Structured vs. Unstructured Data: Understanding the Differences and How to Protect Both

Data is the backbone of the modern organization. Today, organizations generate, process, store, and manage more data than ever before.  

As the volume of data continues to skyrocket, the importance of protecting it rises along with it. This data can be broadly classified into two categories: structured and unstructured data, each with its own set of challenges and protection requirements. 

What is structured data? 

Structured data is highly organized, easily searchable, and typically stored in databases. This data follows a specific format, such as rows and columns in a database. Examples of structured data include customer information, transaction records, and inventory data.  

These data types are easy to manage and analyze due to their predefined structure. 

What is unstructured data? 

Unstructured data is information that lacks a specific format, structure, or schema. Because it does not conform to traditional data structures, it is harder to identify and more challenging to analyze, store, and manage than traditional data management systems. 

Types and characteristics of unstructured data 

Text documents: Found in Word documents, PDFs, or plain text files containing unorganized information, such as articles, reports, and contracts. 

Emails: Significant share of unstructured data, including message text, attachments, metadata, and communication threads. 

Multimedia files: Includes images, audio files, and videos, often containing vast information without a consistent format. 

Social media posts: Content from platforms like LinkedIn, Twitter, TikTok, Facebook, and Instagram, including text, images, videos, and metadata. 

The 4 Vs of unstructured data  

Understanding unstructured data means grappling with its scale, complexity, and unpredictability. To make sense of it all, let’s break it down as the 4 Vs — a simple yet powerful way to describe the defining traits of unstructured data.

These four dimensions — volume, variety, velocity, and veracity — capture the reasons why unstructured data is so difficult to manage, and why protecting it requires more than just traditional tools.

Volume: Exponentially growing due to digital communication, social media, and internet-connected devices. 

Variety: Numerous formats and types, making it difficult to manage and analyze. 

Velocity: Rapid generation and sharing pace, creating significant storage, processing, and security challenges. 

Veracity: Varying quality and accuracy, requiring investment in data validation and cleanup. 

What are the challenges of protecting unstructured data?  

The massive volumes of unstructured data pose potential risks to organizations.  

Here are the key risks and challenges associated with unstructured data. 

Data breaches: Unprotected or poorly managed unstructured data is vulnerable to cyber-attacks, potentially resulting in data breaches and unauthorized disclosure of sensitive information. 

Compliance issues and risks: Adherence to data protection regulations, such as GDPR and CCPA, requires proper management and protection of personal data, including unstructured data. 

Storage and management challenges: The sheer volume and variety of unstructured data can strain organizational resources, requiring adequate storage, processing power, and efficient management practices. 

Lack of standardized format: The lack of a consistent structure makes it difficult to apply uniform security measures.   

Identification and categorization hurdles: Identifying and classifying sensitive unstructured data is labor-intensive and time-consuming.   

Limited access controls: Unstructured data often has minimal or inconsistent access controls, greatly increasing the risk of unauthorized access.   

Increased vulnerability to cyber-attacks: As cybercriminals become more sophisticated and resourceful, unstructured data becomes even more attractive. Given the importance and potential risks associated with unstructured data, it is crucial for organizations to invest in effective strategies and solutions to safeguard it.   

Want are some effective strategies for protecting unstructured data?  

Regardless of format, the core components of a sound data protection strategy are the same: identify, classify, and remediate. For unstructured data, these actions require smarter tools and scalable approaches.

Data inventory and classification: Identify sources of unstructured data and categorize them based on sensitivity. 

Implementing access controls and permissions: Use role-based access control and the least privilege principle (like zero trust) to limit access to sensitive data. 

Data encryption: Encrypt data in transit and at rest to protect it from unauthorized access. 

Monitoring and auditing: Regularly review access logs and proactively address suspicious activities to maintain data security. 

Ultimately, the best solutions for protecting unstructured data are those that leverage AI and Machine Learning. AI-driven data classification speeds up the process of identifying and categorizing sensitive data. At the same time, AI-powered anomaly detection and threat prevention tools can detect and prevent threats in real-time, reducing the risk of data breaches.  

Plus, machine learning algorithms can analyze user behavior and suggest appropriate access controls. 

Protecting both data types 

To truly protect their information assets, organizations need to adopt unified strategies that address both structured and unstructured data.

A complete data protection program delivers visibility into all content types, enforces consistent policies, and enables insight-driven decisions based on comprehensive data intelligence.

Understanding how structured and unstructured data differ is the first step. But recognizing the scale and risk associated with unstructured data is what drives organizations to invest in the right solutions.

Identifying sensitive unstructured data with Concentric AI  

With Concentric AI, organizations can identify, classify and remediate risk to both structured and unstructured data — wherever it is stored and regardless of format. Our solution can even identify sensitive data in audio and video.  

Concentric AI delves into a deep semantic contextual analysis of all data. By understanding the nuances of the data, Concentric AI can clearly identify potentially sensitive information. 

After sensitive data is identified, our solution classifies the data based on its significance and sensitivity and categorizes it appropriately — perhaps as ‘confidential’ or for ‘internal use’. Automated policy applications kick in, aligning the data management to the organization’s predefined policies — whether that means encryption, restricted access, or managerial reviews.   

But what truly sets Concentric AI apart is our ability for continuous learning. Much like with text-based data, as our large language models process more data, Semantic Intelligence continually refines its algorithms. Concentric AI will adapt to new patterns and consistently improve accuracy, ensuring organizations are always a step ahead in protecting their sensitive data from risk.   

Want to see firsthand, with your own data, how you can quickly and easily deploy Concentric AI’s solution and identify unstructured sensitive data?  

Book a demo today, and you’ll experience the freedom of classifying all your data — structured and unstructured — without rules, regex, or end-user involvement.