Decision Support Systems

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6 Νοε 2013 (πριν από 4 χρόνια και 8 μήνες)

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1

CHAPTER 10

DATA, KNOWLEDGE,

AND DECISION SUPPORT

2

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


4

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

5

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

6


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

7

•
Accurate

•
Secure

•
Relevant

•
Timely

•
Complete

•
Consistent


Data Quality


8


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

9

Decision Making Process



Intelligence Phase

Design Phase

Choice Phases



REALITY

Implementation

of Solution



SUCCESS

FAILURE

Testing of Proposed
Solution

Verification
of the Model

Examination


10


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

11

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


12

Mathematical models (cont….)


Analytical models


Simulation models:


Advanced math. techniques


Computational methods


Computational algorithms


IT support

13

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

14

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

15


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

16


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

17



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

18

Are implemented on the software level


Data Management


User Interface


Model Management


Knowledge Management


Users


Components of DSS

19

Decision Support Systems


Individual DSSs:


Functional analysts


Low
-
level managers


Group Decision Support Systems (GDSS):


Groups of managers


Top
-
level managers

20

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

21

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

22

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

23

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

24

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

25

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

26

Knowledge Management

Knowledge capturing, storing,
distribution require:


Knowledge identification


Knowledge discovery and analysis


Establishing organizational
Knowledge base



27

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


28


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

29


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)

30


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

31

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