Many of the topics related to business intelligence, such as data integration and data warehousing, can be understood as solutions to problems concerning abstraction, accessibility, and delivery of data.
In previous chapters, you learned that the data warehouse provides a substantial deal of abstraction from the raw data accumulated in various data sources. Central to that achievement is the organization of data into subject-oriented star schemas, considerably reducing the complexity of translating questions from the business end to database queries.
Although establishing a data warehouse solves some of the data abstraction and accessibility issues, it is still not ideal for delivering data to reporting tools. Business users trying to get data from the warehouse may struggle to get the information they want in a format they can understand, or the system may need to be tweaked to make sure the data can be accessed in a useful way. In this chapter, you learn how the addition of a metadata layer can help in this respect.
In this first section, we explain briefly what kinds of things we are talking about when we use the term "metadata," and what problems it solves. Later in this chapter, we take a closer look at using Pentaho metadata.
The term metadata is a bit overused. In a general sense, it means "data about data." Depending upon the context, there are a lot of different things to say "about" data, and technically this ...