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.


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

Home Depot launched a data warehousing project in 2002. The company had no data warehouse prior to this project. Before this project, analysts seeking big-picture information about Home Depot’s 1500 stores had to access information from as many as 16 separate mainframe systems. One could get the information if one really wanted to, but it took too long to provide effective support for decision-making (Whiting, 2002).

Home Depot was actually behind competitors like Lowe’s in adopting data warehouse technology, but, to make lemons out of lemonade, there may have been a cost advantage to being last mover instead of first mover. Instead of having to go through conversion of even embryonic prior data warehousing efforts, Home Depot could charge straight into building a new system with the latest technology (Schwartz, 2002). Bob DeRodes, the chief information officer who led Home Depot’s IT overhaul at the time, also wisely followed the phased implementation strategy that our textbook and others (such as Schwartz, 2002) say is essential to successful data warehouse development and deployment. Home Depot launched its data warehouse with applications dedicated to “analyzing human-resource expenses” (Whiting, 2002), a key business goal for the new CEO Bob Nardelli. The intent was to add incrementally to the system, with point-of-sale data to be added the following year.

Home Depot’s data warehousing project was part of a company-wide IT overhaul that cost $2 billion and required one year and one million person-hours to complete (Webster, 2006). Measuring the success of the project requires more than a look at the bottom line—after all, even the best data warehouse could not insulate Home Depot a 66% drop in 2008 Q1 profit due to the recession-launching collapse of the housing market (Clifford, 2008). The data warehouse implementation and the entire IT overhaul were part of a larger plan to remake the culture of the organization. When Bob Nardelli took over as CEO at the turn of the millennium, he found a corporation that was surprisingly decentralized. Store managers had vast autonomy and regularly rejected directives from corporate (Charan, 2006). Imagine the challenge of telling such staff to standardize their data for a new data warehouse. But that was part of the challenge Nardelli tackled, as he recognized that the lack of standardization, centralization, and discipline throughout the company would prevent the company from sustaining the growth it had enjoyed in the 1990s, especially now that competitors like Lowe’s were crowding their market.

One cultural change directly supported by the data warehouse was to shift the basis for decision-making from intuition and anecdote to data. Anecdotes and gut feelings, coupled with isolated, nonstandard bits of data, don’t travel well across over a thousand separate retail operations. The data warehouse provided the transparent data necessary for shared decision-making. It also provided the quantitative reports necessary to produce data- based templates for business review meetings. Previously managers would defend their positions with sketchy yet hard-to-challenge anecdotes from their own stores; the rich data from the data warehouse made such anecdotal escapes much more difficult and forced managers to acknowledge and act to correct failings in their operations (Charan, 2006). This change to data-based decision-making did not please everyone, and some employees did leave the company, but this cultural change, supported by the data warehouse, did take hold, as evidenced in part by improved employee satisfaction scores on in-house surveys (Charan, 2006).


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

XOJet is a young fractional aircraft company. Its primary customers are businesses that seek the convenience of private jet travel but lack the resources to purchase and maintain their own aircraft. Customers can charter flights on XOJet’s fleet of Cessna Citation Xs and Bombardier Challenger 300s or buy partial ownership in planes (“fractional” aircraft). With over 15,000 charter routes and a clientele expecting on-demand service, XOJet faces the logistical challenge of ensuring that it has just enough planes in just enough places at the right times to meet customer needs.

XOJet this adopted a data warehouse in 2006 to allow better analysis of flight patterns. XOJet used its data warehouse to develop algorithms and reports to optimize use of its fleet (Tucci 2007). This system allows XOJet to schedule maintenance around peak flight times and route its planes to arrive earlier and avoid traffic (Tucci 2007).

The data warehouse has provided concrete benefits for XOJet. Other airlines in this part of the industry commonly experience utilization rates of 75% or less. They often must “deadhead”—i.e., fly an empty plane to a different airport to pick up a paying customer. XOJet has been able to use the knowledge discovered in its data warehouse to increase its flight utilization rate to 95–97% (Watson, 2008). A 20% increase in utilization—in one year, XOJet increased its flight hours from 1000 to 1200—can lead to a doubling of profit (Tucci 2007). When XOJet can avoid deadheading and schedule more usable flight hours, it can charge less for hourly fees—27% less than competitors (Watson, 2008). While the data warehouse can’t be credited with all of XOJet’s recent growth and success, its strong competitive position helped it win $2.5 billion in financing to expand its fleet for international operations in 2008 (Lattman, 2008), a year when credit was drying up for nearly everyone else.