decision support system
TRANSCRIPT
5/12/2010
Data & Information
The Business School
University of Kashmir
25/12/2010 10:21:02 PM Rafi A Khan
Definitions
Fact – statement of some element of truth about a subject matter or a domain.
Example: milk is white, sun rises in east
Intelligence – capacity to acquire, store, improve and apply knowledge
Experience – what we have done and what has happened in past in a specific
area of work
Common sense – natural ability to sense, judge or perceive situations ; grows
stronger over time
Memory – ability to store and retrieve relevant experience at will, is part of
intelligence
Learning – is knowledge or skill that is acquired by instruction or study
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Definitions
Knowledge. Information once analyzed, understood, and explained is
knowledge or foreknowledge (predictions or forecasts).
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Data, Information and Systems
Data vs. Information
– Data
• A ―given,‖ or fact; a number, a statement, or a picture
• Represents something in the real world
• The raw materials in the production of information
– Information
• Data that have meaning within a context
• Data in relationships
• Data after manipulation
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Data, Information and Systems
Data Manipulation
– Example: customer survey
• Reading through data collected from a customer survey with
questions in various categories would be time-consuming and
not very helpful.
• When manipulated, the surveys may provide useful
information.
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Why Information Systems
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What Is an Information System?
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Systems
Generating Information
– Computer-based ISs take data as raw material, process it,
and produce information as output.
Figure 1.1 Input-process-output
9Rafi A KhanFigure 1.2 Characteristics of useful information
Characteristics of Information
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INPUT OUTPUTPROCESS
FEEDBACK
Activities in an Information System
Why Information Systems
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Why Information Systems
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Information Needs of a Shopkeeper
Daily sales account
List of low stock items to be re-ordered
List of overstock items
Long overdue payments
Profit and loss account
Used to streamline day to day operations called Operational
information
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Slow or fast moving items
Reliable supplier of items
Sales trends
Used to improve profitability of shop called Tactical information
Information Needs of a Shopkeeper
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Whether to stock different varieties of items
Whether to diversify
Whether to start a new branch in a different locality
Whether to start an e-shop
Information to expand business and explore new opportunities
Known as Strategic Information
Information Needs of a Shopkeeper
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Types of Information
Strategic : Needed for long range planning and directions.
• This is less/un- structured.
Tactical : Needed to take short range decisions to improve
• Profitability and Performance.
Operational : Needed for day to day operations of the
organization.
• Eg: Daily Sales, Billing.
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SYSTEM
System as a group of interrelated components working together toward a
common goal by accepting inputs and producing outputs in an organized
transformation process.
Such a system has three basic interacting components or functions:
Input: Involves capturing and assembling elements that enter the system to be
processed. For example, raw materials, energy, data, and human effort must be
secured and organized for processing.
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SYSTEM
Processing: Involves transformation process that converts input into output.
Examples to these are manufacturing process, the human breathing process,
etc.
Output: Involves transferring elements that have been produced by
transformation process to their ultimate destination, Examples to these are
finished products, human services and management information that must be
transmitted to their human users.
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System
Input
Feedback and Control
OutputProcess
System
Environment
Fig. Showing Elements of a System
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System
A system with feedback and control components is sometimes called a
cybernetic system, that is, a self-monitoring, self-regulating system.
Feedback: It is data about the performance of a system. It is actually
measured in terms of the outcome to that of the predefined objectives
set out at the beginning of the process.
Control: It involves monitoring and evaluating feedback to determine
whether a system is moving toward the achievement of its goal.
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System Characteristics
A system does not exist in a Vacuum; rather, it exists and function in an
environment containing other systems
If a system is one of the components of a larger system, it is then
referred to as a subsystem, and the larger system is its environment.
The system that has the ability to change itself or its environment in
order to survive is an adaptive system
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Types of System
A large system can be split or decomposed into smaller subsystems
up to a certain level
The decomposition of a system into subsystems can be in a serial
form or it could be in a matrix form
In a serial system processing, the entire output of a subsystem is the
input to the next subsystem and so on.
In the matrix arrangement the different outputs go to different sub-
systems. A subsystem receives more than one input from other
subsystems.
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Types of System
If the process of input transformation is not visible and
understandable then we say that the system is a black box and
the process is not transparent
Most of the systems can be viewed in a hierarchical structure.
Breaking the system in a hierarchical manner provides a way to
structured systems analysis. It gives a clear understanding of the
contribution of each subsystem in terms of data flow and
decisions, and its interface to the other subsystems.
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Types of System
The systems can be classified in different categories based on the
predictability of its output and the degree of information exchange
with the environment.
Deterministic- when the inputs, the process and the outputs of a
system are known with certainty. In a deterministic system, you can
predict the output with certainty.
Probabilistic- when the output can only be predicted in
probabilistic terms. The accounting system is deterministic while
the demand forecasting system is a probabilistic one.
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Types of System
If a system is functioning in isolation from the environment, then the
system does not have any exchange with the environment nor is it
influenced by the environmental changes. Such a system is called a
closed system.
If the system has exchange with the environment and is influenced by
the environment then it is called an open system.
All kinds of accounting systems, viz., cash, stocks, attendance of
employees are closed systems. Most of the systems based on rules
and principles are closed systems.
The systems which are required to respond to changes in the
environment, such as marketing, communication and forecasting are
open systems
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Types of System
Specify in the inputs, processes, and outputs of the following
systems. Determine what is required for each system to be
efficient and effective.
Post Office
Elementary school
Grocery store
Farm
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Types of System
Organization Inputs Processes Outputs
Post Office Letters mailed Delivery of mail Mail delivered
School Students Teaching Graduating
students
Grocery StoreFood products Stocking, selling
Food sold to
customers
Farm Feedstock, seeds,
fertilizer
Animals and
plants
growing
Food delivered to
market
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System
List possible kinds of feedback for the systems in the previous question.
Post Office: Customers' complaints, average days for a delivery, cost,
percent of lost mail
School: Students' complaints, achievement on national tests, success
in job placement
Grocery store: Customer feedback on quality, quantity, percent of
theft and waste, etc.
Farm: Quality of output sold to market
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•Information system consists of physical and non-physical components
working together
•A computer combines with a software program may constitute an
information system, but only if the program is designed to produce
information that helps an organization or person to achieve a specific
goal.
Information Systems
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Management Information System (MIS) Computer-based or manual system
- transforms data into information to support the decision making.
MIS can be classified as performing three functions:
(1) To generate reports - for example, financial statements, inventory status reports, or performance reports needed for routine or non-routine purposes.
(2) To answer what-if questions asked by management. For example, questions such as "What would happen to deposits if the bank increases interest rates?" can be answered by MIS.
(3) To support decision making. This type of MIS is appropriately called Decision Support System (DSS).
-DSS attempts to integrate the decision maker, the data base, and the quantitative models being used.
Information Systems
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Information Systems?
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Sales and marketing
Manufacturing
Finance
Accounting
Human resources
Major Business Functions
WHY INFORMATION SYSTEMS?
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Marketing Management Information Systems:
It supports managerial activity in the area of product
development, distribution, pricing decisions, promotional
effectiveness, and sales forecasting.
It mainly relies on external sources of data like competitors
and customers.
MIS in Marketing
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Major functions of systems:
Sales management, market research, promotion, pricing, new
products
Major application systems:
Sales order info system, market research system, pricing
system
Sales and Marketing Systems
MAJOR TYPES OF SYSTEMS IN ORGANIZATIONS
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Sales and Marketing Systems
MAJOR TYPES OF SYSTEMS IN ORGANIZATIONS
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Manufacturing Management Information Systems:
Inventories are provided just in time to reduce costs of
warehousing huge inventories .
MIS in Manufacturing
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Major functions of systems:
Scheduling, purchasing, shipping, receiving, engineering, operations
Major application systems:
Materials resource planning systems, purchase order control systems, engineering systems, quality control systems
Manufacturing and Production Systems
MAJOR TYPES OF SYSTEMS IN ORGANIZATIONS
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MAJOR TYPES OF SYSTEMS IN ORGANIZATIONS
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MAJOR TYPES OF SYSTEMS IN ORGANIZATIONS
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Financial Management Information Systems:
It provides financial information to all financial managers
within an organization including the chief financial officer.
The chief financial officer analyzes historical and current
financial activity, future financial needs, and monitors and
controls the use of funds over time using the MIS
MIS in Finance
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Major functions of systems:
Budgeting, general ledger, billing, cost accounting
Major application systems:
General ledger, accounts receivable, accounts payable,
budgeting, funds management systems
Financing and Accounting Systems
MAJOR TYPES OF SYSTEMS IN ORGANIZATIONS
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MAJOR TYPES OF SYSTEMS IN ORGANIZATIONS
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Human Resources Management Information Systems:
These systems are concerned with activities related to
workers, managers, and other individuals employed by the
organization.
It includes, work-force analysis and planning, hiring,
training, and job assignments.
