Definition and purpose of Multidimensional Data Models.

Importance in analyzing data through various dimensions.

Common applications in OLAP and data warehousing.

Assembling data from clients to ensure accuracy.

Grouping data based on system segments for easier management.

Identifying key dimensions from user perspectives.

Preparing and identifying relevant attributes of these dimensions.

Differentiating actual data from collected factors.

Constructing a schema based on the collected information.

Illustrations of sales data in a multifactor model.

Representation of 2D and 3D data cubes for analysis.

Understanding measures and dimensions through examples.

Measures: Numerical data for analysis stored in fact tables.

Dimensions: Descriptive attributes stored in dimension tables.

Cubes: Structures enabling multidimensional data relationships.

Hierarchies: Organizing dimensions for detailed exploration.

Aggregation, drill-down, and roll-up capabilities.

Advantages include ease of handling, better performance, and complex system compatibility.

Disadvantages are complexity, potential inefficiencies due to caching, and vulnerability to security breaches.