Chapter 5. Business Analytics at the Data Warehouse Level
In Chapter 4, we looked at the processes that transform raw warehouse data into information and knowledge. Later on, in Chapter 6, we will look at the typical data-creating source systems that constitute the real input to a data warehouse.
In this chapter, we discuss how to store data to best support business processes and thereby the request for value creation. We'll look at the advantages of having a data warehouse and explain the architecture and processes in a data warehouse. We look briefly at the concept of master data management, too, and touch upon service-oriented architecture (SOA). Finally, we discuss the approaches to be adopted by analysts and business users to different parts of a data warehouse, based on which information domain they wish to use.
WHY A DATA WAREHOUSE?
The point of having a data warehouse is to give the organization a common information platform, which ensures consistent, integrated, and valid data across source systems and business areas. This is essential if a company wants to obtain the most complete picture possible of its customers.
In order to gather information about our customers from many different systems to generate a 360-degree profile based on the information we have about our customers already, we have to join information from a large number of independent systems, such as:
Billing systems (systems printing bills)
Reminder systems (systems sending out reminders, if customers do not pay ...