovative methods for dealing with it. Further, you will understand its impact on business, technology, and our everyday lives.

What is Unstructured Data? Explained

If you are asked to organize data, what’s the easiest way you will come up with? Putting them into a sheet right? Unstructured data is basically all the information that doesn’t fit neatly into traditional row and column databases. It includes all kinds of data that are not easily put into tables or sheets. 

Think of things like videos, emails, social media posts, and even the text within documents that you can’t just extract and put into a table. This type of data doesn’t follow a clear schema or pattern or structure. That makes it tricky to handle using the standard tools that work well with more organized data, like numbers in spreadsheets.

This might sound challenging but this characteristic is what makes unstructured data incredibly useful. It’s rich with details and insights that you don’t usually get from more structured data. 

For example, a product spreadsheet might tell you how many people bought the product. But an analysis of customer reviews and social media reactions can tell you why people liked or disliked it. The latter is known as unstructured data.

Today, with so much of our communication and media being digital, a huge portion of the data companies collect is unstructured. In fact, around 90% of today’s data is unstructured. Businesses and data analysts are finding this type of data increasingly valuable because it offers a deeper understanding of customer behaviors, preferences, and experiences. 

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Unstructured Data Examples and Types

Unstructured data comes in various types and forms. All of these are pretty common in our daily digital interactions. Let’s break down what these types are, and you will be surprised to know how we deal with unstructured data in every step of our daily lives. Here are some unstructured data examples:

Textual Data

This is one of the most common forms of unstructured data. It includes any kind of written content that doesn’t fit into a straightforward database schema. Examples include:

• Emails: Full of text that can vary greatly in format.
• Text messages: WhatsApp messages, and LinkedIn DMs, even a simple hi over SMS.
• Documents: Think of Word documents or PDFs that contain a lot of text.
• Books and articles: Digital or scanned versions of your favorite book or article that contain nothing but plain text.

Multimedia Data

This type of unstructured data includes all types of media formats that are used to convey information. It’s diverse and often requires more complex processing technologies to analyze. Examples include:

• Images: Digital photos and scanned images.
• Videos: Everything from YouTube clips, GIFs to professional broadcasts, CCTV footage, etc.
• Audio: Podcasts, songs, or recordings of meetings.

Webpages

Web content is another major example of unstructured data. Each webpage is unique and can contain combinations of text, images, and videos along with complex formats and links. All of it falls under the category of unstructured data.

Business Documents

These might seem structured but they are not. A business document is one of the best examples of unstructured data. Because they are often made with templates. However, each document can be very different from the next. You may use a template to make your resume but is your resume the same as someone else’s? No right? So, business documents like the following are examples of unstructured data:

• Presentations: Slideshows that mix text, images, and designs.
• Reports: They might have charts or graphs that are structured data. However, the commentary and varied formatting are unstructured.
• Notes and memos: Usually typed in an informal style with no set format.

Social Media Content

Social media is one of the major sources of unstructured data. Over 5 billion people around the world use various social media platforms in different ways. Analysts and businesses use this unstructured data to understand the current qualitative market sentiment. Examples include:

• Tweets: Short messages on X (Formerly Twitter) that can include hashtags, links, and mentions.
• Instagram Posts: Images, reels or videos accompanied by captions, tags, and comments.
• Snapchat Stories: Temporary video and photo posts that come with text and graphic filters and stay for just 24 hours.
• User Generated Content: Content on platforms like Reddit that a user creates. Customer reviews, survey responses, etc.

Sensor Data 

Sensory data from IoT devices also contribute to unstructured data. IoT devices include different types of sensors like temperature sensors, motion sensors, and light sensors. Each of them collects different types of data. For instance, let’s consider a smart home system that has temperature sensors, motion detectors, and light sensors. Each type of sensor produces its unique data format as following:

• Temperature sensors give numerical readings about the temperature. However, the frequency and context of these readings varies depending upon where the smart home system is. It won’t give the same readings in Alaska and India. Even a temperature sensor at one place can’t give the same reading throughout the day because the temperature of your surroundings depends on a number of factors.
• Motion sensors might provide binary data (motion/no motion), detailed timestamps, or even a video feed. It depends on how advanced the motion sensor is. Now, imagine you have a motion sensor installed in your porch. When someone walks by, it sends a signal saying “motion detected.” If you have a more advanced sensor, it might give you timestamps, like “motion detected at 8:45 PM” or “no motion detected at 8:00 PM.” So, the readings vary.
• Light sensors measure light intensity. Let’s say you have a light sensor in your balcony. It measures how bright it is outside. But the data it provides can be influenced by many things. For example, in the morning, when the sun rises, the brightness increases gradually. Then, when clouds pass by, it might decrease suddenly. Shadows from nearby trees or buildings can also affect the data given by the sensors. Therefore, light sensors can give different readings based on a variety of factors.

