What are "dimensions" in the context of data warehousing?

What are "dimensions" in the context of data warehousing?

• A. Management processes behind data operations
• B. Quantitative measures of business performance
• C. Descriptive attributes that provide context to fact data
• D. Data transformation methods used during processing

Answer: (C)

In the context of data warehousing, "dimensions" refer to descriptive attributes that provide context to fact data. Dimensions are used to categorize, filter, and summarize the quantitative data stored in the fact tables. They help to create a meaningful analysis of the data by providing additional information that can give insights into various aspects of the data. For example, in a retail data warehouse, common dimensions might include time (e.g., day, month, year), product information (e.g., product name, category), and customer details (e.g., demographic information, location). By using these dimensions, analysts can perform queries that help to understand sales performance across different periods, product categories, or customer segments. The other options highlight different concepts that are not characteristic of dimensions. Management processes behind data operations relate more to data governance or operational management rather than how data is structured in a warehouse. Quantitative measures of business performance are often referred to as "measures" or "facts", which are typically stored in fact tables. Data transformation methods pertain to the processing and cleansing of data, which are separate activities that precede the analysis of dimensional data. Thus, while each of the other options touches on pertinent data-related concepts, dimensions specifically focus on providing the descriptive

In the context of data warehousing, "dimensions" refer to descriptive attributes that provide context to fact data. Dimensions are used to categorize, filter, and summarize the quantitative data stored in the fact tables. They help to create a meaningful analysis of the data by providing additional information that can give insights into various aspects of the data.

For example, in a retail data warehouse, common dimensions might include time (e.g., day, month, year), product information (e.g., product name, category), and customer details (e.g., demographic information, location). By using these dimensions, analysts can perform queries that help to understand sales performance across different periods, product categories, or customer segments.

The other options highlight different concepts that are not characteristic of dimensions. Management processes behind data operations relate more to data governance or operational management rather than how data is structured in a warehouse. Quantitative measures of business performance are often referred to as "measures" or "facts", which are typically stored in fact tables. Data transformation methods pertain to the processing and cleansing of data, which are separate activities that precede the analysis of dimensional data. Thus, while each of the other options touches on pertinent data-related concepts, dimensions specifically focus on providing the descriptive