How does data in a data warehouse differ from application data?

How does data in a data warehouse differ from application data?

• A. Data in the warehouse is always current
• B. Data is organized functionally rather than by subject
• C. Data in a warehouse is integrated and time-variant
• D. Data warehouses focus solely on real-time data

Answer: (C)

Data in a data warehouse is characterized by being integrated and time-variant, which is the primary reason why this option is the most accurate. In a data warehouse, data from various sources is consolidated, cleaned, and transformed to provide a comprehensive view of the organization's information. This integration allows businesses to analyze data from multiple perspectives, often across different departments or functions. The "time-variant" aspect reflects the way data warehouses store historical data, enabling the analysis of changes over time. This means that users can track trends, review performance, and glean insights based on data collected at different points in time. This chronological dimension is crucial for business intelligence and decision-making processes. In contrast, application data is typically maintained in operational systems, which focus on current transactions and data processing needs. Such systems are designed for day-to-day operations rather than historical analysis, resulting in differences in structure, purpose, and the temporal nature of the data being stored. Therefore, the integrated and time-variant nature of warehouse data sets it apart from standard application data.

Data in a data warehouse is characterized by being integrated and time-variant, which is the primary reason why this option is the most accurate. In a data warehouse, data from various sources is consolidated, cleaned, and transformed to provide a comprehensive view of the organization's information. This integration allows businesses to analyze data from multiple perspectives, often across different departments or functions.

The "time-variant" aspect reflects the way data warehouses store historical data, enabling the analysis of changes over time. This means that users can track trends, review performance, and glean insights based on data collected at different points in time. This chronological dimension is crucial for business intelligence and decision-making processes.

In contrast, application data is typically maintained in operational systems, which focus on current transactions and data processing needs. Such systems are designed for day-to-day operations rather than historical analysis, resulting in differences in structure, purpose, and the temporal nature of the data being stored. Therefore, the integrated and time-variant nature of warehouse data sets it apart from standard application data.