CHAPTER 4:

Planning and Project Management

The Project Team

A data warehouse project is similar to other software projects in that it is human-intensive. It takes several trained and specially skilled persons to form the project team.

Two things can break a project: complexity overload and responsibility ambiguity.

In a life cycle approach, the project team minimizes the complexity of the effort by sharing and performing.

When the right person on the team with the right type of skills and with the right level of experience does an individual task, this person is really resolving the complexity issue.

In a properly constituted project team, each person is given specific responsibilities of a particular role based on his or her skill and experience level. In such a team, there is no confusion or ambiguity about responsibilities.

1- Organizing the Project Team

Organizing a project team involves putting the right person in the right job.You would need specialized skills in the areas of project management, requirements analysis, application design, database design, and application testing. But a data warehouse project calls for many other roles. How then do you fill all these varied roles?

1. A good starting point is to list all the project challenges and specialized skills needed. Your list may run like this: planning, defining data requirements, defining types of queries, data modeling, tools selection, physical database design, source data extraction, data validation and quality control, setting up the metadata framework, and so on.

2. Once you have a list of roles, you are ready to assign individual persons to the team roles.

3. In this personnel allocation process, remember that the user representatives must also be considered as members of the project team.

4. Do not fail to recognize the users as part of the team and to assign them to suitable roles.

2- Roles and Responsibilities Data warehousing authors and practitioners tend to classify the roles or job titles in various ways. They first come up with broad classifications and then include individual job titles within these classifications.

Here are some of the classifications of the roles:

Classifications: Staffing for initial development, Staffing for testing, Staffing for ongoing maintenance, Staffing for data warehouse management

Broad classifications: IT and End-Users, then sub-classifications within each of the two broad classifications, followed by further sub-classifications

Classifications: Front Office roles, Back Office roles Classifications: Coaches, Regular lineup, Special teams Classifications: Management, Development, Support Classifications: Administration, Data Acquisition, Data Storage, Information Delivery

Despite the absence of a standard set of roles, we would suggest a basic set of team roles:

Executive Sponsor Project Manager User Liaison Manager Lead Architect Infrastructure Specialist Business Analyst Data Modeler Data Warehouse Administrator Data Transformation Specialist Quality Assurance Analyst Testing Coordinator End-User Applications Specialist Development Programmer Lead Trainer

Figure 4-7 lists the usual responsibilities attached to the suggested set of roles.

Figure 4-7 Data warehouse project team: roles and responsibilities.

3- Skills and Experience LevelsFigure 4-8 describes the skills and experience levels for our sample set of team roles.

Figure 4-8 Data warehouse project team: skills and experience levels.

4- User ParticipationIn 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 ad hoc programs that are not part of the regular application.

User interaction with a data warehouse is direct and intimate. 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 be performing.

Your data warehouse project will succeed only:1- If appropriate members of the user community are accepted as team members with specific roles. 2- Make use of their expertise and knowledge of the business.

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

Figure 4-9 Data warehouse development: user participation.

Here 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—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

Information 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 users in their respective departments

Project Management ConsiderationsEffective project management is critical to the success of a data warehouse project.

Figure 4-10 Possible scenarios of failure.

Warning Signs

Figure 4-11 Data warehouse project: warning signs.

There are some such indications of success that can be observed within a short time after implementation. The following happenings generally indicate success:

Queries and reports—rapid increase in the number of queries and reports requested by the users directly from the data warehouse

Query types—queries becoming more sophisticated Active users—steady increase in the number of users Usage—users spending more and more time in the data warehouse looking for

solutions Turnaround times—marked decrease in the times required for obtaining strategic

information

Success Factors