CHAPTER 10 DATA, KNOWLEDGE, AND DECISION SUPPORT

1
CHAPTER 10DATA, KNOWLEDGE,AND DECISION SUPPORT
2
Management andDecision 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
6
Data Sources and Collection

• 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

7
Data Quality

• Accurate
• Secure
• Relevant
• Timely
• Complete
• Consistent

8
Difficulties in data management

• 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

9
Decision Making Process
10
Models

• 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

11
Mathematical Models

• Model variables investigated characteristics of
  real world system
• Parameters represent internal and external
  conditions
• Managerial solutions are reflected in models
  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
A Framework for Computerized Decision Support

• 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

16
Management Science

• 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

17
DSS Characteristics

• 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

18
Components of DSS

• Are implemented on the software level
• Data Management
• User Interface
• Model Management
• Knowledge Management
• Users

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)
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
Knowledge Discovery

• 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

29
Online Analytical Processing (OLAP)

• 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

30
Data Mining for Decision Support

• 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

31
Data Mining vs. OLAP