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The K-enabled Approach to Corporate Decision Making – A Technology
Perspective
Sudhir Warier
FIETE, MIMA, MISTD, M.Phil, M.F.M, B.E
Abstract
The emergence of the modern day knowledge enabled global economies has brought about
radical changes in the way an organization “thinks”. It has fostered the development of creative
technologies, processes and procedures lending agility to an organization. To successfully
compete in this new age organizations have to rely on cutting edge technological developments
to harness its intangible resources and integrating them within the existing social, cultural and
traditional business frameworks.
Data Mining or Knowledge Discovery in Databases (KDD) refers to the nontrivial extraction of
implicit, previously unknown, and potentially useful information from data1. Data Mining
encompasses a number of different approaches including clustering, data summarization and
learning classification rules. It refers to the search for relationships and patterns that exist in
large organizational databases but are not readily apparent or useful for decision making.
Continual reassessment of established routines is essential to ensure that the decision-making
processes are in synchronization with changing organizational and business landscapes
1
William J Frawley, Gregory Piatetsky-Shapiro and Christopher J Matheus
Data mining software employs complex algorithms to sieve through huge volumes of data and
information for the purpose of detecting hidden patterns. The understanding these patterns
quickly leads to improved business intelligence.
The objective of this paper is to integrate technology with sound business practices to present a
K-enabled decision making framework for the modern day organizations.
Index Terms
Data Mining, Knowledge Discovery in Databases (KDD), KMS, Decision Trees, Neural
Networks