What kind of data can typically be analyzed through a Data Warehouse?

The correct choice reflects the comprehensive nature of a Data Warehouse, which is designed to facilitate analysis by integrating and storing a variety of data types over time. Data Warehouses typically aggregate historical and current data from multiple sources, including transactional systems, to provide a unified view for reporting and analysis. This allows businesses to analyze trends, make forecasts, and gather insights from both past and present datasets.

Historical data is crucial for understanding trends and patterns over time, while current data helps in making informed decisions based on the most up-to-date information available. By combining these two data types, organizations can derive more valuable insights, leading to better decision-making and strategic planning.

Other options, like focusing only on transactional or external data, restrict the potential insights a Data Warehouse can provide. Regarding the limitations of real-time data, it is worth noting that while some modern Data Warehouses incorporate elements of real-time data processing, their primary function is to store and analyze data collected over time, rather than to serve as a platform solely for real-time analytics. This understanding highlights why the option emphasizing both historical and current data is the most accurate in describing the capabilities of a Data Warehouse.