The warehouse stores information from OLTP systems and other sources of raw data external systems, for example in a single location, cleaned and processed into the right formats for analysis, such as summaries and derived data. Because information is coming from many sources, each with its own view of the data, a company must create an enterprise-level data model to have a consistent view of its information. This metadata is the blueprint for the pieces of the data warehouse architecture. This process is collectively known as data warehouse generation. Doing it right means the difference between finding answers that are valuable and answers that are useless.
Companies often place restrictions on what is extracted.
For example, the extraction process may occur every day, so any changes to the raw-data sources older than 24 hours are ignored. To get a complete view of a customer, a company may consolidate data from order entry systems, sales contact databases and technical support databases. For example, a company may want to know which products customers have ordered but not need the confirmation number used to process a sale. Filtering picks out the relevant data and removes duplicate entries. The classic example of poor-quality data is a single customer with multiple entries for example, John Jones, Jack Jones and J. Jones.