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of mining models with information about mining results. All these approaches
and standards do not take the semantics of the data into account.
In [21] a very good approach of the advantages and disadvantages of tradi-
tional methodologies of software development when applied to Business Intel-
ligence solutions is found. Here the authors state how old practices are good
when every system had a beginning and an end and every system was designed
to solve only one isolated problem for one set of business people from one line
of business. However, this practices fail when integration of different depart-
ments is needed, because they do not include any cross-organizational activ-
ities necessary to sustain an enterprise-wide decision support environment.
For nonintegrated system development, conventional waterfall methodology
is su
cient. However, these traditional methodologies do not cover strategic
planning cross-organizational business analysis. Software methodologies such
as an iterative model had to be improve to deal with risks.
In RUP [17] an architecture centered model is presented in which an it-
erative and incremental way makes it possible to develop a software product
of any scale or size. Outputs of each iteration can be components, modules of
any software part that will be integrated into the next iteration in order to
fulfil the final product at the end. These features make it appropriate for Data
Mining projects in which requirements change as a consequence of already ob-
tained patterns and where the outputs (patterns) of each step integrate the
global solution.
3 Basis of a Data Mining Project Development
Methodology
The term business refer to any activity developed in a company in the most
general sense, no matter the nature and aim of such activity (commercial,
governmental, education, ...). Data mining is one of the technologies that
make Business Intelligence solutions [6] be implemented (“a fairly new term
that incorporates a broad variety of processes and technologies to harvest and
analyze specific information to help a business make sound decisions”). In
fact, any business intelligence solution should include a data mining project
to extract “the intelligence” of the business that will be accordingly deployed.
However, the truth is that data mining projects are being developed more
as an art than as an engineering process. It does not properly meet real busi-
ness needs when dealing with any kind of project. Companies really need to
manage projects in the most controlled way, always trying to reduce risks
without increasing costs. As there is no proper methodology to face data min-
ing projects, several different practices from different areas are applied. This
leads to failures when developing a project to getting poor results, or at least
not as good as they could be.
The need for a proper method to manage data mining projects is thus
clear. This method should allow managers to identify tasks and subtasks,
