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i a i
PART IV K M System Tools and Portals
BOX 12.2
Knowledge management and data mining devel-
oped independently of each other, and their rela-
tionship has not yet been fully recognized, much
less exploited. Born of the understanding that
knowledge is one if not the most im portant
asset of an organization, both knowledge manage-
ment (KM) and data mining (DM) grew and
flourished at the convergence of information tech-
nologies (machine learning, knowledge-based sys-
tems, databases), statistics and data analysis, and
the business and management sciences.
KM and DM embody distinct if not opposite
perspectives on different knowledge-related issues.
One is knowledge capture in the broadest sense
of the term. The KM community has traditionally
focused on knowledge acquisition from humans,
either directly (through manual ontology con-
struction, expert interviews, and authoring tools) or
indirectly, as when human know-how is mimicked
y a program that observes a human expert in
action (such as learning apprentices or program-
ming by demonstration).
In data mining, databases and data ware-
houses are the ultimate sources from which knowl-
edge is extracted. Machine learning emerged pre-
cisely as a way of alleviating difficulties raised by
knowledge elicitation from humans. In addition,
the exponential growth of process-generated data
has spurred the development for more scalable
hence, more thoroughly automatedways of gen-
erating useful knowledge from data.
A second issue is knowledge refinement and
revision. The typical KM approach is again manual:
The knowledge engineer readjusts domain ontolo-
gies, rewrites rules in collaboration with domain
experts, and so forth. Although DM research has
yielded a promising harvest of automated tech-
niques for knowledge revision and theory refine-
ment, these have been demonstrated on highly cir-
cumscribed domain theories and have yet to be
validated on medium and large-scale applications.
What is needed, then, is an integrative approach
that exploits synergies between knowledge man-
agement and data mining in order to monitor and
manage the full life cycle of knowledgeits capture
and discovery, representation, storage, retrieval,
revision or refinement, and reusein an organiza-
tion or community of practice.
SOURCE: First Conference on Integrating Data Mining and Knowledge Management, Geneva 2002.
Business intelligence (BI) is a global term for all processes, techniques, and tools
that support business decision making based on information technology. The
approaches can range from a simple spreadsheet to an advanced decision support sys-
tem. Data mining is a component of BI. Figure 12.2 shows the positioning of different
BI technologies used at different levels of m anagement and for different purposes,
including tactical, operational, and strategic decisions.
» Business Drivers
There are many reasons why data mining has grown substantially in the last few years.
Competition. Successfully competing in todays economy requires an understand-
ing of customer needs and behavior, and a great flexibility to respond to market
demands and competitors challenges. To achieve this, data must be transformed

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