Unstructured Data

What is Unstructured Data?

Unstructured data is digital information that cannot be stored efficiently in a relational database because it does not have a pre-determined data schema and may include more than one file format. Sources for unstructured data include email and text messages, word documents, customer reviews, digital images, audio files, and videos.

Key Takeaways

Unstructured data does not fit neatly into traditional table-based databases or spreadsheets because it lacks a single, pre-defined schema.
Sources for unstructured data include social media posts, email and text messages, online reviews, images, podcasts, and videos.
Approximately 80% of the data that humans and machines produce each day is unstructured.
Unstructured data analysis can require special artificial intelligence (AI) tools like machine learning (ML) and natural language processing (NLP).
The flexibility that unstructured data provides requires robust data governance policies to ensure data quality, privacy, and compliance.

Table of Contents Table of Contents

What is Unstructured Data?
Key Takeaways
How Unstructured Data Works
Unstructured Data Characteristics
Unstructured Data vs. Structured Data
Examples of Unstructured Data
The Importance of Unstructured Data Management
Unstructured Data Techniques & Tools
Unstructured Data Use Cases
Unstructured Data Pros and Cons
The Bottom Line
FAQs
References

Table of Contents

What is Unstructured Data?
Key Takeaways
How Unstructured Data Works

Show Full Guide

Unstructured Data Characteristics
Unstructured Data vs. Structured Data
Examples of Unstructured Data
The Importance of Unstructured Data Management
Unstructured Data Techniques & Tools
Unstructured Data Use Cases
Unstructured Data Pros and Cons
The Bottom Line
FAQs
References

How Unstructured Data Works

Unstructured data captures information in its native format without forcing it into a predefined table structure that has rows and columns. This type of data is typically stored in data lakes that use object-based storage or NoSQL databases that don’t have a predefined organizational structure or schema.

Processing unstructured data can significantly improve its value and accessibility without necessarily converting it into a structured format. For example, optical character recognition (OCR) can be used to convert a scanned document into machine-readable text.

To maximize the value of unstructured data, it’s important to integrate it with structured data, semi-structured data, and business processes. For example, integrating customer feedback from social media with sales data provides a more complete picture of customer preferences.

Unstructured Data Characteristics

5 Key Characteristics of Unstructured Data

Unstructured data is characterized by its lack of predefined format and organization. This type of data can be raw or processed and may include a mix of formats.

5 key characteristics of unstructured data:

Lack of schema

Unstructured data does not follow a predefined format or organizational structure.

Variety

Unstructured data can include a mix of text, images, audio, and video content.

High volume

It’s widely accepted that 80% of the world’s big data is unstructured.

Flexibility

The absence of structure allows unstructured data to capture different types of information, but this same flexibility makes it harder to process and analyze using traditional methods.

Complexity

Processing and analyzing unstructured data often requires advanced big data analytics tools, and artificial intelligence technologies such as machine learning and natural language processing.

Unstructured Data vs. Structured Data

Structured data is organized in a predefined format that typically uses rows and columns. The organized format allows computer programs to search and analyze the data using structured query language (SQL).

In contrast, unstructured data lacks an organizational scheme. Because this type of data can include more than one file type, traditional data processing tools can struggle to interpret and analyze unstructured data.

	Structured data	Unstructured data
Format	Fixed format with predefined fields and data types.	Varied formats that do not have predefined fields or a single data type.
Examples	Spreadsheets, relational databases, sales transactions.	Text documents, images, videos, social media posts.
Storage	Stored in relational databases.	Stored in data lakes, NoSQL databases, or object-based cloud storage.
Processing	Easy to query and analyze using tools like SQL.	Typically requires artificial intelligence and special tools to extract insights.
Scalability	Scalable within database limitations.	Highly scalable.
Use cases	Transactional data, inventory management, financial systems.	Customer reviews analysis, video transcripts, sentiment analysis.
Flexibility	Limited flexibility due to rigid schema.	Highly flexible and able to store diverse types of data.
Value extraction	Insights can be extracted directly from a structure.	Insights typically require complex analysis.

Examples of Unstructured Data

Unstructured Data Examples

Unstructured data can be categorized by whether it is generated by a human or by a machine. Human-generated unstructured data includes emails, videos, social media posts, text messages, audio files, digital images, and text documents. Machine-generated unstructured data includes server logs, Internet of Things (IoT) sensor data, satellite imagery, and digital surveillance footage.