MIS in HR
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Major functions of systems:
Personnel records, benefits, compensation, labor relations,
training
Major application systems:
Payroll, employee records, benefit systems, career path
systems, personnel training systems
Human Resource Systems
MAJOR TYPES OF SYSTEMS IN ORGANIZATIONS
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MAJOR TYPES OF SYSTEMS IN ORGANIZATIONS
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MAJOR TYPES OF SYSTEMS IN ORGANIZATIONS
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People: Managers, knowledge workers, data workers,
production or service workers
Structure: Organization chart, products, geography
Operating procedures: Standard operating procedures (SOP,
rules for action)
Key Elements of An Organization
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Hardware: Physical equipment
Software: Detailed preprogrammed instructions
Storage: Physical media for storing data and the software
Communications Technology: transfers data from one
physical location to another
Networks: link computers to share data or resources
IT/Tools for Managers
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IS & Organizations
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TOWARD THE DIGITAL FIRM
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Information Sytems
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Major Types of Systems
• Executive Support Systems (ESS)
• Decision Support Systems (DSS)
• Management Information Systems (MIS)
• Knowledge Work Systems (KWS)
• Office Systems
• Transaction Processing Systems (TPS)
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MAJOR TYPES OF SYSTEMS IN ORGANIZATIONS
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MAJOR TYPES OF SYSTEMS IN ORGANIZATIONS
Transaction Processing Systems (TPS)
Operational Level :
• Basic business systems that serve the operational level
• A computerized system that performs and records the daily
routine transactions necessary to the conduct of the business
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MAJOR TYPES OF SYSTEMS IN ORGANIZATIONS
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MAJOR TYPES OF SYSTEMS IN ORGANIZATIONS
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Knowledge Work Systems (KWS)
Knowledge level
Inputs : Design specs
Processing : Modeling
Outputs : Designs, graphics
Users : Technical staff and professionals
Example: Engineering work station
MAJOR TYPES OF SYSTEMS IN ORGANIZATIONS
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Management Information System (MIS)
Management level
Inputs : High-volume data
Processing : Simple models
Outputs : Summary reports
Users : Middle managers
Example: Annual budgeting
MAJOR TYPES OF SYSTEMS IN ORGANIZATIONS
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MAJOR TYPES OF SYSTEMS IN ORGANIZATIONS
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MAJOR TYPES OF SYSTEMS IN ORGANIZATIONS
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Decision Support System (DSS)
Management level
Inputs : Low/High volume data
Processing : Interactive
Outputs : Decision analysis
Users : Professionals, Staff
Example: Forecasting
MAJOR TYPES OF SYSTEMS IN ORGANIZATIONS
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MAJOR TYPES OF SYSTEMS IN ORGANIZATIONS
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Executive Support System (ESS)
Strategic level
Inputs : Aggregate data
Processing : Interactive
Outputs : Projections
Users : Senior managers
Example: 5-year operating plan
MAJOR TYPES OF SYSTEMS IN ORGANIZATIONS
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MAJOR TYPES OF SYSTEMS IN ORGANIZATIONS
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MAJOR TYPES OF SYSTEMS IN ORGANIZATIONS
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Business processes
Manner in which work is organized, coordinated, and focused to produce a valuable product or service
Concrete work flows of material, information, and knowledge—sets of activities
Business Processes and Information Systems
ENTERPRISE APPLICATIONS
Management Information Systems 8/eChapter 2 Information Systems in the Enterprise
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Unique ways to coordinate work,
information, and knowledge
Ways in which management chooses
to coordinate work
Business Processes and Information Systems
Management Information Systems 8/eChapter 2 Information Systems in the Enterprise
ENTERPRISE APPLICATIONS
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Business Processes and Information Systems
Information systems help organizations
Achieve great efficiencies by automating parts of processes
Rethink and streamline processes
Management Information Systems 8/eChapter 2 Information Systems in the Enterprise
ENTERPRISE APPLICATIONS
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Manufacturing and production: Assembling product, checking
quality, producing bills of materials
Sales and marketing: Identifying customers, creating customer
awareness, selling
Examples of Business Processes
Management Information Systems 8/eChapter 2 Information Systems in the Enterprise
ENTERPRISE APPLICATIONS
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Finance and accounting: Paying creditors, creating financial
statements, managing cash accounts
Human Resources: Hiring employees, evaluating performance,
enrolling employees in benefits plans
Examples of Business Processes
Management Information Systems 8/eChapter 2 Information Systems in the Enterprise
ENTERPRISE APPLICATIONS
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Cross-Functional Business Processes
Transcend boundary between sales, marketing, manufacturing, and research and development
Group employees from different functional specialties to a complete piece of work
Example: Order Fulfillment Process
Business Processes and Information Systems
Management Information Systems 8/eChapter 2 Information Systems in the Enterprise
ENTERPRISE APPLICATIONS
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Management Information Systems 8/eChapter 2 Information Systems in the Enterprise
ENTERPRISE APPLICATIONS
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Enterprise Applications
Enterprise systems
Supply chain management systems
Customer relationship management systems
Knowledge management systems
Management Information Systems 8/eChapter 2 Information Systems in the Enterprise
ENTERPRISE APPLICATIONS
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Within the business: There are functions, each having its uses of
information systems
Outside the organization‘s boundaries: There are customers and
vendors
Functions tend to work in isolation
Traditional View of the Systems
Management Information Systems 8/eChapter 2 Information Systems in the Enterprise
ENTERPRISE APPLICATIONS
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Management Information Systems 8/eChapter 2 Information Systems in the Enterprise
ENTERPRISE APPLICATIONS
Figure 2-13
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Management Information Systems 8/eChapter 2 Information Systems in the Enterprise
ENTERPRISE APPLICATIONS
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Firm structure and organization: One organization
Management: Firm-wide knowledge-based management processes
Technology: Unified platform
Business: More efficient operations and customer-driven business processes
Benefits of Enterprise Systems
Management Information Systems 8/eChapter 2 Information Systems in the Enterprise
ENTERPRISE APPLICATIONS
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Difficult to build: Require fundamental changes in the way the business operates
Technology: Require complex pieces of software and large investments of time, money, and expertise
Centralized organizational coordination and decision making: Not the best way for the firms to operate
Challenges of Enterprise Systems
Management Information Systems 8/eChapter 2 Information Systems in the Enterprise
ENTERPRISE APPLICATIONS
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Supply Chain Management (SCM)
Close linkage and coordination of activities involved in buying, making, and moving a product
Integrates supplier, manufacturer, distributor, and customer logistics time
Reduces time, redundant effort, and inventory costs
Supply Chain Management (SCM)
Management Information Systems 8/eChapter 2 Information Systems in the Enterprise
ENTERPRISE APPLICATIONS
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Supply Chain
Network of organizations and business processes
Helps in procurement of materials, transformation of raw materials
into intermediate and finished products
Supply Chain Management (SCM)
Management Information Systems 8/eChapter 2 Information Systems in the Enterprise
ENTERPRISE APPLICATIONS
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Limitations:
Inefficiencies can waste as much as 25% of company‘s operating costs
Bullwhip Effect: Information about the demand for the product gets
distorted as it passes from one entity to next
Supply Chain Management (SCM)
Management Information Systems 8/eChapter 2 Information Systems in the Enterprise
ENTERPRISE APPLICATIONS
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Management Information Systems 8/eChapter 2 Information Systems in the Enterprise
ENTERPRISE APPLICATIONS
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Helps in distribution of the finished products to customers
Includes reverse logistics - returned items flow in the reverse direction
from the buyer back to the seller
Supply Chain Management (SCM)
Management Information Systems 8/eChapter 2 Information Systems in the Enterprise
ENTERPRISE APPLICATIONS
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Decide when, what to produce, store, move
Rapidly communicate orders
Communicate orders, track order status
Check inventory availability, monitor levels
Track shipments
Plan production based on actual demand
Rapidly communicate product design change
Provide product specifications
Share information about defect rates, returns
How Information Systems Facilitate Supply Chain Management
Management Information Systems 8/eChapter 2 Information Systems in the Enterprise
ENTERPRISE APPLICATIONS
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Supply chain planning system: Enables firm to generate forecasts for
a product and to develop sourcing and a manufacturing plan for the
product
Supply chain execution system: Manages flow of products through
distribution centers and warehouses
Supply Chain Management (SCM)
Management Information Systems 8/eChapter 2 Information Systems in the Enterprise
ENTERPRISE APPLICATIONS
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Uses digital technologies to enable multiple organizations to
collaboratively design, develop, build, move, and manage products
Increases efficiencies in reducing product design life cycles, minimizing
excess inventory, forecasting demand, and keeping partners and
customers informed
Collaborative Commerce
Management Information Systems 8/eChapter 2 Information Systems in the Enterprise
ENTERPRISE APPLICATIONS
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Management Information Systems 8/eChapter 2 Information Systems in the Enterprise
ENTERPRISE APPLICATIONS
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Private Industrial Networks
Web-enabled networks
Link systems of multiple firms in an industry
Coordinate transorganizational business processes
Industrial Networks
Management Information Systems 8/eChapter 2 Information Systems in the Enterprise
ENTERPRISE APPLICATIONS
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Customer Relationship Management (CRM)
Manages all ways used by firms to deal with existing and potential new customers
Business and Technology discipline
Uses information system to coordinate entire business processes of a firm
Customer Relationship Management (CRM)
Management Information Systems 8/eChapter 2 Information Systems in the Enterprise
ENTERPRISE APPLICATIONS
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Provides end-to-end customer care
Provides a unified view of customer across the company
Consolidates customer data from multiple sources and provides
analytical tools for answering questions
Customer Relationship Management (CRM)
Management Information Systems 8/eChapter 2 Information Systems in the Enterprise
ENTERPRISE APPLICATIONS
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Management Information Systems 8/eChapter 2 Information Systems in the Enterprise
ENTERPRISE APPLICATIONS
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Knowledge Management Systems
Creating knowledge
Discovering and codifying knowledge
Sharing knowledge
Distributing knowledge
Management Information Systems 8/eChapter 2 Information Systems in the Enterprise
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The End
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Management Information Systems
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Feasibility Study
Three types of feasibility :
Technical Feasibility
Economical Feasibility
Operational Feasibility
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Technical Feasibility
H/W - I/P, O/P, Communication, Storage
S/W - Database, OS, Languages
Application - System Packages, Management Science Models
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Economical Feasibility
Costs - Sytems/Programmes, Operations, H/W, S/W
Savings - Operating Expenses, Clerical Personnel,
Equipment
Benefits - Tangible ---- Reduction in Production Cost
Intangible ---- Customer Satisfaction
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Operational Feasibility
Management - Operating Management
Middle Management
Top Management
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Reports of MIS
Periodic Scheduled Reports.
Exception Reports.
Demand Reports and Responses.
Push Reporting.
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Information Technology and MIS
Information Technology is defined as that branch of computer science
that includes:
Hardware.
Software.
Communication Technology.
Storage systems and
Other Information processing technologies.
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Computer Hardware-------The physical equipment
Communication
Devices
Secondary storage
•Magnetic disk
•Optical disk
•Magnetic tape
Primary StorageCentral
Processing Unit
Output Devices
•Printers
•VDT
•Plotters
•Audio output
Input Devices
•Keyboard
•Computer mouse
•Touch screen
•Source data automation
Buses
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Computer Software
Computer
Software
Application
Software
System
Software
General purpose
Application
Programs
Application-Specific
Programs
System Management
Programs
System Development
Programs
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Communication Technology
Communications technology allows systems to transfer data from one location to
another for the transmission of voice, data, images, sound and even video. It can
take the form of :-
• Wired transmission: The transmission media can be
• Twisted pair cable.
• Coaxial cable.
• Fiber-optic cable.
• Wireless transmission: This includes:-
• Microwave Transmission.
• Satellite Transmission.
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Using Communication Technology for Business Solutions
The Internet is revolutionizing communications by providing a
worldwide network linking business, government, and scientific and
educational organizations to individuals. Internet use falls into several
major areas, including:
• Electronic mail/ Voice mail.
• World Wide Web.
• Chat.
• Electronic Data Interchange.
• Electronic Commerce.
• Mobile Commerce.
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Using Communication Technology for Business Solutions
Intranets
These help organizations in creating richer, more responsive information
environments in which members of an organization can exchange
ideas, share information and work together on common projects and
assignments regardless of their physical location.
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Using Communication Technology for Business Solutions
Extranets
These are privately owned networks that are extended to authorized users outside the company e.g. authorized buyers, retailers, distributors, customers. They are often used for collaborating with other companies for:
1. Supply Chain Management.
2. Customer Relationship Management.
3. Product design and development.
4. Training efforts.
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Suppliers
Customer Relationship
Management.
Marketing. Sales. Service
Supply Chain Management.
Sourcing. Procurement
Enterprise Resource Planning.
Internal Business Processes.
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sEnterprise application architecture presenting an overview of the major
cross-functional enterprise applications and their interrelationships.
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Supply Chain Management
It is a cross-functional inter-enterprise system that uses information
technology to help support and manage the links between some of
company‘s key business processes and those of its suppliers, customers
and business partners.
The goal of SCM is to create a fast, efficient and low-cost network of
business relationships, or supply chain, to get a company‘s products
from concept to market.
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Enterprise Resource Planning
Integrated cross-functional software that re-engineers manufacturing,
distribution finance, human resources and other basic business process
of a company to improve its efficiency, agility, and profitability.
It focuses on the company‘s internal aspects giving them an integrated
real-time view of its core business processes.
Simply the technological backbone of e-business.
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Customer Relationship Management
A cross-functional e-business application that integrates and automates
many customer serving processes in sales, direct marketing, account
and order management, and customer service and support.
