Every day, organisations generate loads of data but did you know that over 80% of it is unstructured? From emails and documents to videos, social media posts and AI training datasets, unstructured data holds the insights that structured tables simply cannot capture. Understanding it is not just a nice-to-have but is important to powering AI, predictive analytics and modern cloud applications. In this blog, we break down what unstructured data is, why it matters and how it can be stored, accessed and used at scale.
What is Unstructured Data?
Unstructured data is any data that lacks a predefined structure or consistent data model. It does not fit neatly into relational tables and cannot be easily queried using traditional SQL-based databases. It comprises the majority of enterprise data and includes text, multimedia and sensor data.
Instead of structured fields, unstructured data is typically stored as complete files or objects with meaning derived from the content itself rather than a schema.
Examples of Unstructured Data
Unstructured data appears across almost every modern workload. Each of these data types varies in size, format and structure which is why they are grouped under unstructured data.
- Text documents such as PDFs, Word files and emails
- Images and videos from cameras, applications and user uploads
- Audio recordings and voice data
- Application logs and telemetry data
- Social media posts and customer feedback
- Training datasets for machine learning models
How Unstructured Data is Interpreted
Because unstructured data lacks a schema, it is interpreted using:
- Metadata attached to the file or object
- Indexing and search engines
- Natural language processing (NLP)
- Computer vision and speech-to-text models
Key Aspects of Unstructured Data
Unstructured data behaves in a different manner from traditional database data. To store, process and analyse it, you must understand its main characteristics.
1. High Volume and Continuous Growth
Unstructured data is generated at a massive scale. User uploads, application logs, media files and machine-generated data grow and often unpredictably.
Unlike structured datasets, which tend to grow in controlled increments, unstructured data volumes can spike suddenly. The examples include video uploads, AI training datasets or system telemetry during peak traffic. This growth pattern makes capacity planning difficult and requires storage systems that can scale without manual intervention.
2. Wide Variety of Data Types
Unstructured data includes many formats, sizes and content types:
- Text (documents, emails, chat logs)
- Images and video
- Audio files
- JSON, logs and semi-structured machine data
- Binary files and backups
Each format has different access patterns and performance needs. A single system may need to store kilobyte-sized text files alongside multi-gigabyte video or model checkpoints. This variety is one of the main reasons unstructured data cannot be handled efficiently by relational databases.
3. Schema-on-read Instead of Schema-on-write
Structured data applies a schema before data is written. Unstructured data follows a schema-on-read approach.
This means:
- Data is stored first without enforcing a structure
- Structure is applied later during analysis or processing
- Different tools can interpret the same data in different ways
For example, a log file can be parsed differently for security analysis, performance monitoring or debugging. The underlying data remains unchanged.
4. Metadata-driven Organisation
Since unstructured data lacks inherent structure, metadata plays an important role. Metadata enables search, classification, lifecycle management and access control without modifying the underlying data.
Metadata may include:
- Object name and size
- Creation and modification timestamps
- Content type
- Custom tags such as project, customer or workload
5. Complex Access and Processing Patterns
Unstructured data is accessed in different ways depending on the workload:
- Sequential reads for video streaming
- Random access for analytics
- Parallel reads for AI training
- Write-once, read-many patterns for backups
Why Unstructured Data Matters
Unstructured data matters because it contains most of the information organisations rely on for insight, automation and decision-making today.
1. Has the Majority of Enterprise Data
According to the IDC, 80-90% of the world’s data is unstructured. Organisations generate massive volumes of information every day. Emails, documents, media files, logs and user-generated content account for most data created by modern applications and systems.
2. Powers AI and ML Workloads
LLMs, computer vision and speech systems are trained primarily on unstructured data such as text, images and audio.
3. Captures Real User Behaviour and Context
Customer feedback, chat logs, support tickets and social content provide signals that structured data cannot represent.
4. Enables Advanced Analytics Beyond Dashboards
Search, sentiment analysis, pattern detection and predictive analytics depend on analysing unstructured content.
5. Drives Competitive Differentiation
Organisations that can store, process and extract value from unstructured data gain faster insights and better automation than those limited to structured datasets. See the difference between the two below:
Structured Data vs Unstructured Data
|
Data Types
|
Structured Data
|
Unstructured Data
|
|
Data organisation/format
|
Predefined schema for easy organisation
|
Wide range of formats with no fixed schema
|
|
Ease of analysis
|
Straightforward analysis using traditional tools like CRMs and SQL databases
|
More complex to analyse, often requiring AI, ML or search tools
|
|
Scalability/storage requirements
|
Compact and efficient storage
|
Large, complex datasets that grow rapidly
|
|
Examples
|
Customer records, transaction tables and inventory data
|
Images, videos, documents, emails, logs
|
|
Typical storage systems
|
Relational databases
|
Object storage and distributed file systems
|
What are Use Cases for Unstructured Data?
