Data Warehousing Failures

Topics: Data management, Data mining, Data warehouse Pages: 16 (5385 words) Published: July 10, 2013
Data Warehousing Failures

Eight studies of data warehousing failures are presented. They were written based on interviews with people who were associated with the projects. The extent of the failure varies with the organization, but in all cases, the project was at least a disappointment.

Read the cases and prepare a one or two page discussion of the following:

1. What’s the scope of what can be considered a data warehousing failure? Discuss. 2. What generalizations apply across the cases?
3. What do you find most interesting in the failure stories? 4. Do they provide any insights about how a failure might be avoided?

Case Studies of Data Warehousing Failures

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 were due to be added once the initial subject area project was completed. Auto Guys expects to grow the warehouse to two terebytes 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.

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...
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