As we have seen, sensory data from IoT devices is not something organized like a list of names of numbers or a spreadsheet of the information you need. It’s just bits of information that you need to interpret individually to understand. And such nuggets of data fall under the category of unstructured data. 

Even the smartwatch we wear today that can check our heart rate, or industrial IoT sensors that measure performance metrics of machines, they all collect unstructured data.

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Advantages of Unstructured Data

We learned what unstructured data is and what its types are. But why is it used? Unstructured data comes with a number of advantages. Here are some of the advantages of unstructured data:

• Flexibility: Structured data fits into pre-determined categories and tables. But as we have seen, the majority of our data cannot fit into such tables. Unstructured data includes everything from emails and social media posts to videos and documents. This variety and flexibility allow businesses to use unstructured data in many ways. T adapting as needs change without being stuck to one specific format.
• Speed and Cost-Effectiveness: Gathering unstructured data is usually quicker and cheaper. You can collect such data easily through normal activities like interacting with customers on social media or sending an email or asking them to fill out a survey form or seeing how many likes or comments an Instagram post has got. 
• Rich Insights: Unstructured data can provide deeper insights into customer behavior, preferences, and trends than structured data. For example, seeing how customers react to a product post on social media can give you an idea about their true sentiment regarding the product.
• Real-Time Use: Unstructured data doesn’t require extensive preprocessing. Therefore, it can be used in real-time applications. This is particularly useful for responding quickly to customer inquiries or monitoring social media for immediate feedback.
• Ease of Storage: There are fewer restrictions on how and where you can store unstructured data. You can store it on local servers or in cloud-based systems. There are a variety of scalable storage options to store unstructured data.
• Accessibility: People without technical skills can often understand and interpret unstructured data more easily than structured data. You don’t need to be a specialist to decode texts, videos, or images. They are straightforward and easy to understand. Therefore, different teams within an organization can use unstructured data more effectively than structured one.

Disadvantages of Unstructured Data

• Hard to Search: Unstructured data doesn’t follow a clear format. This makes it difficult to search through it. Finding specific information can take a lot of time and effort.
• Storage Costs: Unstructured data often takes up a lot of space. As it accumulates, the cost of storing it on servers or in the cloud can become very high.
• Analysis Challenges: Analyzing unstructured data to get useful insights is not straightforward. It often requires complex software and a lot of processing power, which can be expensive and time-consuming.
• Security Risks: Protecting unstructured data can be tough because it’s hard to apply strict security measures to data that isn’t well-organized. This can make it more vulnerable to security threats.
• Data Quality Issues: It’s hard to check the quality of unstructured data. Errors or inconsistencies can easily go unnoticed, which can lead to poor decision-making based on inaccurate data.

How to Store Unstructured Data? Top Tools

Tool	Type	Key Features
MongoDB	NoSQL Database	Supports embedded objects, real-time processing, and direct integration without ETL steps.
Apache Hadoop	Big Data Platform	Scalable, supports distributed processing, and integrates with other tools for comprehensive data management.
Apache Spark	Data Processing	In-memory processing for real-time analytics, supports multiple programming languages and machine learning.
Excel	Spreadsheet	Basic to advanced analytics within a familiar interface, integrates with NoSQL for data import.
Tableau	BI Tool	Strong visualization capabilities, integrates well with various data sources for interactive analytics.
Power BI	BI Tool	Comprehensive dashboard and visualization features, integrates with multiple data sources for real-time analytics.
RapidMiner	Data Science Platform	Supports end-to-end data science workflow including machine learning and model deployment.
KNIME	Data Analytics Platform	Open-source, code-free to low-code options, suitable for complex data integration and analytics workflows.
MonkeyLearn	AI Platform	Low-to-no-code platform, integrates with common applications, supports automated text analysis.

Storing unstructured data requires a different approach compared to structured data. You can’t store it in a relational database (RDBMS) like structured data. You can store unstructured data in a non-relational database.

• First, you need to choose the right storage solution that can handle large amounts and various types of data. Cloud storage services like Google Cloud Storage are a popular choice because they are scalable. It means they can grow with your needs without major issues. These services also make it easy to access your data from anywhere, which can be very helpful for businesses that operate online or have remote workers. Cloud storage services handle data as objects. Each has its own metadata. This feature makes data easily retrievable.
• For larger volumes of data or more complex analytics needs, you can go for data lakes. They can store massive amounts of both structured and unstructured data in their native format. This method is highly scalable and cost-effective as it uses pay-as-you-go pricing models. However, it’s important to manage these data lakes effectively to prevent them from becoming disorganized and less useful. This situation is often referred to as a “data swamp.” 
• Non-relational databases, or NoSQL databases, are also a good choice for unstructured data. They are flexible, scalable, and do not require a predefined schema​.
• If you are looking to manage unstructured data without the infrastructure costs, Software-as-a-Service (SaaS) or Infrastructure-as-a-Service (IaaS) options like Microsoft Azure and Amazon Web Services offer comprehensive solutions that include data storage, processing, and analytics capabilities​​. These platforms provide the tools necessary to store, manage, and analyze unstructured data effectively.

