What Is Unstructured Data?

5 min. read

Unstructured data is data that lacks a predetermined structure or format. Examples include text documents, images, and audio files. Unlike structured data such as database tables, unstructured data doesn’t conform to a consistent data model and schema, which makes it more difficult to search, sort, and analyze.

Unstructured data is often stored in blob storage or a data lake — specialized file-based storage systems that are designed to handle large amounts of unstructured data.

In the context of cloud data security, unstructured data can pose a challenge, proving more difficult to identify sensitive data assets as opposed to a relational database. Object storage might not support the same level of granular permissions and access control as found in popular databases.

Understanding Unstructured Data in the Cloud

The volume of unstructured data generated from sources like emails, documents, images, videos, and social media posts grows each day, challenging organizations to manage and extract insights from diverse and complex data that don’t fit neatly into traditional structured databases.

Five Common Types of Unstructured Data

  • Text files: Documents, emails, and source code files that contain unstructured text.
  • Images: Digital photographs, illustrations, and scanned documents in formats like JPEG, PNG, or GIF.
  • Audio files: Recorded voice, music, or sound effects in formats such as MP3, WAV, or FLAC.
  • Video files: Digital video recordings in formats like MP4, AVI, or MOV.
  • Social media posts: User-generated content on platforms like Facebook, Twitter, or Instagram, including text, images, and videos.

In response to this ever-growing volume of unstructured data, object and blob storage solutions have emerged as key technologies for addressing the challenges, at least in terms of facilitating efficient storage and management of unstructured data.

By offering scalable, durable, and versatile storage solutions, organizations can store vast amounts of data without compromising performance. Data durability is ensured through replication across multiple locations, protecting against data loss and hardware failures. Additionally, object and blob storage enable easy access and retrieval of unstructured data, making them suitable for content delivery, big data analytics, and IoT solutions.

Despite the benefits, storing sensitive unstructured data in object and blob storage raises security concerns. Organizations must implement proper security measures to ensure the safe handling of sensitive unstructured data in these storage solutions.

Unstructured Data and Challenges with Data Security

Emails, documents, images, social media posts, videos — these information sources don’t conform to a prescribed format or schema. Unstructured data inherently involves a lack of visibility.

Challenges with securing sensitive data embedded within unstructured data begin with identifying and classifying the data as sensitive. The lack of visibility leaves protected information, well, unprotected. Unable to apply security policies and controls consistently opens organizations to risks of data leak and data breach, as well as noncompliance with data protection regulations. In the absence of predefined schema, organizations struggle to detect potential security threats, unauthorized access, and policy violations. This raises concerns as more organizations move their data to the cloud.

Key Aspects of Data Security for Unstructured Data

To overcome the challenges associated with securing sensitive unstructured data and implement consistent security controls, organizations can adopt the following strategies:

Data Discovery and Classification

Accurate data classification is essential for implementing appropriate security controls, access policies, and ensuring compliance with data protection regulations — which means unstructured data must be categorized based on sensitivity levels to help determine the appropriate security controls for each data set.

Employ an automated tool like data security posture management (DSPM) to identify, classify, and label sensitive data within unstructured sources. This will enable organizations to apply appropriate security policies based on the sensitivity of the data.

Access Control and Identity Management

Implement rigorous access control mechanisms, including role-based access control (RBAC) and attribute-based access control (ABAC), to ensure that only authorized users have access to sensitive unstructured data. Fortify security with centralized identity management and multifactor authentication.

Data Encryption

Encrypt sensitive unstructured data both at rest and in transit using strong encryption algorithms. Manage encryption keys securely and ensure that decryption is only possible for authorized users. Leverage cloud service provider features for data encryption and key management. Many cloud service providers offer built-in encryption features for data storage and transfer.

Data Loss Prevention (DLP) Solutions

Deploy DLP solutions to monitor and prevent unauthorized sharing or leakage of sensitive unstructured data. DLP tools can identify sensitive data in various formats and apply predefined policies to prevent data leaks and breaches.

Monitoring and Auditing

Invest in advanced monitoring and auditing tools that can analyze unstructured data and identify unusual patterns or activities that may indicate security threats, such as unauthorized access, data breaches, or policy violations. Early detection of anomalies enables organizations to respond promptly to potential incidents.

Compliance and Governance

Organizations must ensure that their handling of unstructured data complies with applicable data privacy laws and regulations, such GDPR, HIPAA, or CCPA. Implementing appropriate security measures includes obtaining necessary consents from data subjects. Establish data governance policies that address data retention and deletion to help manage the lifecycle of unstructured data. Organizations must ensure that data is deleted securely and permanently when it’s no longer needed.

Employee Training and Awareness

Educate employees on the importance of data security, the risks associated with unstructured data, and best practices for handling sensitive information. Promote a security-conscious culture within the organization.

Collaborate with Secure Cloud Service Providers

Partner with reputable cloud service providers that follow industry-standard security practices and offer transparency in their security policies. Evaluate their security certifications, such as ISO 27001 or SOC 2, to ensure they meet your organization's security requirements.

By adopting these strategies, organizations can improve visibility and control over sensitive unstructured data, implement consistent security controls, and mitigate the risks associated with data leaks, breaches, and non-compliance.

