SUGI 27: The Knowledge Warehouse: The Next Step Beyond the Data Warehouse

Retaining and applying knowledge enables an organization to know what to do, how to do it, and when to do it, empowering it to succeed in competitive situations. Data warehouses store data and can be a source of knowledge but do not store knowledge directly. However, knowledge in the sense of procedures, best practices, business rules, expert knowledge, facts within context, and processed data can be retained in logical structures accessible by computers. This paper describes the techniques and tools for building a knowledge warehouse and compares these to the data warehouse construction model. The data warehouse "extract, transform, load" (ETL) process has a parallel in the knowledge warehouse. The knowledge warehouse also has logical structures to store knowledge that are analogous to the system of tables that implement data storage in the data warehouse. Knowledge is applied through a layered representation that is readable by both humans and machines and that shields code until the bottom layer. This representation is also a system executable that is portable and can be run under SAS on a computer to help make decisions and take actions.