Once you have a storage solution, it’s time to organize your data effectively. Even though the data is “unstructured,” creating a logical organization system can help you and others find what you need when you need it. 

You might use folders or tags based on date, project, or content type. For instance, you can make separate folders for video content and text documents. Additionally, you can use search-friendly naming conventions and metadata (data about its origin, like where it came from or who created it). This will make it easier to search through large amounts of data quickly. Further, make sure to regularly check your storage to remove outdated or unnecessary data to keep costs down and improve efficiency of your whole system.

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What is the Difference Between Structured Data and Unstructured Data?

Attribute	Structured Data	Unstructured Data
Definition	Data organized in a fixed format, making it easy to enter, query, and analyze.	Data that does not follow a specified format, making it harder to collect and analyze.
Storage	Typically stored in relational databases and data warehouses.	Often stored in data lakes or file systems that do not require a rigid schema.
Examples	Financial records, inventory, customer data.	Emails, videos, social media posts.
Analysis	Easier to automate and analyze due to its predictable structure.	Requires more complex tools like AI and machine learning to manage and derive value.
Tools	SQL databases, CRM systems, business analytics tools.	NsSQL databases, Natural language processing, multimedia analysis tools, text analysis software.
Challenges	Inflexibility of schema, difficulties in adapting to new data types quickly.	Data size and complexity, requiring significant processing power for analysis.
Applications	Ideal for quantitative analysis, reporting, and operations that require accuracy.	Suitable for qualitative analysis, sentiment analysis, trend discovery.

Reading so far, you may have already known the basic difference between structured data and unstructured data.

Structured data refers to information that is organized and formatted in a way that makes it easy to find, sort, and process using standard database tools. It’s often stored in tables or spreadsheets. Here you can find specific information organized in columns and rows. For example, customer information in a CRM system is structured data. In this case, each piece of data, like names, addresses, and phone numbers, fits neatly into predefined categories. Structured data is quantitative. 

Unstructured data, on the other hand, is qualitative. It doesn’t fit neatly into a database’s traditional row and column structure. It’s much more flexible but also more challenging to organize. However, as we discussed in this article, the majority of the data generated today is unstructured. Therefore, unstructured data can be a goldmine for insights. Though it requires more advanced methods and tools for processing and analysis. 

Conclusion

As we wrap up our exploration of unstructured data, it’s clear that while the path to using this kind of data can be challenging, the benefits are substantial. Effective management and analysis of unstructured data allow businesses to gain deeper insights, make more informed decisions, and respond more quickly to market changes and customer needs. 

As technology evolves, so too does the toolkit available for tackling these tasks. It promises ever more efficient and insightful ways to work with complex data. By staying informed and adaptable, businesses can turn the challenge of unstructured data into a significant strategic advantage.

Frequently Asked Questions

What is unstructured data?

• Unstructured data includes information that doesn’t fit into traditional databases.
• Examples include emails, social media posts, videos, and sensor data.
• It lacks a clear structure or format, making it challenging to organize and analyze.
• Despite its complexity, unstructured data provides valuable insights into customer behavior and market trends.

How is unstructured data different from structured data?

• Structured data is organized in a fixed format, making it easy to process using standard database tools.
• Unstructured data, on the other hand, is qualitative and doesn’t fit neatly into rows and columns.
• While structured data is quantitative and ideal for quantitative analysis, unstructured data requires more advanced methods for processing and analysis.

Why is unstructured data important for businesses?

• Unstructured data offers deeper insights into customer behaviors, preferences, and experiences.
• It helps businesses understand market sentiment, trends, and emerging opportunities.
• By analyzing unstructured data, businesses can make more informed decisions and respond quickly to changing market dynamics.
• With around 90% of today’s data being unstructured, effectively managing and analyzing it can provide a significant competitive advantage.

How can businesses manage and analyze unstructured data effectively?

• Businesses can use tools like NoSQL databases, data lakes, and cloud storage services to store and manage unstructured data.
• Effective organization and metadata usage are crucial for navigating and accessing unstructured data.
• Advanced analytics techniques such as natural language processing and multimedia analysis are used to derive insights from unstructured data.
• By staying informed and adaptable to evolving technology, businesses can harness the power of unstructured data for strategic decision-making and innovation.