CRM systems create an IT framework of web-enabled software and
databases that integrates these processes with the rest of a company‘s
business operations.
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Knowledge Management
Organizing and sharing the diverse forms of business information
created within an organization. Includes managing project and
enterprise document libraries, discussion databases, intranet website
databases, and other types of knowledge bases.
Different phases of a knowledge management system (KMS).
• Capturing/Acquisition of data/information
• Transformation of Info. into Knowledge
• Knowledge Storage
• Disseminating/Sharing of Knowledge
Figure
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Capturing/Acquisition of Data/Information
Various technologies that can help in capturing of information
are:
• Document Management System:
Document management system keeps track of masses
of data and information, which is stored in a secure file
vault where its integrity is guaranteed and all changes to it,
is monitored, controlled, and recorded providing far easy
and faster access to all the documents. It takes care of
creating, storing, editing, and distributing documents.
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Capturing/Acquisition of Data/Information
Database
Database is a collection of data organized to serve many
applications efficiently by centralizing the data and minimizing
redundant data. It is a computerized record keeping system that stores,
maintains, and provides access to information.
Database Management System (DBMS) is simply the software
that permits an organization to centralize data, manage them
efficiently, and provide access to the stored data by applications
programs. The DBMS acts as an interface between application
programs and the physical data files.
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Capturing/Acquisition of Data/Information
Data Warehouse
An integrated collection of data extracted from operational,
historical and external databases and cleaned, transformed and
cataloged for retrieval and analysis to provide business intelligence for
business decision making.
Search Engines
These are huge databases of web page files that have been
assembled automatically by machine.
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Transformation of Info. into Knowledge
Useful technologies for this phase of the knowledge management process
include:
Multidimensional Data Analysis: Another term for multidimensional
data analysis is Online Analytical Processing (OLAP), which is a function
of business intelligence software that enables a user to easily and
selectively extract and view data from different points of view.
OLAP tools structure data hierarchically – the way managers think of
their enterprises, and also allows business analysts to rotate that data,
changing the relationships to get more detailed insight into corporate
information.
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Transformation of Info. into Knowledge
Data mining or Knowledge Discovery in Databases (KDD) provides an
organization with highly tangible benefits in the area of analysis. Data
mining is the nontrivial extraction of implicit, previously unknown,
and potentially useful information from data. This encompasses a
number of different technical approaches such as clustering, data
summarization, learning classification rules, finding dependency net
works, analyzing changes, and detecting anomalies.
Data mining software tools find hidden patterns and relationships in
large pools of data and infer rules from them that can be used to
predict future behavior and guide decision-making.
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Transformation of Info. into Knowledge contd.
Decision Support Systems (DSS)
These are a specific class of computerized information system that
supports business and organizational decision-making activities.
Artificial Intelligence (AI)
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Info/Knowledge Storage
Knowledge repositories are widely recognized as key components of
most knowledge management systems. Once knowledge is captured, it
must be stored in a knowledge repository. A knowledge repository is a
collection of both internal and external knowledge.
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Info/Knowledge Dissemination
The final phase is effectively communicating the captured "knowledge."
In fact, knowledge is not truly captured. Instead, what is captured is
information that is more easily transformed into knowledge by the
recipient. The key technologies that can be used for dissemination are:
• Teleconferencing, Data-conferencing
• Videoconferencing
• Groupware, and
• Intranets.
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Decision Making2009
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DECISION MAKING
System is a collection of objects such as people, resources,
concepts, and procedures intended to perform a function or
to serve a goal.
• Closed systems are totally independent.
• Open systems dependent on their environment.
• System effectiveness is the degree to which goals are achieved.
• System efficiency is a measure of the use of inputs (or resources)
to achieve outputs.
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Decision making is a process of choosing among alternativecourses of action for the purpose of attaining a goal or goals.
(1) intelligence
(2) design
(3) choice
(4) implementation
problem solving
decision making
decision making
problem solving
Simon’s 4 Phases of Decision Making
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INTELLIGENCE PHASE
Organizational objectives
Search and scanning
Data collection
Problem identification
Problem ownership
Problem classification
Problem statement
DESIGN PHASE
Formulate a model
Set criteria for choice
Search for alternatives
Predict and measure outcomes
Reality
Implementation
of solution
Failure
Solution
Alternatives
Problem statement
Validation of the model
Verification, testing of proposed solution SuccessCHOICE PHASE
Solution to the model
Sensitivity analysis
Selection of best alternative (s)
Plan for implementation
Simplification/Assumption
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1. Intelligence phase
Scan the environment
Analyze organizational goals (e.g. Inventory Management, Job Selection, lack or an incorrect web presence)
Collect data (Monitoring & analyzing)
Identify problem
Categorize problem
– Programmed (repetitive & routine) ---Scheduling of employees, inventory level etc
– Non-programmed (Unstructured) --- Merger & Acquisitions
– Decomposed into smaller parts
Assess ownership and responsibility for problem resolution
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2. Design phase
• Formulate a model
• Set criteria for choice (Are we willing to take High risk or we prefer low risk approach)
• Search for alternatives
• Predict and measure outcomes (E.g. Profit Maximization)
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3. Choice phase
•Each alternative must be evaluated
•Sensitivity analysis (determines robustness of any given alternative)
•Selection of best alternative (s)
•Plan for implementation
solution - set of values for the decision variables in a selected alternative
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4. Implementation phase
•Putting a recommended solution to work
• Vague boundaries which include:
–Dealing with resistance to change
–User training
–Upper management support
•The problem is considered solved after the recommended solution to the model is successfully implemented.
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Source: Based on Sprague, R.H., Jr., “A Framework for the Development of DSS.” MIS Quarterly, Dec. 1980, Fig. 5, p. 13.
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Decision Support Systems
Intelligence Phase
– Automatic
• Data Mining
– Expert systems, CRM, neural networks
– Manual
• OLAP
• KMS
– Reporting
• Routine and ad hoc
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Decision Support Systems
Design Phase
– Financial and forecasting models
– Generation of alternatives by expert system
– Relationship identification through OLAP and data mining
– Use of KMS
– Business process models from CRM, RMS, ERP, and SCM
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Decision Support Systems
Choice Phase
– Identification of best alternative
– Identification of good enough alternative
– What-if analysis
– Goal-seeking analysis
– May use KMS, GSS, CRM, ERP, and SCM systems
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Decision Support Systems
Implementation Phase
– Improved communications
– Collaboration
– Training
– Supported by KMS, expert systems, GSS
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TYPES OF DECISIONS
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TYPES OF DECISIONS
Decisions are categorized along two dimensions:-
The nature of the decision to be made
The scope of the decision itself
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TYPES OF DECISION
On the basis of the nature of the decision:-
1)Structured decision:-It‘s the one for which a well defined decision making
procedure exists.
2)Unstructured decision:- it is the one for which all the three decision phases
are unstructured.
3)Semi structured decision:- In this type one or two phases are structured and
the others are not.
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On the basis of scope of the decision itself.
1. Strategic Decision:- It is the one which effects the entire organization or a
major part of it for a long period of time
2. Tactical Decision:- It effects how a part of the organization does business
for a limited time in the future.
3. Operational Decision:- It is the one which effects a particular activity
currently taking place in an organization but either has a little impact on
the future.
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Combination of various types of Decisions
Structured /operational
Structured / tactical
Structured/ strategic
Semi-structured/ operational.
Semi-structured/ tactical
Semi-structured / strategic
Unstructured/ operational
Unstructured/ tactical
Unstructured/ strategic
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Structured/Operational: Decide how to cut a log into boards in order
to minimize wastage.
The intelligence phase is trivial; if a log arrives at mill, it must be cut .
The design phases likewise fixed; the products that the mill produces
and hence the acceptable types of cuts.
The choice phase can be optimized mathematically because the value of
each potential board is known from business consideration and the
number of boards that can be operated via each communication of cuts
is a problem.
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Structured /Tactical: Choosing the way in which to depreciate
corporate assets.
Resource allocation problems that can be solved by linear
programming methods are also in this category.
Structured /Strategic: Deciding weather or not to proceed with an
R&D project on the bases of projected ROI
A plant location decision could be in this category if the only factors in
decisions are quantifiable, such as transportation costs of known raw
materials from known locations and of known products to known
markets.
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Semistructured/Operational: Deciding to accept or reject an applicant
to a selective collage.
Semitructured /Tactical: Choosing an insurance company for an
employee health program. Cost per employee is an important and
objective factor in this decision. Intangible factors include acceptability
of a company to the employee population and the relative importance
of different benefits: is 100 percent hospitalization coverage with Rs.
500 deductible amount better or worse than 80 percent coverage with
no deductible?
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Semitructured /Strategic: Deciding whether or not to enter a new
market. Sales projections, marketplace growth data, development cost
estimates and marketing expenses forecasts can combine to provide a
profit-and-loss forecast. However there are countless factors that could
make it totally worthless. Judgment of experienced managers is
needed for the final step.
Unstructured/Operational: Dealing with a machine breakdown. There
is no set procedure what to do while awaiting repairs. The decision is
operational because the way a company deals with one machine failure
need not set a precedent for the next.
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Unstructured /Tactical: Hiring decisions typically fall into this area,
especially if the job to be filled is above level where aptitude and
ability tests can be relied on as performance indicators.
Unstructured/Strategic: Deciding how to respond to an unfriendly
takeover proposal made by a competitor. The action can have a long
term impact on the entire firm.
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Decision Support Frameworks
Type of Control
Type of Decision: Operational Control Managerial Control Strategic Planning
Structured
(Programmed)
Accounts receivable,
accounts payable, order
entry
Budget analysis, short-
term forecasting,
personnel reports
Investments, warehouse
locations, distribution
centers
Semistructured Production scheduling,
inventory control
Credit evaluation,
budget preparation,
project scheduling,
rewards systems
Mergers and
acquisitions, new
product planning,
compensation, QA, HR
policy planning
Unstructured
(Unprogrammed)
Buying software,
approving loans, help
desk
Negotiations,
recruitment, hardware
purchasing
R&D planning,
technology
development, social
responsibility plans
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The components of the quantitative model– result variable indicate how well the system performs– decision variables describe the alternative course of action– uncontrollable variables or parameters are not under the control of the decision maker
Uncontrollablevariables
Mathematicalrelationships
Result variablesDecision variables
– intermediate result variables reflect intermediate outcomes
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Examples of the Components of Models.
Area
Decision
Variables
Result
Variables
Uncontrollable
Variables and Parameters
Financial investment Investment alternatives and
amounts
How long to invest
When to invest
Total profit
Rate of return (ROI)
Earnings per share
Liquidity level
Inflation rate
Prime rate
Competition
Marketing Advertising budget
Where to advertise
Market share
Customer satisfaction
Customers' income
Competitors' actions
Manufacturing What and how much to
produce
Inventory levels
Compensation programs
Total cost
Quality level
Employee satisfaction
Machine capacity
Technology
Materials prices
Accounting Use of computers
Audit schedule
Data processing cost
Error rate
Computer technology
Tax rates
Legal requirements
Transportation Shipments schedule Total transport cost Delivery distance
Regulations
Services Staffing levels Customer satisfaction Demand for services
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Example
Company makes special purpose computers.Decision to be made: how many computers should be produced next month?Two types of computers are considered: T1, T2.They require different days of labour, different costs for material.
