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.
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 |
