Structural data refers to data that is organized in a predefined, systematic format, making it easily searchable, storable, and analyzable. This type of data is often stored in databases and spreadsheets, where it can be represented in rows and columns with clearly defined fields, such as numeric, categorical, or time-based values. Structural data is commonly used in data analysis, reporting, and automated processing.

### Key Features of Structural Data: 1. Predefined Format: Data follows a specific structure, such as tables, with labeled rows and columns. 2. Easily Searchable and Manageable: The organized nature allows for easy querying, filtering, and sorting. 3. Relational Databases: Structural data is typically stored in relational databases (e.g., SQL databases), which use a schema to define how data is related and organized.

### Examples of Structural Data: - Customer Records: A table containing customer information with columns for names, addresses, phone numbers, and purchase history. - Financial Data: Spreadsheet of financial transactions with columns for date, transaction amount, and account number. - Sensor Data: Time-stamped readings from a sensor stored in a table format with fields for time, value, and sensor ID.

Because structural data is well-organized, it is highly suitable for analytical tools, machine learning algorithms, and database systems that require consistent, easily interpretable input.