Time to read: 5 minutes | Published: October 8, 2025
Structured data What is structured data?
Structured data is information that is organized in a standard format that makes it easily accessible and understandable by both humans and machines. Structured data is typically organized using well-defined schema that defines the relationship between the different data fields. The highly organized nature of structured data makes it easy to search, query, and analyze using a variety of tools and techniques. Customer information such as names, addresses, phone numbers, and email addresses are examples of structured data.
What are examples of structured data?
- Relational databases and spreadsheets hold structured data in rows and columns. It is straightforward to save, access, and analyze. Customer, financial, and personnel data are kept in fields like names, transaction amounts, and job descriptions. This arrangement streamlines data querying and analysis.
- In healthcare, structured data is used to record patient information, medical records, medications, etc. Retail and e-commerce companies use it to track inventories, sales transactions and product details. A database can also include product ID, name, price, stock level, and supplier information. Web analytics uses this structured data to track website visits, bounce rates, conversion rates, and session durations.
- In the Internet of Things (IoT), sensors gather temperature, humidity, location coordinates, and timestamps in an organized way. SQL queries and analyzes this data in databases. Businesses can easily produce reports, run analytics, and make data-driven choices with structured data.
What are AI opportunities for structured data?
AI opportunities to improve structured data analysis, administration, and use include:
- Predictive analytics: Regression and classification machine learning models can forecast client purchase patterns, inventory demands, and financial results using structured data.
- Data cleanup and quality improvement: AI can automatically find and fix mistakes, inconsistencies, and missing values in structured data, improving data quality and decision-making.
- Automation of data processing: Machine learning and Robotic Process Automation can automate data entry, categorization, and integration from numerous sources. Enterprises dealing with massive amounts of data can improve analytics, save time, and enhance operational efficiency.
- Better insights and pattern recognition: AI can cluster and classify structured data to reveal hidden patterns and insights, enabling organizations make data-driven choices, streamline operations, and find new possibilities.
- Customization and advice: AI may use structured data like user preferences and behavior to personalize e-commerce product suggestions and streaming content, helping retail enterprises maximize efficiency and identify targeted marketing opportunities.
- Scam detection and risk management: AI can detect and prevent financial fraud and insurance claim abnormalities by analyzing structured data in real time, a huge benefit for financial institutions and healthcare providers.
How can HPE help with structured data?
HPE offers advantages where the competition falls short, with a variety of products and services for structured data including:
- HPE Alletra Storage MP B10000: A modern storage solution.
- The industry’s first disaggregated, scale-out block and file storage system that offers independent scaling of performance and capacity that leads to 40% lower TCO and 45% lower power consumption, simplified cloud experience, efficient scale, and guaranteed 100% data availability with industry-leading SLAs for mission-critical apps as a standard feature.
- Real-time AI-powered ransomware detection and recovery, back by a Cyber Resiliency Guarantee, significantly reducing data loss and downtime.
- Up to 30% cost reduction with a free controller refresh for evolving workloads, enabling non-disruptive, data-in-place upgrades so you don’t need to repurchase existing storage.
- 99% operational time savings with intelligent, self-service provisioning, and a single consistent cloud console to manage and protect workloads across on-prem and public clouds.
- HPE AIOps with Data Services Cloud Console (DSCC): A unified management control plane that includes AI-driven predictive analytics to manage and optimize storage operations. It helps businesses ensure the reliability, performance, and efficiency of their data storage systems by proactively identifying and resolving potential issues. HPE AIOps also provides cross-stack analytics, while competitors fall short with limited analytics and bespoke management interfaces.
- HPE GreenLake: a powerful orchestration and management layer for hybrid and multi-cloud environments, featuring the most comprehensive suite of storage services—including block, file, object, and private cloud. HPE GreenLake offers a centralized, intuitive control plane. It enables unified provisioning, monitoring, cost optimization, and security enforcement across all environments—on-prem, colocation, and public cloud—streamlining operations and reducing complexity. HPE also offers Storage-as-a-Service where customers can pay only for what they use through HPE GreenLake Flex.
Learn more about HPE’s latest storage advancements by checking out the HPE Alletra Storage MP B10000 announcement blog.
Structured data vs. unstructured data
Features | Structured Data | Unstructured Data |
|---|---|---|
| Definition | Organized information stored in a predefined format; (e.g., tables) | Information without a predefined format or structure |
| Schema | Follows a fixed schema (e.g., database tables with rows/columns) | No fixed schema; data is stored in its native format |
| Storage | Stored in relational databases (e.g., SQL databases) | Stored in data lakes, NoSQL databases, or file systems |
| Searchability | Easily searchable using query languages like SQL | Requires advanced tools like AI, NLP, or search engines to analyze |
| Examples | Names, dates, addresses, financial transactions | Emails, videos, images, social media posts, audio files |
| Use Cases | Reporting, analytics, business operations, and transactional systems | Sentiment analysis, image recognition, big data analytics |
| Analysis Complexity | Simple and straightforward | Complex; requires specialized tools and techniques |
Structured data FAQs
Where is structured data typically stored?
It is usually stored in relational databases, data warehouses, and spreadsheets, where it can be queried using SQL or other structured query tools.
Why is structured data important?
It supports fast search, reporting, and analysis—critical for business intelligence, compliance, and operational systems where accuracy and consistency are required.
What industries rely most on structured data?
Finance, healthcare, retail, government, and logistics all rely heavily on structured data for transactions, compliance, customer management, and performance tracking.
What tools are used to manage structured data?
Relational database management systems (RDBMS), data warehouses, SQL, and ETL (extract, transform, load) pipelines are the primary tools for managing structured data.
What are the limitations of structured data?
It cannot easily capture complex or unformatted information such as video, audio, or free-form text, which limits its scope compared to unstructured or semi-structured data.
Can structured and unstructured data be combined?
Yes—many organizations integrate structured and unstructured data in data lakes or modern analytics platforms to gain a fuller view of business and customer activity.