Unstructured Data: Examples and How It Works

Unstructured data does not adhere to a certain model or format, making it more difficult to analyze using typical approaches. But unstructured data accounts for a considerable amount of the information created every day, which means businesses must understand how to work with it to gain the insights they need.

Unstructured data such as text documents, emails, social media postings, photos, and videos can improve decision-making and drive innovation in ways that structured data cannot. Enterprises that want to work with unstructured data need to know how it works, why it is important, and how it functions in real-life circumstances.

Featured Partners: Database Software

Learn More

Learn More

Learn More

How Does Unstructured Data Work?

Unlike spreadsheets or databases, which contain data that is ordered and formatted in ways that make it easy to search, unstructured data defies a set framework. It can come from Internet of Things (IoT) devices, sensors, emails, text messages, images, and videos, to name just a few examples of sources of unstructured data, and can provide valuable information—but it is inherently more difficult to work with.

The difficulty stems from the lack of a preconceived pattern, which makes organization, analysis, and interpretation more challenging. This process may be addressed more efficiently by artificial intelligence and machine learning (AI/ML) techniques than manual efforts.

Why is Unstructured Data Important?

Unstructured data is the most rapidly expanding category of information, accounting for the lion’s share of data accumulated by enterprise organizations. It is full of insight, but the abundance of data comes with a catch—it is more difficult to store, search for, and analyze because it doesn’t have a predefined structure or rules.

Unstructured data may contain customer sentiment from social media, trends concealed in multimedia material, or even game-changing ideas buried in a stack of emails. Exploring and utilizing unstructured data can provide insights that structured data cannot.

Advantages of Unstructured Data

The advantages of unstructured data in contemporary analytics include its capacity to capture varied information sources, represent real-world complexity, scale quickly, enable sophisticated analytics, and supplement structured data. In an ever-changing digital world, embracing unstructured data analytics enables firms to uncover hidden value, acquire a competitive advantage, and make more informed choices.

More Flexible Information

Unstructured data allows us to use information in a variety of ways. Unstructured data adapts to varied environments, allowing us to extract insights and produce value in various ways.

Insights from Diverse Sources

Unstructured data’s flexibility accommodates multiple kinds of data, including text, photos, and videos, for example. By not being limited to certain frameworks, it can provide insights from a wider range of sources.

More Detailed Information

Unstructured data can contain more detailed and granular information that captures nuances, sentiments, and specific details that may get lost in structured data. This richness enhances the depth of insights we can derive.

Deeper Analysis with AI/ML

When AI/ML is used to analyze unstructured data, these technologies can detect patterns, extract relevant insights, and automate data processing to find insights we might miss on our own.

Disadvantages of Unstructured Data

Unstructured data presents challenges in sorting, management, and organization due to its complexity and the multitude of formats it represents. Data processing can be time consuming and resource-intensive. The rigid structure of traditional data storage options can add to the problem—its predetermined structure can lack the flexibility and adaptability needed for unstructured data.

Complex Organization

Unstructured data presents difficulties in sorting, managing, and organizing due to its inherent complexity, which is exacerbated by the wide range of formats it represents.

Processing Time

Processing unstructured data takes time and requires substantial effort and resources to extract valuable insights.

Rigid Storage Choices

Traditional data storage choices for structured data need predetermined schemas, resulting in resource-intensive administration as data requirements change.

9 Characteristics of Unstructured Data

Unstructured data is best described as information without organization in many forms. It can be difficult to process because it doesn’t follow any conventional data models. Here are the key characteristics of unstructured data:

• No Fixed Schema—Unstructured data does not adhere to a specified schema or data model, allowing for a flexible and dynamic organization of information.
• Varied Formats—Text, photos, videos, audio files, social network postings, emails, and other forms are all examples of unstructured data; this variability complicates data processing and analysis.
• Lack of Organization—Unlike structured data, which is grouped into rows and columns, unstructured data lacks a clearly defined organizational framework, which makes it difficult to efficiently categorize, organize, and manage.
• Natural Language Content—Unstructured data frequently contains natural language content, such as text in documents, emails, or social media postings. This necessitates the use of specific tools for language processing and comprehension.
• High Volume—Unstructured data is frequently created in big quantities. For example, social networking platforms, blogs, and other online sources all contribute to the exponential increase of unstructured data.
• Human-Generated—Humans produce a large portion of unstructured data, such as text documents, emails, and multimedia information. This human-generated factor offers variety and context often lacking from organized data.
• Difficult to Analyze—Because unstructured data lacks a set framework, it might be difficult to analyze. Modern techniques such as natural language processing, picture identification, and machine learning can extract relevant insights.
• Dynamic and Evolving—Unstructured data is frequently dynamic and ever-changing. New information is constantly being contributed, necessitating system adaptation to changing content and forms.
• Context-Dependent—Understanding unstructured data often requires considering the context in which the information was created. Context is crucial for interpreting the meaning and relevance of the data.

Examples of Unstructured Data

Examples of unstructured data include text documents, emails, social media posts, multimedia content (images, videos, audio), sensor data, and more. The wide range of examples shows the diversity of unstructured data sources.

This diversified collection highlights the different sources of unstructured data, demonstrating the complexity and richness inherent in this type of information. Other examples include handwritten notes, PDFs, online pages, and any data that does not have a preset arrangement, demonstrating the broad nature of unstructured data sources.

Bottom Line: Overcoming the Challenges of Unstructured Data

In the domain of data analytics, unstructured data presents both obstacles and possibilities due to its diverse and dynamic nature. While it resists traditional patterns and may appear disorganized, employing modern techniques such as machine learning and artificial intelligence allows key insights to be revealed.

Recognizing the value of unstructured data in capturing the complexities of real-world information allows organizations to gain a competitive advantage, make educated choices, and innovate in ways that structured data alone cannot. Embracing its flexibility, leveraging adaptive storage options, and capitalizing on its endless potential for insights highlight the importance of unstructured data in the growing data analytics landscape.

Learn how semi-structured data occupies the middle ground between structured and unstructured data and see examples of how enterprises can adapt their systems to work with it.