Uncontrollable
variables
constraints on labour
and budget
Mathematical
relationships
Maximise profit
subject to constraints
Result variables
Total profit
Decision variables
X1 = NofT1
X2 = NofT2
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Principle of choice is a decision regarding the acceptability of a solution approach.
• Normative models– chosen alternative is the best of all possible alternatives– suboptimisation– optimisation models
• Descriptive models describe things as they are, or as they arebelieved to be.
– no guarantee a solution is optimal– simulation
Generating alternatives– automatically by the model– by using heuristics
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Predicting the outcomes of alternatives
1. Decision making under certainty
Decision maker knows exactly what the outcome of
each course of action will be - deterministic environment.
2. Decision making under risk
Each alternative has several possible outcomes,
each with a given probability of occurrence
- probabilistic or stochastic decision situation.
3. Decision making under uncertainty
Several outcomes are possible for each course of action,
their probabilities are not known.
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Measuring outcomes
The value of the an alternative is judged in terms of
goal attainment.
Scenario describes the decision and uncontrollable variables
and parameters for a specific modelling situation.
Of special interest are:
– the worst possible scenario
– the best possible scenario
– the most likely scenario
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Search
• Analytical techniques
– mathematical formulae
– algorithm: step-by-step search process
• Blind search
– complete enumeration
– incomplete search
• Heuristic search (derived from the Greek word for discovery)
rules guide the search process
Normative models:
– analytical techniques
– complete, exhaustive enumeration
Descriptive models:
– blind search
– using heuristics
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Evaluation
• Multiple goals
– Today's management systems want to achieve
multiple goals simultaneously.
– Goals are usually partially or totally conflicting.
• Sensitivity analysis
Checks the impact of a change in the input data or parameters
on the proposed solution (the result variable)
1. Automatic sensitivity analysis
tells the range within which an input variable or parameter
can vary without impact on the proposed solution
one change at a time
2. Trial and error
some input data are changed
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• What-if-analysis
What will happen to the solution if an input variable or
a parameter is changed?
e.g. what will happen to the total inventory cost if the cost of
carrying inventories increases by 10%?
• Goal seeking analysis
Computes the amount of inputs necessary to achieve a desired level
of an input (goal).
e.g. How many nurses are needed to reduce the average waiting time
of a patient in the emergency room to less than 10 minutes.
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Literature:
1. (a) Decision Support Systems and Intelligent Systems, Fifth Edition
E.Turban, Jay Aronson,
Prentice Hall, 1998.
(b) Decision Support Systems and Expert Systems,
Management Support Systems, E.Turban, Fourth Edition,
Prentice Hall, 1995.
2. Knowledge-based Decision Support Systems, With Applications
in Business, 2nd Edition, M. Klein, L. Methlie,
Wiley, 1995.
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Systems are composed of inputs, outputs, processes, and
decision makers.
A model is simplified representation or abstraction of reality.
They can be iconic, analog, or mathematical.
Decision making involves four major phases: intelligence, design,
choice, and implementation.
SUMMARY
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Models
A model is a simplified representation or abstraction of reality.
1. Iconic model is a physical replica of a system.
2. Analog model gives a symbolic representation of reality, behaves like the real system but does not look like it.
3. Mathematical (quantitative) models use mathematical relationshipsBenefits:– compression of time– easy model manipulation– low cost of the analysis– cost of making mistakes is less than mistakes on real system– can model risk and uncertainty– a very large number of solutions can be analysed– enhance learning and training
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3. Optimisation
model generates an optimal solution
Limitations:
– works if the problem is structured and deterministic
4. Heuristics
Informal knowledge of how to solve problems efficiently and
effectively, how to plan steps in solving a complex problem,
how to improve performance, and so forth.
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Modelling Process
Example: How much to order for the grocery?
The Question: How much bread to stock each day?
1. Trial-and-error
experimentation on the real system
Not appropriate if:
– too many alternatives to explore
– the cost of making errors is very high
– the environment keeps changing
2. Simulation
assume the appearance of the characteristics of reality
Problems:
– no guarantee that the solution is optimal one
– professional development
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Definition of DSS
DSS is an interactive computer-based systems, which help decision makers
utilize data and models to solve unstructured problems.
DSS is an interactive computer-based systems, which help decision makers
utilize data and models to solve unstructured problems.
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Definition of DSS
Decision Support Systems (DSS) are a class of computerized information
systems that support decision-making activities. DSS are interactive
computer-based systems and subsystems intended to help decision makers
use communications technologies, data, documents, knowledge and/or
models to successfully complete decision process tasks.
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Components of DSS
Other computer based systems
Internet, intranet, extranet.
Data management Model management External models
Knowledge-based subsystems
User interface
Manager (user)Organizational KB
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Components of DSS
Data management subsystem
The data management subsystem includes a data base, which contains
relevant data for the situation and is managed by software call the database
management system (DBMS) .the data management subsystem can be
interconnected with the corporate data warehouse, a repository for
corporate relevant decision making data.
Model management subsystem
This is software package that includes financial, statistical, management
science, or other quantitative models that provide the system analytical
capabilities and appropriate software management. Modeling languages in
building custom models are also included, this software is often called a
model base management system (MBMS). This component can be
connected to corporate or external storage of models.
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Components of DSS
Knowledge based management subsystem
This subsystem can support any of the other subsystems or act as an
independent component. It provides intelligence to augment the decision
maker‘s own. It can be interconnected with the organization‘s knowledge
depository, which is called the organizational knowledge base.
User interface subsystem
The user communicates with and commands the DSS through this
subsystem. The user is considered part of the system. Researchers assert that
some of unique contributions of DSS are derived from the intensive
interaction between the computer and the decision maker.
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TH
E D
AT
A M
AN
AG
EM
EN
T S
UB
SY
ST
EM
External data Source
Internal data sources
Finance Marketing Production Personal Other
Extraction
Organizationalknowledge base
Private personal data
Decision support
database
QueryFacility
Corporate datawarehouse
Database management System
oRetrievaloInquiryoUpdateoReport generationoDelete
Data directory
Interfacemanagement
Model management
Knowledge-based subsystem
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THE DATA MANAGEMENT SUBSYSTEM
The data management subsystem is composed of the following elements:
DSS database
Database management system
Data directory.
Query facility.
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THE DATABASE
A database is a collection of interrelated data organized to meet the needs
and structure of an organization and can be used by more than one person
for more than one application
The data in the DSS database are extracted from internal and external data
sources, as well as from personal data belonging to one or more Users.
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DATA ORGANISATION
In small ad hoc DSS, data can be entered directly into models some times
extracted directly from larger databases.
In large organizations that use extensive data ,such as Wal-Mart, AT&T,
and United Air Lines data are organized in a data warehouse and used
when needed .
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EXTRACTION
To create a DSS database or a data warehouse it is often necessary to capture
data from several sources. This operation is called extraction.
It basically consists of importing of files, summarization, standardization,
filtration, and condensation of data.
The data for the warehouse are extracted from internal and external sources.
The extraction process is frequently managed by a DBMS.
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DATABASE MANAGEMENT SYSTEM
A database is created, accessed, and updated by a DBMS.
Most DSS are built with a standard commercial relational DBMS that
provides capabilities such as it captures or extracts data for inclusion in a
DSS database ,it updates (adds, deletes, edits, changes) data records and
files, retrieves data ,provides data security etc.
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THE QUERY FACILITY
Query facility is necessary to access, manipulate, and query data.
The query facility includes a special query language.
Important functions of DSS query system are selection and manipulation
operation (e.g., the ability to follow a computer instruction such as "Search
for a sales in zone B during June 2000 and summarize sales by salesperson").
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THE DIRECTORY
The data directory is a catalog of all the data in the database.
It contains data definitions and its main function is to answer questions
about the availability of data items, their source, and their exact meaning.
The directory is especially appropriate for supporting the intelligence phase
of the decision-making process by helping to scan data and identify problem
areas or opportunities.
It supports the addition of new entries, deletion of entries, and retrieval of
information on specific objects.
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General Functions of the DBMS
Data Definition
Provides a data definition language (DDL) that allows users to describe
the data entities and their associated attributes and relationships
Allows for the interrelation of data from multiple sources
Data Manipulation
Provides the user with a query language to interact with the database
Allows for capture and extraction of data
Provides rapid retrieval of data for ad hoc queries and reports
Allows for the construction of complex queries for retrieval and data
manipulation
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Data Integrity
Allows the user to describe rules (integrity constraints) to maintain the
integrity of the database
Assists in the control of erroneous data entry based on the defined integrity
constraints
Access Control
Allows identification of authorized users
Controls access to data various elements and data manipulation activities
within the database
Tracks usage and access to data by authorized users
Concurrency Control
Provides procedures for controlling simultaneous access to the same data
by more than one user
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Improved data sharing.
The DBMS helps create an environment in which end users have
better access to more and better-managed data. Such access takes it
possible for end users to respond quickly to changes in their
environment.
Transaction Recovery
Provides a mechanism for restart and reconciliation of the database in
the event of hardware failure
Records information on all transactions at certain points to enable
satisfactory database restart
Minimized data inconsistency.
Data inconsistency exists when different versions of the same data
appear in different places.
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Improved decision making.
Better-managed data and improved data access make it possible to
generate better quality information, on which better decisions are based.
Increased end-user productivity.
The availability of data, combined with the tools that transform data
into usable information, empowers end users to make quick, informed
decisions that can make the difference between success and failure in the
global economy.
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Models (model base)•Strategic, tactical, operational•Statistical, financial, marketing,
mgt. science, accounting etc•Model building blocks
Model directory
Model base management•Modeling commands : creation•Maintenance: update•Database interface•Modeling language
Model execution, integration, and command processor
Data management Interface management Knowledge –based subsystem
Structure of Model Management System
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Functions of the MBMS
Creates models easily and quickly, either from scratch or from the building
blocks
Allows users to manipulate models so that they can conduct experiments
and sensitivity analyses ranging from what-if to goal seeking
Stores, retrieves and manages a wide variety of different types of models
in a logical and integrated manner
Accesses and integrates the model building blocks
Catalogs and displays the directory of models for use by several
individuals in the organization
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Functions of the MBMS
Tracks model data and application use
Interrelates model with appropriate linkages with the database and
integrates them within the DSS
Manages and maintains the model base with management functions
analogous to database management: store, access, run, update, link, catalog,
and query
Use multiple models to support problem solving
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US
ER
INT
ER
FA
CE
MA
NA
GE
ME
NT
SY
ST
EM
Data management
and DBMSKnowledge- based
subsystemModel management
and MBMS
User Interface Management System
(UIMS)
Language Processor
Printers, plotters
Users
Input Output
Action DisplayLanguages Languages
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General Functions of the DSS Interface
Allows for interaction with the DSS in a variety of dialog styles
Accommodates the user with a variety of input devices
Presents data with a variety of formats and output devices
Gives user help capabilities, prompting, diagnostic and suggestion routines,
or any other flexible support.
Stores input and output data.
