Download Business Properties and Information Technology

Chapter 5
Business
Intelligence
and
Knowledge
Management
Systems
PowerPoint Presentation
by Charlie Cook
Copyright © 2004 South-Western.
All rights reserved.
Learning Objectives
• To recognize how information is used for different types
of decisions at various levels in the organization
• To become familiar with the support that management
receives from decision aids such as Business
Intelligence systems, OLAP, groupware, expert systems,
and intelligent agents
• To understand the importance and challenges of formally
managing organizational knowledge, and to recognize
the technologies that enable successful knowledge
management
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5–2
Management Decision Making
• Classic decision making model (Simon)
 Intelligence: searching the environment for conditions
calling for a decision.
 Design: inventing, developing, and analyzing possible
courses of action.
 Choice: selecting a course of action.
• Types of decisions
 Structured decisions
 Unstructured decisions
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5–3
Steps in
Decision Making
Note: To simplify the figure, we have
omitted feedback loops to each of the
circles. Such feedback, of course, is an
integral part of decision making and
should improve the intelligence, design,
and choice that occur as part of an
iterative process.
FIGURE 5.1
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5–4
Nature of Information Required
• Strategic managers
 Assess environment; project
future events and conditions.
• Tactical managers
 Focused on relevant
operational units.
• Operational managers
 Narrower in scope, more
detailed, more accurate,
from within.
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5–5
Management Problem Structures and
Information Requirements
FIGURE 5.2
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5–6
Roadblocks for
Quality Decisions
FIGURE 5.3
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5–7
Systems for Aiding Decision Makers
• Business Intelligence systems
 Online Analytical Processing (OLAP) systems
• Group support systems also groupware
 collaboration, virtual meetings, and group scheduling
• Artificial intelligence (AI)
 Expert systems
 Neural networks
 Intelligent agents
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5–8
Business Intelligence Systems
TI 5.2
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5–9
Converting Tabular Data to Graphical Representation
FIGURE 5.4
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5–10
Expert Systems
• Human expert
 Possesses relevant
knowledge.
• Knowledge engineer
 Has the skill to extract
knowledge and encode
in knowledge base.
• Knowledge base
 Contains relevant
expertise, in the form
of rules.
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• Inference engine
 Executes line of
reasoning based on
facts and rules.
• User interface
 Provides for user input
to system and displays
output.
 Contains explanation
facility to let user ask
why and how.
5–11
Expert Systems
Conditions favor the
use of expert systems
when:




Decisions are
extremely complex.
Consistency of
decision making is
desirable.
The decision must
optimized (minimized
for time and
maximized for quality).
An expert decision
maker can be
computer-modeled.
TI 5.3
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5–12
Neural Networks (NN)
• Computer-based systems of hardware and
software.
 Mimic the human brain’s ability to recognize patterns,
predict outcomes with incomplete information.
 Derive knowledge from data; must be “trained”.
• Four types of neural network output:
 Prediction
 Classification
 Data Filtering
 Optimization
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5–13
Knowledge Management
• The process of capturing, storing, retrieving, and
distributing the knowledge of the individuals in
an organization for use by others to improve the
quality and/or efficiency of decision making.
• Factors impeding knowledge management
 Reluctance to share knowledge or expertise for fear
of losing power.
 Failure to record knowledge of problems solved in the
past.
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5–14
Knowledge
Management
System
Groupware uses of
knowledge management:






Purchasing
Inventory control
Distribution
Exchanges and auctions
Channel and partner
relationship management
Customer care and
support
FIGURE 5.5
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5–15