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.