By knowing the critical success factors of a data warehouse or data mining project, the project manager is able to focus on what absolutely has to be in place for the project to be successful. Data warehouse experts Sid Adelman and Larissa Moss walk you through the steps that will help your project succeed:
If a factor or characteristic is critical to the success of a project, it is called a "critical success factor" (CSF). The absence of that factor or characteristic dooms the project. The following seven CSFs, therefore, are mandatory for a successful data mining or data warehouse project:
1. Expectations are communicated to the users
IT is often unwilling or afraid to tell the users what they will be getting and when. Users should be told about the following:
2. User involvement is ensured
There are three levels of user involvement, as follows:
The last level is by far the most successful approach, while the first almost always results in failure.
3. The project has a good sponsor
The best sponsor is from the business side, not from IT. Most importantly, the sponsor should be in serious need of the data warehouse's capabilities to solve a specific problem or gain some advantage for his or her department.
4. The team has the right skill set
Without the right skills dedicated to the team, the project will fail. The emphasis is on "dedicated to the team."
5. The schedule is realistic
The most common cause of failure is an unrealistic schedule, usually imposed without the
To continue reading for free, register below or login
To read more you must become a member of SearchSQLServer.com
input or the concurrence of the project manager or team members. Most often, the imposed schedules have no rationale for specific dates, but are only means to "hold the project manager to a schedule." A realistic schedule will include all the required tasks to implement the project along with their durations, assigned resources and task dependencies.
6. The right tools have been chosen
The first decisions to be made are the categories of tools: Extract/Transform/Load, data cleansing, OLAP, ROLAP, data modeling, administration, and so on. The tools must match the requirements of the organization, the users, and the project. The tools should work together without the need to build interfaces or write special code.
7. Users are properly trained
In spite of what the vendors tell you, users must be trained and the training should be geared to the level of user and the way they plan to use the data warehouse. All users must learn about the data, and power users should have additional in-depth training on the data structures.
Sid Adelman is president of Sid Adelman & Associates, a consulting firm specializing in data warehouse and strategic data architecture. He co-authored a methodology and project-planning product (PlanXpert for Data Warehouse) tailored specifically for data warehouse. Sid is a regular speaker at data warehouse and industry conferences. With Larissa Moss, he co-authored the book "Data Warehouse Project Management". Sid is a founding member of the BIAlliance. He can be reached at sidadelman@aol.com. This tip provided courtesy of Informit.com.
