Data Warehousing is concerned with building a data store to facilitate efficient operational reporting and analytics. This data store exists independently of any transactional systems (financial, sales, material management, logistics, etc.). This data store also facilitates the consolidation of data from multiple disparate transactional systems.

Let’s give a real world example:

The Acme Corporation is a corporation on the acquisition trail. They’ve purchased 3 companies over the last 18 months. Each company runs a different ERP system (SAP, Oracle, and JD Edwards). The corporate controller has requested a consolidated accounts receivable aging that includes the core company plus the 3 newly acquired companies.

Step 1: Analyze the data required to accomplish the consolidated aging. Build the necessary data stores in the data warehouse.

Step 2: Design, build, test, and implement the methods necessary to acquire the data from the existing 4 systems and populate the new data warehouse with it.

Step 3: Design, build, test, and implement the consolidated accounts receivable aging operational reports and analytics.

Step 4: Deploy

Data warehousing works hand-in-hand with Business Intelligence. Data warehousing serves up the data and business intelligence transforms the data into meaningful information via reporting and analytics.