Provides support for communication among and between multiple DSS
users
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General Functions of the DSS Interface
variety of formats included menu driven, question/answer, procedural
command language, or natural command language
Provides for the presentation of data in a variety of formats
Allows for detailed report definition and generation by the DSS user
Allows for the creation of forms, tables, and graphics for data output
Can provide multiple ―windows‖ or views of the data to be available
simultaneously
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CHARACTERISTICS OF DSS
DSS provides support for decision makers mainly in semi-structured and
unstructured situations by bringing together human judgment and
computerized information.
Support is provided for various managerial levels, ranging from top
executives to line managers.
Support is provided to individuals as well as to groups.
DSS provides support to several interdependent or sequential decisions.
The decisions may be made once, several times or repeatedly.
DSS supports all phases of decision making process; intelligence, design,
choice and implementation.
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CHARACTERISTICS OF DSS
DSS are adaptive over time. DSS are flexible and so users can add, delete,
combine, change or rearrange basic elements.
User Interface – Interactive and friendly.
DSS attempt to prove the effectiveness of decision making rather than its
efficiency.
The decision maker has complete control over all steps of the decision
making process in solving a problem. A DSS specifically aims to support
and not to replace the decision maker.
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CHARACTERISTICS OF DSS
End users should be able to construct and modify simple systems by
themselves. Larger systems can be built with assistance from information
system (IS) specialists.
A DSS usually utilizes models for analyzing decision making situations. The
modeling capability enables experimenting with different strategies under
different configurations.
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Benefits of DSS Use
Extend the decision-maker‘s ability to process information and knowledge
Extend the decision-maker‘s ability to tackle large-scale, time-consuming,
complex problems
Improve the time associated with making a particular decision
Improve the reliability of a particular decision process or outcome
Encourage exploration and discovery on the part of the decision-maker
Reveal new approaches to thinking about a particular problem space or
decision context
Generate new evidence in support of a particular decision or confirmation
of existing assumptions
Create a strategic or competitive advantage over competing organizations
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Limitations of DSS Use
DSSs cannot yet be designed to contain distinctly human decision-
making talents such as creativity, imaginativeness, or intuition
The power of a DSS is limited by the computer system upon which it is
running, its design, and the knowledge it possesses at the time of its
use
Language and command interfaces are not yet sophisticated enough to
allow for natural language processing of user directives and inquiries
DSSs are normally designed to be narrow in scope of application thus
limiting their generalizability to multiple decision-making contexts
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DSS Classification
1. Alter’s Output Classification (1980)
2. Holsapple and Whinston’s Classification
1. Text-oriented DSS
2. Database-oriented DSS
3. Spreadsheet-oriented DSS
4. Solver-oriented DSS
5. Rule-oriented DSS
6. Compound DSS
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Alters' Classification of DSS
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Alter’s Classification of DSS
Data-Driven DSS
Data-Driven DSS take the massive amounts of data available through the
company's TPS and MIS systems and cull from it useful information which
executives can use to make more informed decisions.
Data- Driven DSS emphasize access to and manipulation of large databases
of structured data
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Alter’s Classification of DSS
Model-Driven DSS
A second category, Model-Driven DSS (accounting and financial models,
representational models, and optimization models).
Model-Driven DSS emphasize access to and manipulation of a model.
Model-Driven DSS use data and parameters provided by decision-makers to
aid them in analyzing a situation, but they are not usually data intensive.
Very large databases are usually not needed for Model-Driven DSS.
Primarily used for the typical "what-if" analysis. That is, "What if we
increase production of our products and decrease the shipment time?"
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DSS Classifications
Holsapple and Whinston’s Classification
1. Text-oriented DSS
2. Database-oriented DSS
3. Spreadsheet-oriented DSS
4. Solver-oriented DSS
5. Rule-oriented DSS
6. Compound DSS
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Holsapple and Winston Classification
TEXT ORIENTED DSS
Textually represented information that could have a bearing on decision.
Documents to be electronically created, revised and viewed as needed.
Information Technologies such as documents imaging, hypertext etc can be
incorporated into this type.
DMS, KMS, Content Mgt System, Business rule system
DATABASE ORIENTED DSS
In this type of DSS the database plays a major role in the DSS structure.
Strong report generation and query capabilities.
Data are organized in a highly structured format.
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Holsapple and Winston Classification
SPREADSHEET ORIENTED DSS
Spreadsheet is a modeling language that allows the user to write models to
execute DSS analysis.
Tools- Statistical packages, linear programming package (Solver), financial
and management science models.
The most popular tools used are Excel and Lotus 1-2-3.
SOLVER ORIENTED DSS
A solver is an algorithmic or procedure written as a computer program for
performing certain computations for solving a particular problem type.
EOQ for calculating optimal ordering quantity or a linear regression routine
for calculating trend.
Excel, Lotus 1-2-3 and quatro pro can be used to develop such a system.
C++, Lingo etc
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Holsapple and Winston Classification
RULE ORIENTED DSS
The knowledge component of DSS includes both procedural and inferential
(Reasoning) rules, often in an expert system, format.
Assignment Algorithm for Flight Scheduling
COMPOUND DSS
It is a hybrid system that includes two or more of the fine basic structures
explained above. It can be built by using a set of independent DSS, each
specializing in one area.
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Other DSS Classification
Personal
Group
Organizational
Custom VS Readymade
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DSS Classification
OTHER CLASSIFICATIONS OF DSS
INSTITUTIONAL DSS
Deal with decisions of a recurring nature. An institutionalized DSS can be
developed and refined as it evolves over a number of years because the DSS
is used repeatedly to solve identical or similar problems.
Portfolio Management
ADHOC DSS
Deals with specific problems that are usually neither anticipated nor
recurring. Adhoc decisions often involve strategic planning issues
sometimes management control problems.
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Knowledge-Driven DSS
Knowledge-Driven DSS
It suggest or recommend actions to managers.
These DSS are computer systems with specialized problem-solving
expertise.
The "expertise" consists of knowledge about a particular domain,
understanding of problems within that domain, and "skill" at solving some
of these problems.
A related concept is Data Mining. It refers to a class of analytical
applications that search for hidden patterns in a database.
Data mining is the process of searching through large amounts of data to
produce data content relationships.
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Document-Driven DSS
A new type of DSS, a Document-Driven DSS is evolving to help managers
retrieve and manage unstructured documents and Web pages.
The Web provides access to large document databases including databases
of hypertext documents, images, sounds and video.
Examples of documents that would be accessed by a Document-Based DSS
are policies and procedures, product specifications, catalogs, and corporate
historical documents, including minutes of meetings, corporate records, and
important correspondence.
A search engine is a powerful decision aiding tool associated with a
Document-Driven DSS.
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Communications-Driven and Group DSS
Group Decision Support Systems (GDSS) came first, but now a broader
category of Communications-Driven DSS or groupware can be identified.
It includes communication, collaboration and decision support technologies
that do not fit within those DSS types identified.
A Group DSS is a hybrid Decision Support System that emphasizes both the
use of communications and decision models.
A Group Decision Support System is an interactive computer-based system
intended to facilitate the solution of problems by decision-makers working
together as a group.
Groupware supports electronic communication, scheduling, document
sharing, two-way interactive video, White Boards, Bulletin Boards, and
Email.
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MIS
1. Impact on Structured Tasks, where standard procedures, decision
rules and information flows can be reliably Predefined.
2. Payoff – Improvement in efficient by reducing costs, turnaround
time , replacing clerical personnel or increasing their productivity.
Mg
t. Sci / O
R
1. Impact mostly on Structured problems (rather than tasks), in which
the objective, data and constraints can be prespecified.
2. Payoff – generation of better solutions for general categories of
problems (e.g. inventory).
DS
S
1. Impact is on decisions in which there is sufficient structure for
computer and analytic aids to be of value but where the managers
judgment is essential.
2. Payoff – extending the range and capability of managers decision
process to help them improve their effectiveness.
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MIS
3. Relevance for managers decision making – indirect (e.g. by
providing reports and access to data.
4. MIS application is routine and done periodically.
Mg
t. Sci / O
R
3. Relevance for managers – provision of detailed recommendation
and new methods handling complex problems.
4. Application are nonroutine, as needed.
DS
S
3. Relevance for managers – creation of supportive tool, under their
own control..
4. Application are nonroutine, as needed.
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5/12/2010
Knowledge Management
The Business School
University of Kashmir
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Knowledge Management
Ancient
Collaboration at the organizational level
Could revolutionize collaboration and computing
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Knowledge Management
Helps organizations
Identify
Select
Organize
Disseminate
Transfer
Important information and expertise within the organizational
memory in an unstructured manner
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Knowledge
Understanding gained through experience or study
Know-how or familiarity with how to do something
Information that is contextual, relevant, and actionable
Accumulation of facts, procedural rules or heuristics
Knowledge is INFORMATION IN ACTION
Actionable (relevant) information available in the right format, at
the right time, and at the right place for decision making
(TIWANA2000)
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Knowledge
Fact – statement of some element of truth about a subject matter or a domain.
Example: milk is white, sun rises in east.
Heuristics – rule of thumb based on years of experience.
Example: strike on independence day in our state
Intelligence – capacity to acquire, improve and apply knowledge.
Experience – what we have done and what has happened in past in a specific
area of work
Common sense – natural ability to sense, judge or perceive situations ; grows
stronger over time.
Memory – ability to store and retrieve relevant experience at will, is part of
intelligence.