This distinction is important because the source influences how unstructured data is analyzed. For example, analyzing social media posts involves social sentiment analysis, while analyzing sensor data involves time-series analysis.

The Importance of Unstructured Data Management

Unstructured data management allows organizations to transform their unstructured data into a standardized format and enrich it with additional metadata.

Generative AI (genAI) has significantly changed unstructured data management by automating data pre-processing tasks. The extent to which data can be standardized depends on the specific management tools used and the organization’s objectives.

Some tools, like Elasticsearch, enhance usability by adding metadata and search capabilities while leaving the core data in its original unstructured form. Other tools, like Dataiku, can transform unstructured data into structured formats so it can be used by machine learning models and big data analytics tools.

Unstructured Data Techniques & Tools

Data preprocessing techniques can be used to transform unstructured data into structured or semi-structured formats that can be analyzed and used to make data-driven decisions. For example, natural language processing and computer vision can be used to extract key features and information from video content and transform it into a more organized format that can be analyzed with traditional data analysis tools and techniques.

One of the biggest challenges of working with unstructured data is that its volume and velocity require an immense amount of storage. Popular storage tools include data lakes, NoSQL databases, and cloud storage services that use object storage.

It should be noted that several data center real estate investment trusts (REITs) are expanding their infrastructures to provide the physical facilities, bandwidth, and power needed to store massive amounts of unstructured data. Because this demand is expected to continue, it has created a favorable investment environment for data center platforms like Equinix and Digital Realty.

Unstructured Data Use Cases

While structured data is ideal for transactional and operational uses such as tracking inventory or processing sales transactions, unstructured data is better suited for interpreting multimedia content and capturing qualitative insights.

Analyzing unstructured data can reveal valuable insights into customer sentiment, market trends, and emerging patterns that aren’t obvious by analyzing structured data alone.

Use case	Type of unstructured data	Processing techniques	Benefits
Analyze customer sentiment in social media posts and comments.	Text	NLP	Understand brand recognition and sentiment.
Identify individuals or objects in surveillance footage.	Images, video	Computer vision, ML	Enhance security measures and threat detection.
Convert audio recordings into text for documentation and analysis.	Audio	Speech recognition, NLP	Make audio content searchable.
Detect phishing emails.	Text	NLP, ML	Protect users from malicious spam.
Provide automated responses to customer inquiries using past interaction data.	Text	NLP, AI Chatbots	Improve customer satisfaction.
Analyze medical images and scans.	Images	Deep learning, Image processing	Enhance the accuracy and speed of medical diagnoses.
Extract insights from product reviews to inform business strategies.	Text	Sentiment analysis, NLP	Drive product improvements and marketing decisions.
Interpret voice commands	Audio	Speech recognition, NLP	Provide hands-free device operation.
Analyze contracts to extract key clauses and obligations.	Text	NLP, text mining	Reduce manual review time and identify legal risks.
Use unstructured sensor data to predict equipment failures before they occur.	Sensor data	ML, data mining	Minimize downtime and maintenance costs.
Identify fraudulent activities.	Text, logs	ML	Detect anomalies in unstructured data.
Monitor environmental changes using satellite imagery.	Images	Image processing, Computer vision	Support environmental policies.
Collect and summarize news articles from a variety of sources.	Text	NLP, summarization algorithms	Keep users informed by expanding their filter bubble.

Unstructured Data Pros and Cons

Working with unstructured data presents both exciting opportunities and significant challenges. Here are some of the pros and cons of using unstructured data in business.

Pros

Offers deeper insights into complex aspects of market trends and human behavior
Provides a holistic view of a specific human’s actions from a wide variety of sources
Improves customer experience management (CXM) and give businesses a competitive edge

Cons

Complicates extraction of actionable insights due to large volume
Demands significant storage capacity and processing capacity, potentially increasing operational costs
Consumes time and resources during preparation
Requires human expertise and domain knowledge for accurate analysis
Can contain biases that need to be carefully addressed during analysis to avoid unfair conclusions

The Bottom Line

Unstructured data, by definition, is information that lacks a predefined format or organizational structure. The value of unstructured data depends on the quality of the data, and how easily it can be used to answer specific questions or meet business goals.

For example, a collection of social media posts can be valuable for understanding customer sentiment. However, if the data is full of irrelevant posts or spam, its value diminishes. Similarly, if the organization lacks the tools or expertise to analyze the unstructured data effectively, the potential value of this type of data will remain untapped.