How is a dimensional data model characterized?

How is a dimensional data model characterized?

• A. By its strict data quality controls
• B. As a fully-attributed perspective of the conceptual data model
• C. As an initial approach to data modeling
• D. By its dependence on specific technology

Answer: (B)

A dimensional data model is characterized as a fully-attributed perspective of the conceptual data model because it organizes data into dimensions and facts in a way that is intuitive for end users, facilitating data retrieval and analysis. This model emphasizes the relationships between data and allows for easy exploration of data from various perspectives. It is specifically designed to support business intelligence and analytic processes, providing a structured way to understand and use the underlying data for decision-making. The dimensional model incorporates attributes that define the dimensions and measures associated with the facts, making it easier to understand the context of the data. This comprehensive view aligns closely with the needs of end-users who seek to derive insights from large volumes of data through queries and reporting. In contrast, the other options do not accurately define the dimensional data model. Strict data quality controls pertain to data governance practices, a fully-attributed context is key to achieving actionable insights in a dimensional model rather than being general data modeling approaches, and dependence on specific technology does not capture the essence of what a dimensional model is fundamentally about; rather, it can be implemented across various platforms and tools without being limited to specific technologies.

A dimensional data model is characterized as a fully-attributed perspective of the conceptual data model because it organizes data into dimensions and facts in a way that is intuitive for end users, facilitating data retrieval and analysis. This model emphasizes the relationships between data and allows for easy exploration of data from various perspectives. It is specifically designed to support business intelligence and analytic processes, providing a structured way to understand and use the underlying data for decision-making.

The dimensional model incorporates attributes that define the dimensions and measures associated with the facts, making it easier to understand the context of the data. This comprehensive view aligns closely with the needs of end-users who seek to derive insights from large volumes of data through queries and reporting.

In contrast, the other options do not accurately define the dimensional data model. Strict data quality controls pertain to data governance practices, a fully-attributed context is key to achieving actionable insights in a dimensional model rather than being general data modeling approaches, and dependence on specific technology does not capture the essence of what a dimensional model is fundamentally about; rather, it can be implemented across various platforms and tools without being limited to specific technologies.