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Tuesday, August 20, 2013

Guiding Principles

Guiding Principles
Having worked on OLTP system projects, you are already aware of sonic of the guiding principles of project management—do not give into analysis paralysis, do not allow scope creep, monitor slippage, keep the project on track, and so on. Although most of those guiding principles also apply to data warehouse project management, we do not want to repeat them here. On the other hand, we want to consider sonic guiding principles that pertain to data warehouse projects exclusively. At every stage of the project, you have to keep the guiding principles as a backdrop so Cial these principles can condition each project management decision and action. The major guiding principles are: 

Sponsorship. No data warehouse project succeeds without strong and committed executive spon6orship.

Project Manager: It is a serious mistake to have a project manager who is more technology-oriented than user-oriented and business-oriented.

New Paradigm. Data warehousing is new for most companies; innovative project man-agement methods arc essential to deal with the unexpected challenges.

Team Roles. Team roles are not to be assigned arbitrarily; the roles must reflect the needs of each individual data warehouse project.

Data Quality. Three critical aspects of data in the data warehouse are: quality, quality, and quality.

User Requirements.. Although obvious, user requirements alone form the driving force of every task on the project schedule.

Building for Growth. Number of users and number of queries shoot up very quickly af-ter deployment; data warehouses not built for growth will crumble swiftly.

Project Polities, The first data Wa FCIIOUNC project in a company poses challenges and threats to users at different levels; trying to handle project politics is like walking the proverbial tightrope, to be trodden with extreme caution,

Realistic Expectations. It is easy to promise the world in the first data warehouse project; setting expectations at the right and attainable levels is the best course,

Dimensional data Marketing. A we dimensional data model is a required to and blueprint.

External Data. A data warehouse does not live by internal data alone; data from relevant external sources is an absolutely necessary ingredient. 

Training. Data warehouse user tools arc different, and new. If the us do not know how to use the tools. they will not use the data warehouse. An unused data ware-house is a failed data warehouse.


Your project team was organized, the development phases were completed, the testing w done, the data warehouse was deployed and the project was pronounced completed on time and within budget.

 Has the efforts been successful? In spite of the best intentions of the project team, it is likely that the deployed data warehouse turns out to be anything but a data warehouse. Figure 4-10 shows possible scenarios of failure. How will your data warehouse turn out in the cud?

Effective project management is critical to die success of a data warehouse project hi this section; we will consider project management issues as they especially apply to data warehouse projects. Review some basic project management principles, and list the possible success factors. We will review a real-life successful project and examine the reasons for its success. When all is said and done, you cannot always run your project totally by the book. Adopt a practical approach that produces results without getting bogged down in unnecessary drudgery.

User Participation

User Participation
In a typical OLTP application, the users interact with the system through GUI screens. They use the screens for data input and for retrieving information. 

The users receive any additional information through reports produced by the system at periodic intervals. If the users need special reports, they have to get IT involved to write an hoc programs that are not part or the regular application.

In striking contrast, user interaction with it data warehouse is direct and intimate. Usually, there are no just a few set reports or queries. When the implementation is complete, your users will begin to use the data warehouse directly with no mediation from IT. There is no predictability. In the types of queries they will be running the types of reports they will be requesting, or the types of analysis they will  performing. If there is one major difference between OUT systems and data warehousing systems, it is in the usage of the system by the users.

What is the implication of this major difference in project team composition and data warehouse development? The implication is extremely consequential. What does this mean? This means that lithe users will be using the data warehouse directly in unforeseen ways they must have a strong voice in its development. They must be part of the project team all the way. More than an OLTP system project a data warehouse project calls for serious joint application development (JAD) techniques.

Your data warehouse project will succeed only if appropriate members of the user community are accepted as the team members with specific roles. Make use of their experience and knowledge of the business. Tap into their experience in making business decision. Actively involve them in the selection of information delivery tools. Seek their help in testing the system before implementation. 

Figure 4-9 illustrates how and where in the development process 'Users must he made to participate. Review each development phase and clearly decide how and where your users nerd to participate. This figure relates user participation to stages in the development process.

Like is a list of a few team roles that users can assume to participate in the development:

  • Project Sponsor—executive responsible for supporting the project effort all the way
  • User Department Liaison Representatives-7 help IT to coordinate meetings and review sessions; ensure active participation by the user departments
  • Subject Area Experts—provide guidance in the requirements of the users in specific subject areas; clarify semantic meanings of business terms used in the enterprise
  • Data Review Specialists—review the data models prepared by IT; confirm the data elements and data relationships
  • In limitation Delivery Consultants --examine and test information delivery tools; assist in the tool selection
  • User Support Technicians--act as the first-level, front-line support for the ti in their respective departments

Monday, June 17, 2013

Microsoft 70-342 Exam

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