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CHAPTER 10 DATA, KNOWLEDGE, AND DECISION SUPPORT. Learning Objectives. Describe the concepts of managerial decision making and computerized support decision making Understand the life cycle of processing data into information and knowledge for use in decision support

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learning objectives
Learning Objectives
  • Describe the concepts of managerial decision making and computerized support decision making
  • Understand the life cycle of processing data into information and knowledge for use in decision support
  • Describe the framework for computerized decision support and the concept of decision support systems
  • Describe executive information systems and group support systems, and analyze their roles in management support
  • Describe data presentation methods and explain geographical information systems as a decision support tool
  • Explain the concepts of knowledge management and organizational databases
  • Describe knowledge discovery, online analytical processing, and data mining
chapter overview

Management and Decision Making

Data Management Life Cycle

Decision Support Systems

  • The Manager’s
  • Job
  • Why Managers
  • Need IT
  • Support
  • The Data Life Cycle Process
  • Data Sources and Collection
  • Data Quality
  • Data Storage and
  • Management
  • Document Management
  • Analytical Processing
  • The Decision Support Process
  • Modeling
  • A Framework for Computerized DSS
  • DSS Concepts, Characteristics, and
  • Capabilities
  • Components and Structure of DSS

Enterprise Decision Support

Data Visualisation Technologies

Knowledge Management and Organization Knowledge Bases

Knowledge Discovery and Analysis

  • Executive
  • Information Support
  • Supporting Idea
  • Generation and
  • Creativity
  • Group Decision
  • Support Systems
  • (GDSS)
  • Data
  • Visualization
  • Visual Interactive
  • Decision Making
  • Geographical
  • Information
  • Systems
  • What is knowledge
  • Management?
  • IT Support of
  • Knowledge
  • Management Activities
  • Implementing
  • Knowledge
  • Management
  • The Foundations of
  • Knowledge
  • Discovery
  • Online Analytical
  • Processing
  • Data Mining for
  • Decision Support
  • Ethical and Legal
  • Issues
Chapter Overview
case managing global business at 3m corporation

The company had difficulties responding to new customers and markets in the global

  • economy as each business unit kept separate databases and conducted independent decision support activities
Case: Managing Global Business at 3M Corporation
  • The Business Problem
  • The Solution
  • The Company created a global enterprise data warehouse (GEDW)
  • The Results
  • Distributors and retailers, as well as end-users, now receive information in minutes instead of weeks or months, including the delivery of rich multimedia
  • Customers can find all their information in one place
  • Inventories are lower, and better and quicker inventory decisions are made, even in globally remote locations
case continued
Case (continued…)
  • What have we learned from this case??
  • The case demonstrates the existence of vast amounts of important data in organizations, and the importance of organizing that data for optimum use
  • The case shows the need to share a company’s data internally and with business partners and customers, and to make it available in a format that enables end users to process data quickly
  • The case explains the data warehouse, and its role in supporting managerial decision making
management and decision making
Management andDecision Making
  • Management
    • a process by which certain goals are achieved through the use of resources
  • Managers - make decisions in every step of the process
    • interpersonal roles : figurehead, leader, liaison
    • informational roles : monitor, disseminator, spokesperson
    • decisional roles : entrepreneur, disturbance handler, resource allocator, negotiator
management and decision making continued
Management andDecision Making (continued …)
  • Why Managers Need IT Support
    • processing information manually is growing increasingly difficult
    • computerized modeling
      • examining numerous alternatives very quickly
      • providing a systematic risk analysis
      • being integrated with communication systems and databases
      • being used to support group work
the data management life cycle process

Data Sources (databases)

End Users:

Decision Making and other Tasks;

