We had an assignment in INFS 762, Data Warehousing + Data Mining, to write three quick briefs on industry data warehousing projects. Here’s the third from my paper:

International Truck and Engine was bogged down in its own financial data. Monthly finances were taking two weeks to process. The company had implemented a data warehouse in 1996, but it wasn’t providing the business performance metrics executives and analysts needed to guide their decision-making (Whiting, 2003).

Therefore, in 2001, International Truck and Engine overhauled its data warehouse and developed its Key Business Indicators portal. The new system provided a “10–12% efficiency gain in the monthly close process” (Whiting, 2003). The system also gave International the ability to review historical trends, forecast demand, and give its suppliers more lead-time on production orders (D’Antoni, 2005). Executives and analysts could access business performance metrics that previously could only be found in hefty three-ring binders of monthly and quarterly reports (Eckerson, 2004). The project was sufficiently successful to win The Data Warehousing Institute’s 2003 “Business Performance Management” Best Practices Award (Edwards, 2003).

International’s data warehousing overhaul also followed the vital path of phased implementation. The data flowing into the warehouse came from 32 source systems. The developers chose to implement the warehouse by source rather than work group: “This way, the team delivered enterprisewide KBIs [key business indicators] while maintaining project delivery in bitesize chunks” (Eckerson, 2004). In other words, developers were able to keep steps small while regularly and from the beginning delivering tools that would prove useful to workers across the organization.