Use the attached file.
- What is the scope of what can be considered a data warehousing failure?
- What generalizations apply across the cases?
- What do you find most interesting in the failure stories?
- Do they provide any insights about how a failure might be avoided?
1. The scope of what can be considered a data warehousing failure:
1). Exceed deadline
2). Exceed the budget
3). Lack of the understanding of the importance of the project and thus abandon it in the middle
4). Failed to reach expectation due to the unrealistic high expectation.
5). Failed to be in syn with the change in other applications that employ the data warehousing project, i.e., is out of syn and is
not coordinated well with the change of other parts of the whole application picture.
6). Failed to reflect the user's change of requirements and standards that is related to the requirements of the project.
7). Failed to provide sufficient maintenance support.
8). Failed to involve end-users in making the requirements. The requirements only comes from high level management.
9). Failed to choose appropriate data warehousing tools.
10). Failed to identify the scope of the project and failed to set up appropriate milestones.
11). Failed to provide appropriate software, such as transactional data marketed as data warehouse.
12). Too ambitious project that involves huge finance commitment and long development time and is not realistic to finish.
13). Failed to prove itself to the management for continuing funding support.
14). Cancelled ...
Data Warehousing is traced.