Best practices in data warehousing to support business initiatives and needs

Abstract

The paper presents the data warehousing architecture and practices used at a major U. S. retailing company. Many considerations were assessed when deciding which data warehousing architecture to adopt. The paper discusses the two pre-dominant styles in data warehousing, namely the "Bill Inmon Style" or the top-down approach and the "Ralph Kimball Style "or the bottom-up approach. The company chose the Inmon style due to a unique combination of circumstances in their business and technical environments, which are being discussed in detail. Much of the information presented in this paper is based upon the direct experiences of the lead data architect assigned to the projects under which this U. S. retailing company's customer data warehouse evolved. The architecture has evolved over time and currently has been accepted at the company as a best practice. It is interesting to mention that both the hardware platform (CPU and disk drives) and relational database management system (RDBMS) software employed today at this company for data warehousing is not the same as was selected for the first instantiation. The implication was that the best plan or practice was a flexible one. There were many challenges, like organizational, technical, data sourcing and data naming, needed to be solved during the pre-project, initial stages, and throughout the project and beyond. The initial data warehouse, implemented in 1996, was termed an overall success and approved for expansion. The current data warehouse data are being used by over six hundred registered users to fine-tune customer marketing and leverage and share data in an enterprise manner. The data warehouse has allowed the company to strengthen customer relationship management (CRM) core capabilities and business partnerships. Today, there are many departments benefiting from queries and requests for data warehouse data, many anticipated, some not. Although not planned, the data warehouse has been a valuable source of purchase and customer data in case of a manufacturer recall of merchandise. Above all, the company has been able to leverage and share enterprise customer data to the benefit of the entire company.

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