Case 1 – Auto Guys
Auto Guys initiated a data warehousing project four years ago but it never achieved full usage. After initial support for the project eroded, management revisited their motives for the warehouse and decided to restart the project with a few changes. One reason for the restructuring, according to the project manager, was the complexity of the model initially employed by Auto Guys.
At first, the planner for the data warehouse wanted to use a dimensional model for tabular information. But political pressure forced the system’s early use. Consequently, mainframe data was largely replicated and these tables did not work well with the managed query environment tools that were acquired. The number of tables and joins, and subsequent catalog growth, prevented Auto Guys from using data as it was intended in a concise and coherent business format.
The project manager also indicated that the larger the data warehouse, the greater the need for high level management support – something Auto Guys lacked on their first attempt at setting up the warehouse. Another problem mentioned by the project manager was that the technology Auto Guys chose for the project was relatively new at the time, so it was not accepted and did not garner the confidence that a project using proven technology would have received. This is a risk inherent in any “cutting edge” technology adoption.
The initial abandonment of the project was undoubtedly hastened by both corporate discomfort with this new technology and the lack of top management support.
A short time after dropping the project, top management felt pressure to reestablish it. Because Auto Guys initially planned an enterprise-wide warehouse, they had considerable computer capacity. It was put to use on a much smaller project that focused exclusively on a single subject area. Other subject areas we due to be added once the initial subject area project was completed. Auto Guys expects to grow the warehouse to two terabytes within a year or two and eventually expand to their projected enterprise-wide data warehouse.
The biggest difference between pre- and post-resurrection will be that the project will evolve incrementally.
Given his experience with the warehouse, the project manager made the following summary observations: (1) the management of expectations is critical to any sizeable data warehousing project; (2) proven technology, although not essential, does make the project easier to explain and justify; and (3) the construction of a sizeable data warehouse should be treated more like and R&D effort instead of a typical IT project because of the time it takes to complete the project, the amount of money involved, and the short-term focus of top management.
Case 2 – Government Research Laboratory
The Government Research Laboratory (GRL) has a finance department in each of the fifteen nearly identical laboratories that report to its national home office. As a member of the finance team, Bob was familiar with the monthly financial reports required by the home office. Although the financial reports themselves were not complicated, access to the mainframe where the data was housed was necessary, and an understanding of COBOL was needed to generate any report that differed from the standard. Once a month, reports would be distributed in paper form and each member of the finance team would sort through them and file them away. If the reports required any alteration, then someone from IS, or one of two people from finance familiar with COBOL, was contacted.
Because of these reporting difficulties, an IS manager made the suggestion that the company’s first data warehouse be constructed, and that the finance department be the primary beneficiary. Two people from IS began to work full-time on the project and a financial analyst also joined the group. The IS manager then offered a bonus to the IS technicians if they could get the data warehouse up and running by the end of the fiscal year which was just four month away.
Both the IS and the finance members of the team, firmly rooted in reality, knew this would be a difficult if not impossible task. But they resolved to give it their best shot and attempted a full transfer of all available reports to the warehouse. When it became clear that this was too ambitious, they cut out all of the detailed reports and focused on just the summaries, assuming the more detailed material could be integrated at some point after the initial deadline.
The team was successful and had all summary reports transferred to the data warehouse at the end of the fiscal year. The fact that the necessary tables were up and functional, however, was not an indicator of future success.
The first problem involved changes to the mainframe database which were initiated at the same time, but uncoordinated with, the data warehousing project. At the same time the foundation for the data warehouse was being laid, the planning system on the mainframe was undergoing modifications not captured in the data warehouse. In particular, changes in cost accounting standards within the organization changed the number of key summary categories from the standard five used in the past to seven, rendering the traditional five next to useless.
The second problem occurred when the goal to establish the data warehouse became the end goal. As the GRL financial analyst for the team describes it, the feedback and modification period he had anticipated after September never came. The preliminary fix became the permanent solution. The analyst later learned that IS had always intended to set the system up but only funded its basic maintenance. Modifications were not in the budget and the finance department, only minimally included in the warehouse project, never had a budget that would fund the inclusion of more data and alterations to the system.
Essentially, GRL found itself with a data warehouse that contained too little data and data that was outdated because of format changes in GRL’s cost accounting standards. Also, neither finance nor IS budgeted for changes necessary to create a fully functional data warehouse. Those two problems alone would have killed most data warehouse initiatives, but the problems did not end there.
The data warehouse was supposed to solve two accessibility problems. One involved the need for COBOL language expertise whenever a report required alteration, and the other involved the sheer mass of printed documents being disseminated and archived. Instead of providing a solution, reports theoretically available on a network were handled in much the same manner as the old reports. For one thing, the data access software installed on each user’s PC was frequently incompatible with the mix of software already there. Many end-users, therefore, found access to the data warehouse difficult, and those who were able to access the data warehouse had such bad experiences with the new system they just did not use it. Also, the small minority that did not experience accessibility problems simply printed hard copies of the reports, which was no great change from how things had been done in the past. Additionally, the programming barriers in existence when COBOL knowledge was necessary simply changed form. PowerBuilder, very much a programmer’s tool, was selected to build the user interfaces. Ironically, IS only had one individual with PowerBuilder skills, thus creating more of a bottleneck than had existed with COBOL.
The situation remained the same, if not worse, for three years following the first warehousing initiative. Finally, another IS project manager became interested in the idea of breathing life into the old warehouse. He was motivated by the organization’s solution to the Y2K problem, which involved abandoning the old mainframe system and transferring the old reports to the warehouse. Fortunately, his interest was accompanied by funding that allowed the enhancements anticipated at the very beginning of the first project to finally be realized. Also, all users are able to access Web-based reports.