CHAPTER 4 PLANNING AND PROJECT MANAGEMENT
CHAPTER OBJECTIVES
CHAPTER OBJECTIVES
- Review the essentials of planning for a data warehouse
- Distinguish between data warehouse projects and OLP system projects
- Learn how to adapt the life cycle approach for a data warehouse project
- Discuss project team organization, roles, and responsibilities
- Consider the warning signs and success factors
As soon as you read the title of this chapter, you 'night hasten to conclude that this is a chapter intended for the project manager or the project coordinator. If you are not already a project manager or planning to be one in the near future, you might be inclined to just skim through the chapter. That would be a mistake. This chapter is very much designed for all IT professionals irrespective of their roles in data warehousing projects. It will show you how best you can fit into your specific role in a project. If you want to be part of a team that is passionate about building a successful data warehouse, you need the details presented in this chapter. So please read on. First read the following confession.
Although this conversation is a bit exaggerated, according to industry experts, more than 50°A, or data warehouse projects are considered failures. In many cases, the project is not completed and the system is not delivered. In a few cases, the project somehow gets completed but the data warehouse turns out to be a data basement. The project is improperly sized and architected. The data warehouse is not aligned with the business. Projects get abandoned in midstream.
Several factors contribute to the 1-ailures. When your company gets into data warehousing for the first time, the project will involve many organizational changes. At the present tune, the emphasis is on enterprise-wide information analysis. Until now, each department and each user "owned- their data and \yen: concerned with a set of their "own" computer systems. Data warehousing will change all of that and make managers, data owners, and end-users uneasy. You are likely to uncover problems with the production systems as you build the data warehouse.
Although this conversation is a bit exaggerated, according to industry experts, more than 50°A, or data warehouse projects are considered failures. In many cases, the project is not completed and the system is not delivered. In a few cases, the project somehow gets completed but the data warehouse turns out to be a data basement. The project is improperly sized and architected. The data warehouse is not aligned with the business. Projects get abandoned in midstream.
Several factors contribute to the 1-ailures. When your company gets into data warehousing for the first time, the project will involve many organizational changes. At the present tune, the emphasis is on enterprise-wide information analysis. Until now, each department and each user "owned- their data and \yen: concerned with a set of their "own" computer systems. Data warehousing will change all of that and make managers, data owners, and end-users uneasy. You are likely to uncover problems with the production systems as you build the data warehouse.
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