Data warehouse systems have gained popularity as companies from the most varied
industries realize how useful these systems can be. A large number of these
organizations, however, lack the experience and skills required to meet the
challenges involved in data warehousing projects. In particular, a lack of a methodological
approach prevents data warehousing projects from being carried out successfully.
Generally, methodological approaches are created by closely studying similar
experiences and minimizing the risks for failure by basing new approaches on a
constructive analysis of the mistakes made previously.
This
chapter considers a few methodological approaches mentioned in the literature
that describe how best to manage data warehouse lifecycles. This chapter also defines our methodological approach
to a data mart project.
Top-Down vs. Bottom-Up
When you consider
methodological approaches, their top-down structures or bottom-up structures
play a basic role in creating a data warehouse. Both structures deeply affect the datawarehouse lifecycle.
If you use a top-down approach, you will have to
analyze global business needs, plan how to develop a data warehouse, design it, and implement it as a whole. This
procedure is promising: it will achieve excellent results because it is based
on a global picture of the goal to achieve, and in principle it ensures
consistent, well integrated data warehouses. However, a long story of failure with top-down
approaches teaches that:
- high-cost estimates with
long-term implementations discourage company managers from embarking on
these kind of projects;
- analyzing and bringing together all relevant
sources is a very difficult task, also because it is not very likely that
they are all available and stable at the same time;
- it
is extremely difficult to forecast the specific needs of every department
involved in a project, which can result in the analysis process coming to
a standstill;
- since no prototype is going to
be delivered in the short term, users cannot check for this project to be
useful, so they lose trust and interest in it.
In a bottom-up approach, data warehouses are incrementally built and several data marts are iteratively created. Each data mart is based on a set of facts that are
linked to a specific company department and that can be interesting for a user
subgroup (for example, data marts for
inventories, marketing, and so on). If this approach is coupled with quick
prototyping, the time and cost needed for implementation can be reduced so
remarkably that company managers will notice how useful the project being
carried out is. In this way, that project will still be of great interest.
The bottom-up approach turns out to be more
cautious than the top-down one and it is almost universally accepted. Naturally
the bottom-up approach is not risk-free, because it gets a partial picture of the
whole field of application. We need to pay attention to the first data mart to be used as prototype to get the
best results: this should play a very strategic role in a company. In fact, its
role is so crucial that this data mart should be a reference point for the whole data warehouse. In this way, the following data marts can be easily added to the original one. Moreover, it
is highly advisable that the selected data mart exploit consistent data already made available.
