Structured Data
Data organised in a predefined format with clear schema, typically stored in databases with rows and columns.
In-Depth Explanation
Structured data is information organised in a defined format with a consistent schema. It fits neatly into rows and columns, making it easy to search, analyse, and process with traditional tools.
Characteristics:
- Follows predefined schema/model
- Organised in tables with rows and columns
- Each field has specific data type
- Easy to search and query
- Machine-readable
Examples:
- Relational database tables
- Spreadsheets
- CSV files
- Transaction records
- CRM records
Structured vs semi-structured vs unstructured:
- Structured: Fixed schema, relational databases
- Semi-structured: Flexible schema (JSON, XML)
- Unstructured: No schema (documents, images, audio)
For AI/ML:
- Easiest to work with
- Traditional ML algorithms work well
- Features clearly defined
- Data quality issues still important
Business Context
Structured data is the foundation of traditional analytics and many ML applications. Most business operational data is structured.
How Clever Ops Uses This
We work extensively with structured data for Australian business AI, often combining it with unstructured sources for comprehensive solutions.
Example Use Case
"Customer transaction data in a database: customer ID, date, product, amount - each field clearly defined and queryable."