With the right tools, unstructured data can support a range of modern analytics and AI-driven workloads, such as:
Generative AI
Unstructured data, including text, images, audio and video, forms the foundation for training and fine-tuning generative AI models. By using this diverse data, AI systems can generate realistic content, summarise documents, complete code and produce multimodal outputs. Organisations can create personalised experiences, automate content creation and enhance creative workflows.
Retrieval Augmented Generation
RAG combines unstructured data storage with AI model capabilities to improve response accuracy. By retrieving relevant documents, knowledge bases or multimedia content, AI systems can provide context-specific and up-to-date answers. This ensures that generated outputs are grounded in actual data. RAG is useful for enterprises managing large knowledge repositories or dynamic information sources.
Customer Behaviour and Sentiment Analysis
Unstructured data from sources like social media, reviews, chat logs and support tickets reveals customer sentiment and preferences. Analysing this data helps identify trends, detect issues and uncover unmet needs. Businesses can use these insights to improve products, enhance customer experiences and create targeted marketing strategies.
Predictive Data Analytics
Historical unstructured data, including system logs, sensor readings and user interactions, enables predictive analytics. By identifying patterns and correlations, organisations can forecast equipment failures, demand shifts or anomalous behaviour. Predictive analytics transforms raw and unstructured inputs into actionable insights.
Chatbot Text Analysis
Chatbots process unstructured text from user queries, chat history and support tickets to understand intent and extract meaning. By analysing this data in real time, AI systems generate relevant, context-aware responses that improve user engagement and satisfaction. Text analysis enables chatbots to learn, handle complex queries and personalise interactions.
Object Storage for Unstructured Data
Object storage is a data storage architecture designed to handle massive volumes of unstructured data. Compared with SSV (Shared Storage Volumes), object storage supports multi-read and multi-write operations, allowing multiple clients to access or update the same object concurrently.
Object storage offers several advantages, such as:
- Scalability: Its flat architecture allows you to scale and avoid the limits often encountered with traditional file or block storage.
- Concurrent Access: Multiple clients can read and write simultaneously to removethe bottlenecks of single-volume storage.
- Simplified Management: Object storage makes data retrieval straightforward, no matter the file path.
- Enhanced Searchability: Metadata is embedded in every object for quick search and organisation. Tag objects with attributes like cost, usage or retention policies to keep everything under control.
- Built-in Resiliency: Data can be automatically replicated across devices or even regions, protecting against outages, loss and improving disaster recovery.
Why Use Hyperstack Object Storage
You should choose Hyperstack Object Storage because it is optimised for unstructured data. You can store and manage unstructured data like logs, datasets and media at scale.
-
Smarter Cost Control: Designed for high-volume usage, Hyperstack Object Storage uses a pay-as-you-go model, letting you use high-volume storage while keeping costs predictable.
-
Fully S3 Compatible: Connect instantly with existing tools and SDKs such as S3cmd, Boto3 Python SDK, MinIO. Client (mc) and more.
-
Efficient Metadata Handling: Add custom metadata to every object, making it easier to search, categorise and retrieve exactly what you need, when you need it.
-
Multipart Upload Support: Hyperstack Object Storage supports multipart uploads, enabling faster and more reliable transfers for large files through parallel uploads and automatic retry handling.
Manage Your Unstructured Data with Hyperstack Object Storage
Get reliable, cost-efficient and scalable storage built for modern, data-heavy workloads. With high durability, S3-compatible access and seamless scalability, you can store and manage your data without worrying about growth or performance limits.
FAQs
What is unstructured data with an example?
Unstructured data is data without a predefined format. Examples include emails, images, videos, PDFs, audio recordings and application log files.
Why is it called unstructured data?
It is called unstructured data because it does not follow a fixed schema, table structure or relational database format.
What best describes unstructured data?
Unstructured data is best described as content-based data where meaning is derived from text, media or files rather than predefined fields.
What is structured and unstructured data?
Structured data is organised in rows and columns with a fixed schema, while unstructured data has no predefined format and includes text, images and multimedia.
What is the best storage for unstructured data?
Object storage is the best storage option for unstructured data due to its scalability, durability and support for metadata-driven access.
Why choose object storage for unstructured data?
Object storage is designed for large-scale unstructured data, offering high durability, API-based access and efficient handling of diverse data types.