Learning – is knowledge or skill that is acquired by instruction or study
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Knowledge Types
Explicit knowledge
– Objective, rational, technical
– Policies, goals, strategies, papers, reports
– Codified
– Leaky knowledge
Tacit knowledge
– Subjective, experiential learning
– Highly personalized
– Difficult to formalize
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Knowledge Types
Shallow (surface) knowledge
– Indicates minimal understanding of the problem area
Example – If u don‘t have petrol in your car, the car
wont start
Deep knowledge
– Indicates maximal understanding of the problem area
Example – why don‘t a car starts when it has no petrol
(need to know various components of car)
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Knowledge Types
– Descriptive – data, information
– Procedural – how to do something
– Reasoning – policies or rules
– Linguistic – vocabulary or grammar
– Presentation – graphing, messaging
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DATA
Processed
INFORMATIONRelevant and
actionable
KNOWLEDGE
Relevant and actionable data
Data, Information and Knowledge
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Knowledge Management (KM)
A process of capturing, transformation, and diffusion of
knowledge throughout an enterprise so that it can be
shared and thus REUSED
Helps organizations find, select, organize, disseminate,
and transfer important information and expertise
Transforms data / information into actionable knowledge
to be used effectively anywhere in the organization by
anyone
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KM Objectives
Create knowledge repositories
Improve knowledge access
Enhance the knowledge environment
Manage knowledge as an asset
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KMS Manage
Knowledge creation through learning
Knowledge capture
Knowledge sharing and communication through
collaboration
Knowledge access
Knowledge use and reuse
Knowledge archiving
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Cyclic Model of KM
Create knowledge
Capture knowledge
Refine knowledge
Store knowledge
Manage knowledge
Disseminate knowledge
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Cyclic Model of KM
Create Knowledge
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Cyclic Model of KM
Create Knowledge
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Cyclic Model of KM
Create Knowledge
CaptureKnowledge
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Cyclic Model of KM
Create Knowledge
CaptureKnowledge
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Cyclic Model of KM
RefineKnowledge
Create Knowledge
CaptureKnowledge
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Cyclic Model of KM
RefineKnowledge
Create Knowledge
CaptureKnowledge
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Cyclic Model of KM
RefineKnowledge
Create Knowledge
CaptureKnowledge
StoreKnowledge
22
2
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Cyclic Model of KM
RefineKnowledge
Create Knowledge
CaptureKnowledge
StoreKnowledge
22
3
Rafi A Khan5/12/2010 10:21:05 PM Rafi A Khan
Cyclic Model of KM
ManageKnowledge
RefineKnowledge
Create Knowledge
CaptureKnowledge
StoreKnowledge
22
4
Rafi A Khan5/12/2010 10:21:05 PM Rafi A Khan
Cyclic Model of KM
ManageKnowledge
RefineKnowledge
Create Knowledge
CaptureKnowledge
StoreKnowledge
22
5
Rafi A Khan5/12/2010 10:21:05 PM Rafi A Khan
Cyclic Model of KM
ManageKnowledge
RefineKnowledge
Create Knowledge
CaptureKnowledge
StoreKnowledge
DisseminateKnowledge
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Cyclic Model of KM
ManageKnowledge
RefineKnowledge
Create Knowledge
CaptureKnowledge
StoreKnowledge
DisseminateKnowledge
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Why Adopt KM
Cost savings
Better performance
Demonstrated success
Share Best Practices
Competitive Advantage
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KM Methods, Technologies, and Tools
Email or messaging
Document management
Search engines
Enterprise information portal
Data warehouse
Groupware
Workflow management
Web-based training
Others
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Knowledge Acquisition Techniques
The Business School
University of Kashmir
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Knowledge Acquisition
The following are main methods of knowledge acquisition :
• Production Rule
• Frames
• Semantic Network
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Production Rules
IF-THEN
Independent part, combined with other pieces, to produce
better result
Model of human behavior
Examples
– IF condition, THEN conclusion
– Conclusion, IF condition
– If condition, THEN conclusion1 (OR) ELSE conclusion2
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Frames
Organized structure of knowledge
Put related knowledge in one area called frame
A frame consists of slots representing a part of
knowledge
Each slot has a value in the form of data,
information, process and rules
Frame can be related to other frames
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Frames(Engine Overheating)
Slot : Symptoms Value
Temp More than 80 deg
Water Boiling
Speed Retardation
Slot : Inspection
Value
Check Water Level
Oil in Engine
Carburetor
Slot : Treatment Value
Stop Engine & Drain
Water
Start Engine & pour cold Water
Increase oil level
Adjust Carburetor
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Semantic Networks
Graphical
depictions
Nodes and links
connecting nodes
Node represents an
Entity & link
represents
Association
Hierarchical
relationships
between concepts
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Inferencing
Inferencing means deriving a conclusion based on statements
that only imply that conclusion.
Every rule in knowledge base can be checked to see whether
its premise (principle) or conclusion can be satisfied by
previously made assertions.
This process can be done in two directions :
–Forward
–Backward
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Inference Techniques
Forward Chaining
Forward chaining is a data-driven approach . We start from available
information as it becomes available or from a basic idea, and then we try to
draw conclusions.
Backward chaining
Backward chaining is a goal-driven approach in which you start from an
expectation of what is going to happen (hypothesis) and then seek evidence
that supports (or contradicts) your expectation.
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Example
Investment Decision : Whether to invest in IBM Stocks
The following variables are used:
– A= Have Rs.10,000
– B= Younger than 30
– C= Education at college level
– D= Annual income of atleast Rs.40,000
– E= Invest in securities
– F= Invest in growth stocks
– G= Invest in IBM stock (the potential goal)
The facts: we assume that an investor has Rs.10,000(that A is true) and
that she is 25 years old (B is true). She would like advice on
investing in IBM stock(yes or no for the goal).
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Example
The Rules:Our knowledge base includes the following five rules:
R1: IF a person has Rs10,000 to invest and has a college degree THEN she should invest in securities
R2: IF a persons annual income is atleast Rs40,000 to invest and has a college
degree THEN she should invest in growth stocks
R3: IF a person is < 30 and is investing in securities THEN she should invest in
growth stocks
R4: IF a person is < 30 and >22 THEN she has a college degree
R5:IF a person wants to invest in growth stocks then the stock should be IBM
– R1: IF A and C, THEN E
– R2: IF D and C,THEN F
– R3: IF B and E, THEN F
– R4: IF B, THEN C
– R5: IF F, THEN G
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Forward Chaining
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Backward Chaining
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The End
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Experts
Experts
– Have special knowledge, judgment, and experience
– Can apply these to solve problems
• Higher performance level than average person
• Faster Solutions
• Recognize Patterns
Expertise
– Task specific knowledge of experts
• Acquired from reading, training, practice
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Expert System
Expert Systems: a computer application that employs a set of rules based
on human knowledge to solve problems that require human expertise
Information systems that solve problems by capturing knowledge for a
very specific and limited domain of human expertise are called expert
systems
– For example, diagnosing a cars ignition system, classifying biological
specimen
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Common Expert System Architecture
Knowledge Base
User
Interface
Inference
Engine
User Environment
User
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KBES
Knowledge based expert system (KBES) has three basic
components:
• Knowledge base
• User control mechanism
• Inference Mechanism
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User Interface
Design of the UI focuses on human concerns such as ease of use,
reliability and reduction of fatigue
Design should allow for a variety of methods of interaction
(input, control and query)
Mechanisms include touch screen, keypad, light pens, voice
command, hot keys
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Knowledge Base
Contains the domain-specific knowledge acquired from the
domain experts
Can consist of all the theoretical foundations, facts, judgments,
rules, formulas, intuitions and experience
The success of an ES relies on the completeness and accuracy of
its knowledge base
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Inference Engine
Here, the knowledge is put to use to produce solutions
Interprets the knowledge available and performs logical
deductions in a given situation.
It is a strategy used to search through rule base
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Applications of Expert Systems
DENDRAL project
– Applied knowledge or rule-based reasoning commands
– Deduced likely molecular structure of compounds
MYCIN
– Rule-based system for diagnosing bacterial infections
XCON
– Rule-based system to determine optimal systems configuration
Credit analysis
– Ruled-based systems for commercial lenders
Pension fund adviser
– Knowledge-based system analyzing impact of regulation
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Applications
Finance
– Insurance evaluation, credit analysis, tax planning, financial planning and reporting, performance evaluation
Data processing
– Systems planning, equipment maintenance, vendor evaluation, network management
Marketing
– Customer-relationship management, market analysis, product planning
Human resources
– HR planning, performance evaluation, scheduling, pension management, legal advising
Manufacturing
– Production planning, quality management, product design, plant site selection, equipment maintenance and repair
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Benefits of Expert Systems
Increased outputs
Increased productivity
Decreased decision-making time
Increased process and product quality
Reduced downtime
Capture of scarce expertise
Flexibility
Ease of complex equipment operation
Elimination of expensive monitoring equipment
Operation in hazardous environments
Access to knowledge and help desks
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Benefits of Expert Systems
Ability to work with incomplete, imprecise, uncertain data
Provides training
Enhanced problem solving and decision-making
Rapid feedback
Facilitate communications
Reliable decision quality
Ability to solve complex problems
Ease of knowledge transfer to remote locations
Provides intelligent capabilities to other information
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Limitations
Knowledge not always readily available
Difficult to extract expertise from humans
Lack of end-user trust
Knowledge subject to biases
Systems may not be able to arrive at conclusions
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The End
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5/12/2010
The Data Warehouse
Department of Management Studies
University of Kashmir
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Objective
How operational data and decision support data differ
What a data warehouse is, how data for it are prepared, and how it is
implemented
What data mining is and what role it plays in decision support
What online analytical processing (OLAP) is
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The Need for Data Analysis
Managers must be able to track daily transactions to evaluate how the
business is performing
By tapping into operational database, management can develop strategies
to meet organizational goals
Data analysis can provide information about short-term tactical
evaluations and strategies
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Operational Data
– Mostly stored in relational database
– Optimized to support transactions representing daily operations
DSS Data
– Give tactical and strategic business meaning to operational data
– Differs from operational data in following three main areas:
• Time span
• Granularity
• Dimensionality
Operational Data vs. Decision Support Data
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Operational Data vs. Decision Support Data
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Operational Data vs. Decision Support Data
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The Data Warehouse
Integrated, subject-oriented, time-variant, nonvolatile collection of data
that provides support for decision making
Usually a read-only database optimized for data analysis and query
processing
Requires time, money, and considerable managerial effort to create
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Characteristics of a Data Warehouse
• Subject orientation: data is organized based on how the users refer to it.
• Integrated: all inconsistencies regarding naming convention and value
representations are removed.
• Non-volatile: data is stored in read-only format and do not change over
time.
• Time Variant: data are not current but normally time-series.
• Summarized: operational data are mapped into a decision-usable
format.
• Large Volume: time-series datasets are normally quite large.
• Not Normalized: DW data can, and often is, redundant.
• Metadata: data about data is stored.
• Data Sources: internal and external unintegrated operational systems.
