Final answer:
Normalization is not a process used in data warehousing; it's more common in database design. Data warehousing involves aggregation, indexing, and summarization to optimize for analytics and reporting. Secondary data is unobtrusive, known as nonreactive, meaning it does not interfere with the subjects of the study.
Step-by-step explanation:
A data warehouse typically involves various processes to make the data usable and efficient for analysis. These processes include aggregation, which is combining multiple pieces of data into a single summary, indexing, which involves creating indexes to speed up the retrieval of data, and summarization, which is simplifying or condensing data for easy understanding and interpretation. However, normalization is not a process typically used in data warehousing. Instead, normalization is more commonly associated with the design of databases in order to minimize redundancy and dependency by organizing data into tables and columns. In data warehousing, the focus is generally on denormalization for improved query performance as the data structure is designed to facilitate analytics and reports.
When considering the use of secondary data, it is described as unobtrusive or nonreactive. This means the data can be used for research without interfering with the subject from whom the data was collected, ensuring that their behavior remains unaffected by the study.