Unstructured Data FAQs

Data management in the cloud encompasses the strategies, processes, and tools used to organize, store, protect, and maintain data across cloud environments. It ensures data availability, reliability, and security while optimizing storage and resource utilization. Cloud data management includes data ingestion, data quality management, data governance, access controls, data backup and recovery, and data lifecycle management.
Structured data is organized in a predefined format, such as databases, spreadsheets, and data tables, enabling efficient storage, querying, and analysis. Semi-structured data has some organization, but lacks a rigid schema, often employing tags or labels to define data elements. Examples include XML and JSON files. Unstructured data lacks a consistent structure, making it challenging to search or analyze without advanced techniques. Examples include text documents, emails, images, and videos.
The risks associated with unstructured data include potential data breaches, unauthorized access, and non-compliance with data protection regulations. As unstructured data lacks a predefined format, managing, securing, and analyzing it can be challenging. Organizations may struggle to implement consistent security policies, classify sensitive data, or monitor access effectively. Furthermore, unstructured data's diverse nature can make it more vulnerable to cyberattacks and data leaks, leading to reputational damage, financial loss, and legal penalties.

NLP is a subfield of AI and linguistics that focuses on enabling computers to understand, interpret, and generate human language. NLP encompasses a wide range of tasks, including sentiment analysis, machine translation, text summarization, and named entity recognition. NLP techniques typically involve computational algorithms, statistical modeling, and machine learning to process and analyze textual data.

A LLM is a type of deep learning model, specifically a neural network, designed to handle NLP tasks at a large scale. LLMs, such as GPT-3 and BERT, are trained on vast amounts of text data to learn complex language patterns, grammar, and semantics. These models leverage a technique called transformer architecture, enabling them to capture long-range dependencies and contextual information in language.

The primary difference between NLP and LLM is that NLP is a broader field encompassing various techniques and approaches for processing human language, while LLM is a specific type of neural network model designed for advanced NLP tasks. LLMs represent a state-of-the-art approach within the NLP domain, offering improved performance and capabilities in understanding and generating human-like language compared to traditional NLP methods.

Natural language processing, as a tool that enables computers to understand, interpret, and generate human language, can extract valuable information from text-based sources, such as documents, emails, and social media posts. Using techniques like tokenization, part-of-speech tagging, named entity recognition, and dependency parsing, it can break down and analyze unstructured text, making it easier to identify patterns and relationships.
Sentiment analysis, also known as opinion mining, is an NLP application that targets the sentiment or emotion expressed in a piece of text. It helps analyze unstructured data like customer reviews, social media comments, or survey responses. Sentiment analysis helps organizations understand public opinion, customer satisfaction, and market trends.

Text mining is the process of discovering knowledge, patterns, and insights from large volumes of unstructured text data. It combines NLP, data mining, and machine learning techniques to transform unstructured data into structured forms suitable for analysis.

Text mining applications include information retrieval, text classification, clustering, summarization, and topic modeling. By applying text mining techniques to unstructured data, organizations can uncover hidden trends, relationships, and actionable insights that may not be apparent through manual analysis.

Blob storage, or Binary Large Object storage, is a service offered by cloud providers for storing unstructured data, such as images, videos, documents, and other multimedia files. Blob storage allows users to store and manage massive amounts of data in a highly-scalable and cost-effective way. Designed for high availability, redundancy, and durability, blob storage ensures that data remains accessible and secure even in the event of hardware failures or network issues. It also supports data encryption, access control, and versioning, making it suitable for a range of use cases.
Blob storage enables content delivery by providing highly scalable storage with low latency access, making it suitable for serving multimedia files to a global audience. For backup and archiving, it ensures data durability and redundancy through replication across multiple locations, protecting against data loss and hardware failures. In big data analytics, blob storage can store large volumes of raw data for processing by analytics engines, such as Hadoop or Spark. IoT solutions benefit from blob storage's ability to handle diverse data types generated by sensors and devices, allowing for efficient storage, analysis, and real-time processing of collected data.
Object storage is a data storage architecture specifically designed to handle unstructured data by managing it as discrete units called objects. Each object consists of the data itself, associated metadata, and a unique identifier. This approach enables high scalability, durability, and cost-effectiveness, making object storage an ideal solution for cloud storage, big data, and backup and archiving applications.
Object storage and blob storage share similarities in purpose and functionality, as both are designed for storing unstructured data in a highly scalable and cost-effective manner. Blob storage, though, is a specific implementation of object storage, which is primarily used in the context of cloud storage services like Microsoft Azure Blob Storage and Amazon S3. While object and blob storage manage data as discrete units with metadata and unique identifiers, the terminology and features may differ slightly between cloud service providers.
Data ingestion in the cloud is the process of collecting, importing, and transforming data from various sources into a centralized storage system, such as a data lake or data warehouse. It supports batch, real-time, and streaming data, enabling organizations to process and analyze data more efficiently. Cloud-based data ingestion tools, such as Apache Kafka, Amazon Kinesis, and Azure Event Hubs, facilitate scalable and reliable data ingestion across multiple sources and destinations.