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The Data Warehouse (continued)
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The Data Warehouse (continued)
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OLAP tools
Data Mining Tools
Ad-hoc Queries
Reporting Tools
Monitoring/
Operational
Database(s)
Data
Warehouse
Independent
Data Mart
External
Data
ETL Routine(Extract/Transform/Load)
Dependent
Data Mart
Extract/Summarize Data
Fig. Data warehouse process model
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Data Warehousing Benefits
Increase in knowledge worker productivity
Supports all decision makers‘ data requirements
Provide ready access to critical data
Insulates operation databases from adhoc processing
Provides high-level summary information
Provides drill down capabilities
Yields
– Improved business knowledge
– Competitive advantage
– Enhances customer service and satisfaction
– Facilitates decision making
– Help streamline business processes
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The Data Mart
Data mart
– Small, single-subject data warehouse subset
– Each is more manageable data set than data warehouse
– Provides decision support to small group of people
– Typically lower cost and lower implementation time than data
warehouse
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Twelve Rules that Define a Data Warehouse
Data warehouse and operational environments are separated
Data warehouse data are integrated
Data warehouse contains historical data over long time horizon
Data warehouse data are snapshot data captured at given point in time
Data warehouse data are subject oriented
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Twelve Rules that Define a Data Warehouse (continued)
Data warehouse data are mainly read-only with periodic batch updates
from operational data
– No online updates allowed
Data warehouse development life cycle differs from classical systems
development
Data warehouse contains data with several levels of detail: current detail
data, old detail data, lightly summarized data, and highly summarized
data
Data warehouse environment is characterized by read-only transactions to
very large data sets
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Twelve Rules that Define a Data Warehouse (continued)
Data warehouse environment has system that traces data sources,
transformations, and storage
Data warehouse‘s metadata are critical component of this environment
Data warehouse contains a mechanism for resource usage that enforces
optimal use of data by end users
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OLAP Activities
Generating queries
Requesting ad hoc reports
Conducting statistical and other analyses
Developing multimedia applications
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Online Analytical Processing
Advanced data analysis environment that supports decision making,
business modeling, and operations research
OLAP systems share four main characteristics:
– Use multidimensional data analysis techniques
– Provide advanced database support
– Provide easy-to-use end-user interfaces
– Support client/server architecture
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Multidimensional Data Analysis Techniques
Data are processed and viewed as part of a multidimensional structure
Particularly attractive to business decision makers
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OLTP vs OLAP
• Time-critical
• In-place data update
• Current data
• Functional transaction focus
• Store details only
• Only keeps company internal data
• Small delays tolerable
• Append only
• Historical and current data
• Reporting (information
delivery) focus
• Store summary + details
(e.g. counts and aggregates)
• Warehouse also keeps external data
On-Line Transaction Processing On-Line Analytical Processing
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Multidimensional Data Analysis Techniques
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Multidimensional Data Analysis Techniques (continued)
27
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OLAP Architecture
27
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OLAP Architecture (continued)
27
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OLAP Architecture (continued)
Designed to use both operational and data warehouse data
Defined as an ―advanced data analysis environment that supports decision making, business modeling, and an operation‘s research activities‖
In most implementations, data warehouse and OLAP are interrelated and complementary environments
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Multi Dimensional Data
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Multi Dimensional Data
28
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Drill Down & Roll Up
28
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Drill Down & Roll Up
28
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Fresh Produce
Tinned Food
Toiletries
60
30
50
82
84
15
63
79
46
59
64
73
August
July
Sept
28
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Fresh Produce
Tinned Food
Toiletries
60
30
50
82
84
15
63
79
46
59
64
73
August
July
Sept
28
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Fruit
Vegetables
Dairy
15
15
August
July
Fresh Produce
Tinned Food
Toiletries
60
30
50
82
84
15
63
79
46
59
64
73
Fresh ProduceSept
30
August
July
Sept
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Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan
Fruit
Vegetables
Dairy
15
15
August
July
Fresh Produce
Tinned Food
Toiletries
60
30
50
82
84
15
63
79
46
59
64
73
Fresh Produce
30
Sept
August
July
Sept
28
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Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan
Fruit
Vegetables
Dairy
15
15
August
July
Fresh Produce
Tinned Food
Toiletries
60
30
50
82
84
15
63
79
46
59
64
73
Fresh Produce
30
Sept
August
July
Sept
28
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Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan
Fruit
Vegetables
Dairy
15
15
August
July
Fresh Produce
Tinned Food
Toiletries
60
30
50
82
84
15
63
79
46
59
64
73
Fresh Produce
2
3
2
4
3
1
1
4
3
1
2
4
30
Sept
10-20 sept21-30 sept
1st-10 sept
August
July
Sept
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Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan
Fruit
Vegetables
Dairy
15
15
August
July
Fresh Produce
Tinned Food
Toiletries
60
30
50
82
84
15
63
79
46
59
64
73
Fresh Produce
2
3
2
4
3
1
1
4
3
1
2
4
30
Sept
10-20 sept21-30 sept
1st-10 sept
Apples
Mangoes
Oranges
Fruits
August
July
Sept
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Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan
Some Tools in the Marketplace
• Data Warehousing
• Microsoft SQL Server 2000 Data Transformation Service
• Oracle 9i Warehouse Builder
• IBM Red Brick Warehouse, and DB2
• NCR/Teradata
• SAS Data Warehousing (Warehouse Administrator)
• OLAP
• Cognos PowerPlay
• Business Objects Analytics• Microstrategy 7i
• Microsoft SQL Server 2000 Analysis Service
+ MDX query language for decision support
+ Microsoft Data Analyzer
• Oracle 9i OLAP
Data Warehouse, OLAP, and Data Mining solutions are sometimes listed
under the title ‗Business Intelligence‘ (BI) software.
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Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan
Knowledge Discovery in Databases (KDD)
• Knowledge Discovery in Databases (KDD) is the automated discovery
of patterns and relationships in large databases.
• Knowledge Discovery in Databases (KDD) as it is also known, is the
nontrivial extraction of implicit, previously unknown, and potentially
useful information from data.
• KDD is the search for relationships and global patterns that exist in
large databases but are ̀ hidden' among the vast amount of data, such
as a relationship between patient data and their medical diagnosis.
These relationships represent valuable knowledge about the database
and the objects in the database
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Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan
Knowledge Discovery in Databases (KDD)
Selection:
Extraction of the data from a database that is relevant to the data mining analysis.
Preprocessing:
Ensuring that values have a uniform meaning, eliminating missing values in the data, and inaccurate (inconsistent) data.
Data Transformation:
Converting the data into a two-dimensional table and eliminating unwanted fields so the results are valid.
Data mining:
The extraction of patterns from the data using by an appropriate set of algorithms.
Interpretation:
The transformation of the identified patterns into knowledge
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Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan
KDD PROCESS
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Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan
Trends leading to Data Flood
More data is generated:
– Bank, telecom, other business
transactions ...
– Scientific data: astronomy,
biology, etc
– Web, text, and e-commerce
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Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan
Big Data Examples
Europe's Very Long Baseline Interferometry (VLBI) has 16 telescopes, each
of which produces 1 Gigabit/second of astronomical data over a 25-day
observation session
– storage and analysis a big problem
AT&T handles billions of calls per day
– so much data, it cannot be all stored -- analysis has to be done ―on the
fly‖, on streaming data
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Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan
Largest databases in 2003
Commercial databases:
– Winter Corp. 2003 Survey: France Telecom has largest decision-support
DB, ~30TB; AT&T ~ 26 TB
Web
– Alexa internet archive: 7 years of data, 500 TB
– Google searches 4+ Billion pages, many hundreds TB
– IBM WebFountain, 160 TB (2003)
– Internet Archive (www.archive.org),~ 300 TB
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Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan
From terabytes to exabytes to …
UC Berkeley 2003 estimate: 5 exabytes (5 million terabytes) of new data
was created in 2002.
www.sims.berkeley.edu/research/projects/how-much-info-2003/
US produces ~40% of new stored data worldwide
2006 estimate: 161 exabytes (IDC study)
– www.usatoday.com/tech/news/2007-03-05-data_N.htm
2010 projection: 988 exabytes
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Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan
Largest Databases in 2005
Winter Corp. 2005 Commercial Database
Survey:
1. Max Planck Inst. for Meteorology ,
222 TB
2. Yahoo ~ 100 TB (Largest Data
Warehouse)
3. AT&T ~ 94 TB www.wintercorp.com/VLDB/2005_TopTen_Survey/TopTenWinners_2005.asp
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Data Growth
In 2 years, the size of the largest database TRIPLED!
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Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan
Data Growth Rate
Twice as much information was created in 2002 as in 1999 (~30% growth
rate)
Other growth rate estimates even higher
Very little data will ever be looked at by a human
Knowledge Discovery is NEEDED to make sense and use of data.
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Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan
Machine Learning / Data Mining Application areas
Science
– astronomy, bioinformatics, drug discovery, …
Business
– CRM (Customer Relationship management), fraud detection, e-commerce,
manufacturing, sports/entertainment, telecom, targeted marketing, health care,
…
Web:
– search engines, advertising, web and text mining, …
Government
– surveillance, crime detection, profiling tax cheaters, …
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Application Areas
What do you think are some of the most important and
widespread business applications of Data Mining?
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Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan
Data Mining for Customer Modeling
Customer Tasks:
– attrition prediction
– targeted marketing:
• cross-sell, customer acquisition
– credit-risk
– fraud detection
Industries
– banking, telecom, retail sales, …
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Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan
Customer Attrition: Case Study
Situation: Attrition rate at for mobile phone customers is around 25-30% a year!
With this in mind, what is our task?
– Assume we have customer information for the past N months.
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Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan
Customer Attrition: Case Study
Task:
Predict who is likely to attrite next month.
Estimate customer value and what is the cost-effective offer to
be made to this customer.
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Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan
Customer Attrition Results
Verizon Wireless built a customer data warehouse
Identified potential attires
Developed multiple, regional models
Targeted customers with high propensity to accept the offer
Reduced attrition rate from over 2%/month to under 1.5%/month (huge impact, with >30 M subscribers)
(Reported in 2003)
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Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan
Assessing Credit Risk: Case Study
Situation: Person applies for a loan
Task: Should a bank approve the loan?
Note: People who have the best credit don‘t need the loans, and people
with worst credit are not likely to repay. Bank‘s best customers are in the
middle
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Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan
Credit Risk - Results
Banks develop credit models using variety of machine learning methods.
Mortgage and credit card proliferation are the results of being able to
successfully predict if a person is likely to default on a loan
Widely deployed in many countries
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Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan
e-commerce
A person buys a book (product) at Amazon.com
What is the task?
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Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan
Successful e-commerce – Case Study
Task: Recommend other books (products) this person is likely to buy
Amazon does clustering based on books bought:
– customers who bought ―Advances in Knowledge Discovery and Data
Mining‖, also bought ―Data Mining: Practical Machine Learning
Tools and Techniques with Java Implementations‖
Recommendation program is quite successful
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Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan
Security and Fraud Detection - Case Study
Credit Card Fraud Detection
Detection of Money laundering
– FAIS (US Treasury)
Securities Fraud
– NASDAQ KDD system
Phone fraud
– AT&T, Bell Atlantic, British Telecom/MCI
Bio-terrorism detection at Salt Lake Olympics 2002
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Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan
Data Mining and Privacy
in 2006, NSA (National Security Agency) was reported to be mining years
of call info, to identify terrorism networks
Social network analysis has a potential to find networks
Invasion of privacy – do you mind if your call information is in a gov
database?
What if NSA program finds one real suspect for 1,000 false leads ?
1,000,000 false leads?
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Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan
Related Fields
Statistics
MachineLearning
Databases
Visualization
Data Mining and
Knowledge Discovery
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Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan
Statistics, Machine Learning andData Mining
Statistics:
– more theory-based
– more focused on testing hypotheses
Machine learning
– more heuristic(experience-based techniques that help in problem solving, learning and discovery)
– focused on improving performance of a learning agent
– also looks at real-time learning and robotics – areas not part of data mining
Data Mining and Knowledge Discovery
– integrates theory and heuristics
– focus on the entire process of knowledge discovery, including data cleaning, learning, and integration and visualization of results
Distinctions are fuzzy
witten&eibe
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Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan
Data mining
Many Definitions…
– A short one…
Search for Valuable Information in Large Volumes of Data.
– A long one…
Exploration & Analysis, by Automatic or Semi-Automatic Means,
of Large Quantities of Data in order to Discover Meaningful
Patterns & Rules.
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Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan
Data Mining
Data Mining is the step in the process of knowledge discovery in
databases, that inputs predominantly cleaned, transformed data, searches
the data using algorithms, and outputs patterns and relationships to the
interpretation/evaluation step of the KDD
Data mining is a process that uses a variety of data analysis tools to
discover patterns and relationships in data that may be used to make valid
predictions.
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Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan
Data Mining
Data Mining constitutes one step in the KDD process.
The transformed data is used in the data mining step. It is in this step that
the actual search for patterns of interest is performed.
The appropriate data mining algorithm (linear/logistic regression, neural
networks, association rules, etc.) for the data mining task (classification,
database segmentation, rule generation, etc) are applied.
It is necessary to remove redundant and irrelevant patterns from the set of
useful patterns. Once a set of good patterns have been discovered, they
then have to be reported to the end user. This can be done can be done
textually, by way of reports or using visualizations such as graphs,
spreadsheets, diagrams, etc.
