What is unstructured data? Examples and more

Data comes in various forms. While structured data neatly fits into predefined tables and databases, a significant portion of the information available is unstructured—think: text documents, emails, and multimedia files. Unstructured data lacks a specific data model or organizational schema, making it more challenging to analyze using traditional methods. That’s why Benn Stancil, ThoughtSpot’s Field Chief Technology Officer and co-founder of Mode, said:

'For most companies, ‘data’ is synonymous with ‘structured data.’

Despite its seemingly chaotic nature, unstructured data holds valuable insights that can be unlocked with advanced analytics. In this article, we will delve into the intricacies of unstructured data, exploring its definition, uses, key differences from structured data, and notable examples.

What is unstructured data?

Unstructured data is any data that doesn’t fit into neatly structured rows and columns—including text documents, images, videos, and other formats.

A significant portion—80% to 90%—of the data generated and collected by organizations is unstructured, and those volumes are expanding rapidly. These unstructured data repositories hold a wealth of crucial business information, including financial projections and customer sentiment. However, extracting and organizing this data is a prerequisite for its effective utilization. 

Although unstructured data has been historically challenging to analyze, advancements in AI and machine learning are now making it possible to reveal valuable and actionable business intelligence.

Structured vs unstructured data

Structured data adheres to a predefined data model and is typically organized within relational databases or RDBMS (relational database management systems)—that’s why it’s often referred to as relational data. This data is organized into tables with rows and columns, with each piece of data being stored in a specific and defined field. This configuration makes structured data easy to search, retrieve, and analyze by both humans and computers, creating the foundation for the natural language search that BI solutions like ThoughtSpot use to analyze data. 

On the other hand, unstructured data defies the constraints of predefined data models and lacks a specific organizational schema. Unlike structured data, it cannot be neatly stored in an RDBMS due to its diverse formats and absence of consistent internal structure. 

Despite distinct differences between structured vs unstructured data, structured tables can still contain unstructured elements. For example, consider a survey that has both qualitative and quantitative answers. Your data table may contain customer reviews rating a product on a scale from 1 to 5. Performing aggregate mathematical operations on these numeric values is straightforward. However, applying aggregate functions like averaging to unstructured data, such as open-ended survey responses, is challenging due to the lack of inherent mathematical properties in text. 

While techniques for analyzing unstructured text data, including natural language processing algorithms, are advancing, there isn't yet a standardized method for deriving averages or statistical summaries from open-ended responses.

Unstructured data examples

  • Emails, blogs, social media posts: Communication in email, blog entries, and social media often includes unstructured text. It can be conversational and expressive, and it may not follow a rigid format.

  • Text documents: These contain textual information without a predefined structure. The content may include narratives, descriptions, or any form of written communication.

  • Multimedia files: Images and videos may lack explicit labeling or categorization, while audio files can contain spoken words or other sounds without a predefined structure.

  • Web pages: The content of web pages is typically unstructured, especially when considering the diverse elements such as text, images, links, and multimedia embedded within them.

  • Open-ended survey responses: Responses to open-ended questions may vary widely in content and structure, making them unstructured and challenging to analyze with traditional methods.

What is unstructured data used for?

Unstructured data provides invaluable insights into customer sentiments, market trends, and user behaviors. You can leverage this qualitative information to enhance your decision-making processes, improve customer experiences, and gain a competitive edge in the market. Some common uses for different types of unstructured data include:

  • Text analysis and NLP: Unstructured text data, such as emails, customer reviews, social media posts, and documents, can be analyzed using natural language processing (NLP) techniques to extract valuable insights, sentiment analysis, and information categorization.

  • Image and video analysis: Unstructured data in the form of images and videos is utilized for tasks like facial recognition, object detection, and content classification. This is especially relevant in fields like security, healthcare, and entertainment.

  • Speech and audio processing: Unstructured audio data can be analyzed for speech recognition, voice sentiment analysis, and other applications in areas like customer service and voice assistants.

  • Internet of Things (IoT): IoT devices, such as those deployed by a healthcare organization, generate vast amounts of unstructured data. This data can be analyzed to monitor patient health, predict potential health issues, and improve overall healthcare outcomes.

  • Knowledge discovery: This data is often used for knowledge discovery and exploration. For instance, by analyzing customer support tickets, you can uncover patterns in common issues, relationships between product features and user feedback, and valuable insights into customer preferences.

Challenges with unstructured data

While unstructured data can be extremely beneficial, it poses several notable challenges due to its lack of predefined data models. 

