TDWI Summer

Prerequisite: This course assumes a general understanding of DW.

Claudia Imhoff

Ph.D.

Some companies can afford to spend hundreds of thousands or millions of dollars to build a data warehouse, while others cannot. There is hope even for companies who fall into the latter group. This session begins with a brief description of a full-scale data warehouse architecture and the methodology required to implement it. That description provides the foundation for the remainder of the course. For companies with limited funding, the importance of an early hit is magnified. This course will describe alternative approaches for providing a quick and relatively inexpensive business deliverable. Cost estimates and estimating factors will also be presented.

You Will Learn

• Data warehouse architecture and methodology: A brief review of a sustainable data warehouse architecture and the methodology required to implement it
• Leadership and scope: Emphasis on the importance of the project sponsor for providing leadership, and of the scope document for establishing expectations; desired and detrimental traits for the team leader will also be discussed
• Planning: Description of the critical program-oriented activities that ensure the warehouse built with the limited resources can eventually fit within a sustainable architecture
• Component analysis: Review of the approaches for building each of the architecture components—the data warehouse and data marts—with minimal cost
• Infrastructure: Review of the compromises that can be made with the infrastructure components to get the warehouse implemented with a minimal investment
• Methodology analysis: Provide a streamlined methodology aimed at providing a quick and inexpensive data warehouse and/or data mart; the importance of versatility within the team and steps that can be taken to minimize the complexity will be described
• Communication: Description of the key components of a communication plan and explanation of the importance of this plan

Geared To

• Project managers; project team members; business users of the data warehouse