For most business users, the data warehouse's front room with its business intelligence (BI) reports and analytic applications is the only visible layer of the data warehouse. We need to ensure it effectively answers the users' questions if we stand any chance of real business acceptance and return on the DW/BI investment.
This chapter begins with an introduction to the thought process and approach used by many analysts as they embark on a new analysis of business performance. They start with a report of historical metrics, but that's just the beginning of the typical analysis lifecycle, not the end state goal. Hopefully, deeper understanding of this process and associated activities will encourage DW/BI teams to go beyond merely publishing reports. We then make a case for behavior analysis to highlight a low hanging analytic opportunity.
From there, we turn our attention to the more mundane tasks of producing the starter set of standard BI reports, along with a BI portal. We also warn you about the dangers of committing to a dashboard too early. Next we shift gears to focus on data mining, from what it is, to how it might impact the data warehouse, and finally, how to jump on the bandwagon. The final section in this chapter deals with SQL; although it is the fundamental language used by application developers and query tools to access the data warehouse, it has serious limitations for conducting analysis.