  • Limited analytical capabilities: Finding qualitative insights within qualitative data can be extremely challenging, limiting your ability to perform analysis and quantify results—especially when using traditional analytics tools that are designed for structured data.

  • Lack of centralized collection: Data scattered across emails, documents, and databases makes centralized collection a formidable task for companies. This fragmentation hampers the ability to leverage data effectively for analysis and decision-making. Moreover, the absence of streamlined collection processes sustains the cycle of inaccessible and underutilized data.

  • Search and retrieval issues: Searching and retrieving information from unstructured data can be time-consuming and less accurate compared to structured data.

  • Data quality and consistency: Unstructured data may vary in quality and consistency due to its diverse sources and different formats. This poses challenges in ensuring reliability for insights derived from the data.

  • Security and privacy: Unstructured data may contain sensitive information that’s hard to identify. For example, it can be hard to identify which information in a document is private, and which is not. Companies have to mask that private information to meet security compliance standards and regulations.

  • Ease of integration: Integrating unstructured data with structured environments and existing systems and databases often requires additional effort and resources.

  • Semantic understanding: Extracting meaningful insights from unstructured data requires a deep understanding of semantics and context, often addressed through Natural Language Processing (NLP) and machine learning techniques.

  • Cost of storage and processing: Storing and processing unstructured data, especially in large quantities, can come at a cost. To justify the price tag, you need to ensure you’re getting value out of your unstructured data.

How to overcome the challenges of unstructured data

In the world of unstructured data, you face the challenge of extracting meaningful insights and aggregating information for actionable intelligence. Here's how you can navigate this process–

Extraction with LLMs

To start, you can employ advanced analytics tools that often leverage Large Language Models (LLMs) to parse unstructured data. These models excel at understanding the context and content of your diverse data sources, transforming raw information into a more organized and structured format.

Building structured insights

Following the parsing process, the structured data can be further refined and organized. The goal is to create a foundation for your analysis. This allows you to ask complex questions and derive meaningful insights from the now-structured data.

Aggregation for actionable intelligence

With the data transformed into a structured format, you can now focus on aggregation—combining information from different sources into a digestible format. This step is essential for deriving actionable intelligence, helping you uncover patterns, relationships, and insights across the aggregated data.

Exploration and analysis

The final stage involves exploration and analysis. ThoughtSpot is designed to help you unlock actionable insights hiding in your data. Here's how you can leverage ThoughtSpot.

  • User-friendly interface: Any user, regardless of their familiarity with data or technical background, can effortlessly navigate and interact with your data through interactive Liveboards and natural language search.

  • AI-driven search: With ThoughtSpot Sage, you can ask questions, just as easily as you’d ask your favorite search engine, to reveal instant insights and data visualizations.

  • Automated insights: Using SpotIQ to automatically analyze your data, you’ll uncover hidden patterns, underlying trends, and unforeseen outliers—saving you time and money while simultaneously simplifying the data exploration process.




  • Visualizations and dashboards: With Liveboards, you can create visually compelling charts, graphs, and interactive dashboards for any use case, making it possible to get real-time updates on the data that matters most. 

  • Collaboration and sharing: Data is built for sharing, not silos. ThoughtSpot is designed to easily share insights, reports, and dashboards through business apps, Slack messages, and even mobile alerts. Plus, with ThoughtSpot’s note tiles, you can provide important context to ensure everyone is analyzing the data the same way.


Unlock the power of your data

As we continue to witness the exponential growth of unstructured data, the ability to harness its power will undoubtedly become a pivotal factor in shaping the future of data-driven enterprises. The journey from chaos to clarity in the domain of unstructured data is marked by both the hurdles we overcome and the insights we unlock. ThoughtSpot stands out as a powerful solution to assist you in unlocking the complete potential of your data.

Consider Accern, whose no-code AI streamlined the extraction of insights from vast amounts of unstructured data, such as news articles and financial filings. While they had the technology to extract the data, they needed a more customizable and user-friendly data visualization solution that would allow them to present the data in a more cohesive way. This led them to explore ThoughtSpot Everywhere, which gave Accern the ability to deliver personalized data experiences at scale.

ThoughtSpot is and will continue to be a key factor that will help us showcase results,” says Cristian, Chief Product and Technology Officer at Accern. “As we continue to improve Accern’s data customization, it naturally improves the quality of our data output and the visualizations customers have access to.

See how ThoughtSpot can revolutionize your approach to data-driven decision-making–schedule a demo.