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Management Planning and Project

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CHAPTER OBJECTIVES Review the essentials of planning for a data warehouse. Distinguish between data warehouse projects and OLTP 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.

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planning Your Data Warehouse First, determine if your company really needs a data warehouse. Is it really ready for one? You need to develop criteria for assessing the value expected from your data warehouse. Your company has to decide on the type of data warehouse to be built and where to keep it. You have to find out where the data is going to come from and even whether you have all the needed data. You have to establish who will be using the data warehouse, how they will use it, and at what times.

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Business Requirements, Not Technology Data warehousing is not about technology, it is about solving users need for strategic information. Do not plan to build the data warehouse before understanding the requirements. Start by focusing on what information is needed and not on how to provide the information. The basic structure and the architecture to support the user requirements are more important a preliminary survey of requirements. The outcome of this preliminary survey: A. Will help you formulate the overall plan. B. Will be help you to set the scope of the project. C. Will assist you in prioritizing and determining the rollout plan for individual data marts.

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What types of information must you gather in the preliminary survey? At a minimum, obtain general information on the following from each group of users: Mission and functions of each user group Computer systems used by the group Key performance indicators Factors affecting success of the user group Who the customers are and how they are classified Types of data tracked for the customers, individually and groups Products manufactured or sold Categorization of products and services Locations where business is conducted Levels at which profits are measuredper customer, per product, per district Levels of cost details and income Current queries and reports for strategic information Business Requirements

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The Data Warehouse Project Data warehouse projects are different from projects building the transaction processing systems. If you are new to data warehousing, your first data warehouse project will reveal the major differences.

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How is it Different? Figure 4-2 lists the differences between Data Warehouse Project and OLTP System Project Data Warehouse: Distinctive Features ad Challenges for Project Management Figure 4-2 How a data warehouse project is different.

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The Life-Cycle Approach As an IT professional you are all too familiar with the traditional system development life cycle (SDLC). You know how to begin with a project plan, move into the requirements analysis phase, then into the design, construction, and testing phases, and finally into the implementation phase. The life cycle approach accomplishes all the major objectives in the system development process. The life cycle methodology breaks down the project complexity and removes any ambiguity with regard to the responsibilities of project team members. A data warehouse project is complex in terms of tasks, technologies, and team member roles. But a one-size fits- all life cycle approach will not work for a data warehouse project. Adapt the life cycle approach to the special needs of your data warehouse project. Remember that the broad functional components of a data warehouse are data acquisition, data storage, and information delivery. Make sure the phases of your development life cycle wrap around these functional components. Figure 4-3 shows how to relate the functional components to SDLC.

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Figure 4-3 DW functional components and SDLC.

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Figure 4-4 Data warehouse project plan: sample outline.

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The Development Phases In the previous section, we again referred to the overall functional components of a data warehouse as data acquisition, data storage, and information delivery. Therefore, when we formulate the development phases in the life cycle, we have to ensure that the phases include tasks relating to the three components. The phases must also include tasks to define the architecture as composed of the three components and to establish the underlying infrastructure to support the architecture. The design and construction phase for these three components may run somewhat in parallel. Refer to Figure 4-5 and notice the three tracks of the development phases. In the development of every data warehouse, these tracks are present with varying sets of tasks. You may change and adapt the tasks to suit your specific requirements. You may want to emphasize one track more than the others. If data quality is a problem in your company, you need to pay special attention to the related phase. The figure shows the broad division of the project life cycle into the traditional phases:

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Figure 4-5 Data warehouse development phases.

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Organizing 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. 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? 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. Once you have a list of roles, you are ready to assign individual persons to the team roles. Do not fail to recognize the users as part of the team and to assign them to suitable roles.

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Figure 4-7 lists the usual responsibilities attached to the suggested set of roles. Figure 4-7 Data warehouse project team: roles and responsibilities.

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Skills and Experience Levels Figure 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.

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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 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.

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Figure 4-9 Data warehouse development: user participation.

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Here is a list of a few team roles that users can assume to participate in the development: Project Sponsorexecutive responsible for supporting the project effort all the way User Department Liaison Representativeshelp IT to coordinate meetings and review sessions; ensure active participation by the user departments Subject Area Expertsprovide guidance in the requirements of the users in specific subject areas; clarify semantic meanings of business terms used in the enterprise Data Review Specialistsreview the data models prepared by IT; confirm the data elements and data relationships Information Delivery Consultantsexamine and test information delivery tools; assist in the tool selection User Support Techniciansact as the first-level, front-line support for the users in their respective departments

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Project Management Considerations Effective project management is critical to the success of a data warehouse project. Figure 4-10 Possible scenarios of failure.

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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 reportsrapid increase in the number of queries and reports requested by the users directly from the data warehouse Query typesqueries becoming more sophisticated Active userssteady increase in the number of users Usageusers spending more and more time in the data warehouse looking for solutions Turnaround timesmarked decrease in the times required for obtaining strategic information Success Factors

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CHAPTER SUMMARY While planning for your data warehouse, key issues to be considered include: setting proper expectations, assessing risks, deciding between top-down or bottom-up approaches, choosing from vendor solutions. Business requirements, not technology, must drive your project. A data warehouse project without the full support of the top management and without a strong and enthusiastic executive sponsor is doomed to failure from day one. Benefits from a data warehouse accrue only after the users put it to full use. Justification through stiff ROI calculations is not always easy. Some data warehouses are justified and the projects started by just reviewing the potential benefits. A data warehouse project is much different from a typical OLTP system project. The traditional life cycle approach of application development must be changed and adapted for the data warehouse project. Standards for organization and assignment of team roles are still in the experimental stage in many projects. Modify the roles to match what is important for your project. Participation of the users is mandatory for success of the data warehouse project. Users can participate in a variety of ways. Consider the warning signs and success factors; in the final analysis, adopt a practical approach to build a successful data warehouse.

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