Implementation Strategies

There are several types of BI decision-support implementation strategies with every conceivable combination of BI target databases (e.g., operational data store and enterprise data warehouse; Web warehouse and data marts; exploration warehouses and data mining databases; data marts and operational marts [oper marts]). By far the most popular implementation strategy is a data mart environment.

Regardless of which implementation strategy is selected, there is a right way and a wrong way to implement it. The wrong way is to build a collection of stand-alone BI target databases, each with its own independent ETL process. This approach will not produce an integrated and reconciled BI decision-support environment because creating separate ETL processes is no different than developing traditional stovepipe decision-support systems.

The right way to implement a chosen strategy is to build a BI decision-support environment in which all BI target databases are integrated and reconciled. When building this environment, it is critical to perform the common data transformations for all BI target databases only once and to reconcile these data transformations back to the operational source files and source databases. This will demonstrate the validity of the data in the various BI target databases. It is also important to reconcile all the data across the different BI target databases in order to demonstrate the data consistency among the various BI target databases. Both reconciliation processes are best accomplished with a coordinated ETL effort for all BI target databases, as illustrated in Figure 9.2.

Figure 9.2. Integrated BI Implementation Strategy

The most important ETL rule for an integrated BI implementation strategy is to share one coordinated ETL process. This is what differentiates BI from a traditional decision-support approach.