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CHAPTER 10
DATA, KNOWLEDGE,
AND DECISION SUPPORT
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Management and
Decision Making
Management
a process by which certain goals are achieved
through the use of resources
Two main phases for decision making:
Problem identification and possible solutions
formulation: information filtration, analysis,
and interpretation
Choice of appropriate solution
3
Reasons for IT support
The increasing number of alternatives
Time pressure
Decision complexity
The need to access remote information and
knowledge
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The Data Life
-
cycle Process
Business needs information and knowledge
Data collection
Data transformation:
Data storage and management
Data analysis and processing
Document management
Knowledge management
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The Data Life Cycle Process
Data Sources
(databases)
End Users:
Decision Making and other Tasks;
Data Visualization
Data Warehouse
(storage)
Analytical Processing,
Data Mining
Generate
Knowledge
Organizational
Knowledge Bases
Purchased
Knowledge
Storage
Direct
Use
Data
Organization;
Storage
Direct Use
Use
Use
Use
Use of
Knowledge
Storage
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Internal Data
-
generated within organization by
the corporate TPS, FIS, MIS
Personal Data
-
created by IS users or other
corporate employees documenting their own expertise
External Data
-
generated outside and organization
Methods for Collecting Raw Data
manually or by instruments and sensors
transferred electronically
Data Sources and Collection
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•
Accurate
•
Secure
•
Relevant
•
Timely
•
Complete
•
Consistent
Data Quality
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Exponential increases of data with time
Various sources of raw data
Only small portions are relevant
Increasing amount of external data
Different legal requirements relating to data
Selecting data management tools
-
a problem
Data security, quality, and integrity
Difficulties in data management
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Decision Making Process
Intelligence Phase
Design Phase
Choice Phases
REALITY
Implementation
of Solution
SUCCESS
FAILURE
Testing of Proposed
Solution
Verification
of the Model
Examination
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A model is a simplified representation of reality
Models classification:
Mental
model (or conceptual model) is verbal
description of reality
Iconic
(scale) model is a physical replica of a
system, usually based on a different scale form
original
Analog
model
-
a physical model, but the shape of
the model differs from that of the actual system
Mathematical
(quantitative) model describes a real
system based on mathematical formulas and
constructions
Models
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Mathematical Models
Model variables
–
investigated
characteristics of real world system
Parameters
–
represent internal and external
conditions
Managerial solutions are reflected in
model’s initial values and parameters
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Mathematical models (cont….)
Analytical models
Simulation models:
Advanced math. techniques
Computational methods
Computational algorithms
IT support
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Model Investigation
Model validation
Stability analysis
–
model reaction to small
disturbances in initial values
Sensitivity analysis
–
model reaction to
small disturbances on parameters values
Simulation experiments
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Simulation experiments
What
-
if analysis
–
checks the consequences
of possible solution
Goal
-
seeking analysis
–
attempts to find
inverse solution:
Not every model has inverse solutions
Computational algorithms based on series of
direct simulations must be used
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Structured decision making process
–
all four
stages are structured
Semistructured decision making process
–
not
all stages are structured
Unstructured decision making process
–
all four
stages are unstructured, required intuition and
knowledge
A Framework for Computerized
Decision Support
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Systematic process for solving problems
Define the problem
Classify the problem into a standard category
Construct a standard mathematical model
Find potential solutions
Choose and recommend a specific solution
Management Science
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support decision makers at all managerial levels
support several interdependent and/or sequential
decisions
support all phases of decision making and variety of
decision
-
making processes and styles
can be adapted over time to deal with changing
conditions
utilize models
integrate systems
execute analysis of models
DSS Characteristics
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Are implemented on the software level
Data Management
User Interface
Model Management
Knowledge Management
Users
Components of DSS
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Decision Support Systems
Individual DSSs:
Functional analysts
Low
-
level managers
Group Decision Support Systems (GDSS):
Groups of managers
Top
-
level managers
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Group Decision Support
Systems
Specially designed
User
-
friendly
Flexible
Support collaboration of geographically
dispersed users
Contain nominal group techniques:
Send feedback
Votes
Anonymous inputs
Keeping records
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Executive Information Support
Drill down
Critical success factors and key performance indicators
Status access
Access to the external information and knowledge
Trend analysis
Ad hoc analysis
Exception reporting
Integration with DSS
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Data Visualization Technologies
Present data in clear and understandable
form
Traditional forms:
digital images, graphs, charts, animation,
multimedia
Visual Interactive Decision Making
Visual interactive modeling
Geographical Information Systems
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Geographical Information
systems
DSSs supporting decision making process
using digital maps
Contain geographically referenced data
tying to objects on a map
Databases, spreadsheets, analytical tools
and user interface are main components
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Geographical Information
System (GIS)
GIS
Spatial Imaging
Function
Design and
Planning
Function
Database
Management
Function
Decision Modelling
Function
Surveying and Mapping
Design and
Engineering
Facilities
Management
Strategic
Planning and
Decision Making
Demographic
and Market
Analysis
Transportation and Logistics
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Emerging GIS Applications
help reengineer the aviation,
transportation, and shipping industries
enables vehicles or aircraft equipped with
a GPS receiver to pinpoint their location
as they move
include railroad car tracking and earth
-
moving equipment tracking
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Knowledge Management
Knowledge capturing, storing,
distribution require:
Knowledge identification
Knowledge discovery and analysis
Establishing organizational
Knowledge base
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Types of Organizational
Knowledge
Knowledge assets
-
regarding markets, products,
technologies, and organizations that a business owns
or needs to own
Best practices
-
collection of the most successful
solutions and/or case studies
Intellectual capital
-
collection of knowledge
amassed by an organization over the years
competitive intelligence
-
collection of
competitive information
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Identify valid, novel, potentially useful
data, and understand patterns in data
Supported by : massive data collection,
powerful multiprocessor computers, and
data mining and OLAP algorithms
Tools : data mining and online analytical
processing
Knowledge Discovery
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Analysis by end users from their desktop, online
Analyze the relationships between many types of
business elements
Involve aggregated and summarized data
Compare data over hierarchical time period
Present data in different perspectives
Work with queries
Online Analytical Processing
(OLAP)
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Data Mining searches for valuable
business information in a large database
and “mines a mountain for a vein of
valuable ore”
Functions:
Classification
Forecasting
Clustering
Association
Sequencing
Data Mining for Decision
Support
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Data Mining
vs.
OLAP
OLAP
Data Mining
Purpose
Supports data
analysis and
decision making
Supports data
analysis and
decision making
Types of analysis
Top
-
down,
query
-
driven
Bottom
-
up,
discovery
-
driven
User’s Skills
Data analysis
and data
business context
Must trust data
mining tools
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