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?
- What are AI opportunities for structured data?
- How can HPE help with 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, saving time and enhancing 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.
- Scam detection and risk management: AI can detect and prevent financial fraud and insurance claim abnormalities by analyzing structured data in real time.
How can HPE help with structured data?
HPE offers 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 storage with a 100% data availability guaranteed with industry’s first disaggregated scale-out block storage architecture that offers simplified cloud experience, efficient scale, and guaranteed 100% data availability.
- HPE AIOps with Data Services Cloud Console: A unified management control plane that includes AI-driven predictive analytics to manage and optimize structured data. It helps businesses ensure the reliability, performance, and efficiency of their data storage systems by proactively identifying and resolving potential issues.
- HPE GreenLake: A flexible cloud service for storing and managing structured data that supports hybrid cloud environments and helps streamline data management across on-premises and cloud-based systems. HPE also offers Storage-as-a-Service where customers can pay only for what they use through HPE GreenLake Flex.
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 |