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Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan
Data Mining
Data mining tools do the following:
– Analyze data
– Uncover problems or opportunities hidden in data relationships
– Form computer models based on their findings
– Use models to predict business behavior
Require minimal end-user intervention
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Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan
Data Mining (continued)
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Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan
Major Data Mining Tasks
Classification: predicting an item class
Clustering: finding clusters in data
Associations: e.g. A & B & C occur frequently
Visualization: to facilitate human discovery
Summarization: describing a group
Deviation Detection: finding changes
Estimation: predicting a continuous value
Link Analysis: finding relationships
…
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Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan
Data Mining Tasks: Classification
Learn a method for predicting the instance class from pre-labeled (classified) instances
Many approaches:
Statistics,
Decision Trees,
Neural Networks,
...
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Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan
Data Mining Tasks: Clustering
Find “natural” grouping of instances given un-labeled data
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Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan
Data Mining (continued)
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Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan
We want to know ...
Given a database of 100,000 names, which persons are the least likely to
default on their credit cards?
Which types of transactions are likely to be fraudulent given the
demographics and transactional history of a particular customer?
If I raise the price of my product by Rs. 2, what is the effect on my ROI?
If I offer only 2,500 airline miles as an incentive to purchase rather than
5,000, how many lost responses will result?
If I emphasize ease-of-use of the product as opposed to its technical
capabilities, what will be the net effect on my revenues?
Which of my customers are likely to be the most loyal? Data Mining helps extract such information
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Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan327
Major Data Mining Characteristics and Objectives
Data are often buried deep
Client/server architecture
Sophisticated new tools--including advanced visualization tools
End-user miner empowered by data drills and other power query tools with
little or no programming skills
Often involves finding unexpected results
Tools are easily combined with spreadsheets, etc.
Parallel processing for data mining
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Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan
Difference between OLAP & Data Minig
OLAP is part of the spectrum of decision support tools. Traditional query and
report tools describe what is in a database. OLAP goes further; it‘s used to
answer why certain things are true. The user forms a hypothesis about a
relationship and verifies it with a series of queries against the data. For
example, an analyst might want to determine the factors that lead to loan
defaults. He or she might initially hypothesize that people with low
incomes are bad credit risks and analyze the database with OLAP to verify
(or disprove) this assumption. If that hypothesis were not borne out by the
data, the analyst might then look at high debt as the determinant of risk. If
the data did not support this guess either, he or she might then try debt
and income together as the best predictor of bad credit risks.
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Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan
OLAP analysis is essentially a deductive process.
The OLAP analyst generates a series of hypothetical patterns and
relationships and uses queries against the database to verify them or disprove
them.
It becomes much more difficult and time-consuming to find a good
hypothesis, when the number of variables being analyzed is in the dozens or
even hundreds? and analyze the database with OLAP to verify or disprove it.
Difference between OLAP & Dataminig
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Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan
Data mining , rather than verify hypothetical patterns, it uses the data itself to
uncover such patterns. It is essentially an inductive process.
For example, suppose the analyst who wanted to identify the risk factors for
loan default were to use a data mining tool. The data mining tool might
discover that people with high debt and low incomes were bad credit risks (as
above), but it might go further and also discover a pattern the analyst did not
think to try, such as that age is also a determinant of risk.
Difference between OLAP & Dataminig
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Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan
Data mining Applications
Many organizations are using data mining to help manage all phases of the
customer life cycle, including acquiring new customers, increasing revenue
from existing customers, and retaining good customers.
Telecommunications and credit card companies are two of the leaders in
applying data mining to detect fraudulent use of their services.
Insurance companies and stock exchanges are also interested in applying
this technology to reduce fraud.
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Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan
Retail/Marketing
Identify buying patterns from customers
Find associations among customer demographic characteristics
Predict response to mailing campaigns
Market basket analysis
Retailers are making more use of data mining to decide which products to
stock in particular stores (and even how to place them within a store), as
well as to assess the effectiveness of promotions and coupons.
Data mining Applications
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Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan
Medicine
Characterise patient behaviour to predict office visits
Identify successful medical therapies for different illnesses
Medical applications are another fruitful area: data mining can be used to
predict the effectiveness of surgical procedures, medical tests or
medications.
Pharmaceutical firms are mining large databases of chemical compounds
and of genetic material to discover substances that might be candidates for
development as agents for the treatments of disease.
Data mining Applications
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Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan
Banking
Detect patterns of fraudulent credit card use
Identify `loyal' customers
Predict customers likely to change their credit card affiliation
Determine credit card spending by customer groups
Find hidden correlations between different financial indicators
Identify stock trading rules from historical market data
Companies active in the financial markets use data mining to determine
market and industry characteristics as well as to predict individual
company and stock performance.
Data mining Applications
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Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan
Insurance and Health Care
Claims analysis - i.e which medical procedures are claimed together
Predict which customers will buy new policies
Identify behaviour patterns of risky customers
Identify fraudulent behaviour
Transportation
Determine the distribution schedules among outlets
Analyse loading patterns
Analyse loading patterns
Data mining Applications
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Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan
Data Mining (continued)
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Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan
Data Mining (continued)
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Rafi A KhanFriday, July 18, 2008 Rafi A. Khan
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Rafi A KhanFriday, July 18, 2008 Rafi A. Khan
Systems Development Life Cycle
Four phases
– Planning
– Analysis
– Design
– Implementation
Cyclical
Can return to other phases
Waterfall model
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Rafi A KhanFriday, July 18, 2008 Rafi A. Khan
Tools
Computer-aided software design tools
– CASE tools- Oracle 9i developer suite, Rational rose, Paradigm Plus
RAD design tools- Sybase power Designer. Oracle Internet Development Suite, Rational RequisitePro
Code debugging methods -
Testing and quality assurance tools - Red Views WebLoad, Load Runner, Rational RequisitePro, SilkPerformer
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Rafi A KhanFriday, July 18, 2008 Rafi A. Khan
Successful Project Management
Define requirements
Manage change
Get support from upper management
Establish timelines, milestones, and budgets based on realistic
goals
Involve users
Document everything
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Rafi A KhanFriday, July 18, 2008 Rafi A. Khan
Implementation Failures
Lack of stakeholder involvement
Incomplete requirements
Unrealistic expectations
Project champion leaves
Lack of skill or expertise
Inadequate human resources
New technologies
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Rafi A KhanFriday, July 18, 2008 Rafi A. Khan
Project Management Tools
Project management software can allow:
– Collaboration among disparate teams
– Resource and program management
– Portfolio management
– Web enabled
– Analyses of project data
S/W Examples : Microsoft project, PlanView, ActiveProject
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Rafi A KhanFriday, July 18, 2008 Rafi A. Khan
Alternative Development Methodologies
Parallel Development
– Multiple development on separate systems (Design & Implementation Phases)
– Database, Model base, UI and Knowledge can be developed in parallel
RAD
– Quick development allowing fast, but limited functionality
– Methods of RAD
• Phased development
– Sequential serial development (Break system into Pieces)
• Prototyping ( Analysis, Design & Implementation repeatedly)
– Rapid development of portions of projects for user input and modification
– Small working model or may become functional part of final system
• Throwaway prototyping
– Pilot test or simple development platforms
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Rafi A KhanFriday, July 18, 2008 Rafi A. Khan
34
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Rafi A KhanFriday, July 18, 2008 Rafi A. Khan
34
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Rafi A KhanFriday, July 18, 2008 Rafi A. Khan
Tools
Computer-aided software design tools
– CASE tools - Oracle 9i developer suite, Rational rose, Paradigm Plus
RAD design tools- Sybase power Designer. Oracle Internet Development
Suite, Rational RequisitePro
Code debugging methods -
Testing and quality assurance tools - Red Views WebLoad, Load Runner,
Rational RequisitePro, SilkPerformer
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Rafi A KhanFriday, July 18, 2008 Rafi A. Khan
DSS Prototyping
Short steps
– Planning
– Analysis
– Design
– Prototype
Immediate user feedback
Iterative
– In development of prototype
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Rafi A KhanFriday, July 18, 2008 Rafi A. Khan
Successful Project Management
Define requirements
Manage change
Get support from upper management
Establish timelines, milestones, and budgets based on realistic
goals
Involve users
Document everything
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Rafi A KhanFriday, July 18, 2008 Rafi A. Khan
Implementation Failures
Lack of stakeholder involvement
Incomplete requirements
Unrealistic expectations
Project champion leaves
Lack of skill or expertise
Inadequate human resources
New technologies
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Rafi A KhanFriday, July 18, 2008 Rafi A. Khan
Project Management Tools
Project management software can allow:
– Collaboration among disparate teams
– Resource and program management
– Portfolio management
– Web enabled
– Analyses of project data
S/W Examples : Microsoft project, PlanView, ActiveProject
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Rafi A KhanFriday, July 18, 2008 Rafi A. Khan
Agile Development
Rapid prototyping used for rapidly changing requirements
Used for:
– Unclear or rapidly changing requirements
– Speedy development
Heavy user input
Incremental delivery with short time frames
Tend to have integration problems
Example : Extreme Programming (XP), Scrum, Crystal.
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Rafi A KhanFriday, July 18, 2008 Rafi A. Khan
DSS Prototyping
Advantages
– User and management involvement
– Short user-reaction time(Feedback from user)
– Short intervals between iterations
– Low cost & Short development time
– Improved user understanding of system
Disadvantages
– Changing requirements
– May not have thorough understanding of benefits and costs
– Poorly tested
– Dependencies, security, and safety may be ignored
– High uncertainty
– Problem may get lost
– Reduction in quality
– Higher costs due to multiple productions
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Rafi A KhanFriday, July 18, 2008 Rafi A. Khan
Change Management
Crucial to DSS
People resistant to change
Examine cause of change
May require organizational culture shift
Lewin-Schein change theory steps
– Unfreeze
• Create awareness of need for change
• People support what they help create
– Move
• Develop new methods and behaviors
• Create and maintain momentum
– Refreeze
• Reinforce desired changes
• Establish stable environment
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Rafi A KhanFriday, July 18, 2008 Rafi A. Khan
DSS Technology Levels
DSS primary tools
–Fundamental elements
•Programming languages, graphics, editors, query systems
DSS generator (engine)
–Integrated software package for building specific DSS
•Modeling, report generation, graphics, risk analysis
•These range from spreadsheets such as Excel—perhaps with some add-ins or a more sophisticated generator such as MicroStrategy‘s DSS Architect.
Specific DSS
–For some problem types there may be a commercially available package that can be acquired and customized
DSS primary tools are used to construct integrated tools that are used to construct specific tools
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DSS
Hardware
– PCs to multiprocessor mainframes
Software
– Involves multiple criteria
– Develop in house, outsource, or buy off the shelf
– Off the shelf software rapidly updated; many on market
– Prices fluctuate
– Different tools available
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Rafi A KhanFriday, July 18, 2008 Rafi A. Khan
DSS Team developed DSS requires substantial effort to build and
manage
End user developed DSS
– Decision-makers and knowledge workers develop to solve problems or enhance productivity
• Advantages
– Short delivery time
– User requirements specifications are eliminated
– Reduced implementation problems
– Low costs
• Risks
– Quality may be low
– May have lack of documentation
– Security risks may increase
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Rafi A KhanFriday, July 18, 2008 Rafi A. Khan
DSS
Microstrategy 8
Hyperion System 9 BI+
Business Object XI
Microsoft Biztalk server2004
IBM Websphere Commerce Suite
Oracle Daily Business Intelligence(DBI)