Data Visualization

Direct Use

Data Organization; Storage

Direct Use

Use of Knowledge


Data Warehouse


Generate Knowledge


Analytical Processing,

Data Mining



Purchased Knowledge

Organizational Knowledge Bases


The Data ManagementLife Cycle Process
the data management life cycle continued
The Data ManagementLife Cycle (continued …)
  • Data Sources and Collection
    • Internal Data -generated by the corporate transaction processing systems, functional user information systems, and other functions and individuals
    • Personal Data -created for IS users or other corporate employees documenting their own expertise
    • External Data -generated outside and organization, but relevant portions of it flow into the organization
    • Methods for Collecting Raw Data
      • manually or by instruments and sensors
      • scanned or transferred electronically
      • in the field; other times from within the organization or from people
the data management life cycle continued1
The Data ManagementLife Cycle (continued …)
  • Data Quality
    • an extremely important issue since quality determines the data’s usefulness as well as the quality of the decisions based on these data
    • need to be : accurate, secure, relevant, timely, complete, and consistent
the data management life cycle continued2
The Data ManagementLife Cycle (continued …)
  • Data Storage
    • databases or in data warehouse and data marts
  • Data Management
    • difficulties in data management
      • exponential increases of data with time
      • data collected by several methods and devices
      • various sources of raw data
      • only small portions are relevant
      • an ever-increasing amount of external data
      • different legal requirements relating to data
      • selecting data management tools - a problem
      • data security, quality, and integrity
the data management life cycle continued3
The Data ManagementLife Cycle (continued …)
  • Document Management
    • Document Management System (DMS)
      • automates the control of electronic documents through their entire life cycle within an organization, from initial creating to final archiving
      • retains an image of an electronic document
      • creates an index of key-words
      • puts the entire document into computer readable format
      • manages (and limits) distribution
    • Functions
      • document identification, storage, and retrieval; tracking version control’ workflow management’ and presentation
the data management life cycle continued4
The Data ManagementLife Cycle (continued …)
  • Analytical Processing - the activity of analyzing accumulated data
    • work directly with the existing operational systems, using software tools and components known as front-end tools
    • work with the data warehouse
decision support systems dss



Intelligence Phase

Verification of the Model

Design Phase

Verification, Testing of Proposed Solution

Choice Phases



of Solution


Decision Support Systems (DSS)
  • The Decision Support Process
dss continued
DSS (continued …)
  • Modeling in DSS
    • Iconic (scale) models
      • aphysical replica of a system, usually based on a different scale form original
    • Analog models
      • a physical model, but the shape of the model differs from that of the actual system
    • Mathematical (qualitative) model
      • models complex relationships and conducts experimentations with them
    • Mental models
      • provide a description of how a person thinks about a situation
dss continued1
DSS (continued …)
  • A Framework for Computerized Decision Support
    • Problem Complexity
      • decision making processes fall along a continuum that ranges from highly structured to highly unstructured decisions
    • Nature of Decisions
      • strategic planning - the long-range goals and policies for resource allocation
      • management control - the acquisition and efficient utilization of resources in the accomplishment of organizational goals
      • operational control - the efficient and effective execution of specific tasks
dss continued2

Type of Control

Operational Control

Managerial Control





Type of Decision


Accounts Receivable Order entry


Budget analysis,

short-term forecasting, personnel reports, make-of-buy analysis



management ,

warehouse location, distribution systems



MS models

F & S models





Production scheduling inventory control

Credit evaluation, Budget Preparation, plan layout, project scheduling, rewarded systems design

Building new plant, mergers and acquisitions, new product planning, compensation planning, quality assurance planning






Selecting a cover for a magazine, buying software approving loans


recruiting and executive,

buying hardware,


R & D planning, new technology development social responsibility planning



Neural Networks





Neural Networks

Support Needed

DSS (continued …)
  • Decision Support Framework
dss continued3
DSS (continued …)
  • Computer Support for Structured decision
    • support the nine cells, especially to the operational and managerial control type
    • lower-level managers encounter on a regular basis typically have a high level of structure
  • Management Science - adopts the view that managers can follow a fairly systematic process for solving problems
    • Define the problem
    • Classify the problem into a standard category
    • Construct a standard mathematical model
    • Find potential solutions
    • Choose and recommend a specific solution
dss continued4
DSS (continued …)
  • Concepts
    • an approach or a philosophy rather than a precise methodology
  • Characteristics and Capabilities
    • support decision makers at all managerial levels
    • support several interdependent and/or sequential decisions
    • support all phases of decision making and variety of decision-making processes and styles
    • can be adopted over time to deal with changing conditions
    • easy to construct
    • utilize models
    • integrate systems
    • execute sensitivity analysis
dss continued5
DSS (continued …)
  • Sensitivity Analysis
    • the study of the effect that changes in one or more parts of a model have on other parts of the model
  • What-if Analysis
    • checks the impact of a change in the assumptions or other input data on the proposed solution
  • Goal-seeking Analysis
    • find the value of the inputs necessary to achieve a desired level of output
dss continued6
DSS (continued …)
  • Components and Structure of DSS
    • Data Management
      • includes the database(s), which contains relevant data for the decision situation
    • User Interface
      • enables the users to communicate with and command the DSS
    • Model Management
      • includes software with financial, statistical, management science, or other quantitative models
    • Knowledge Management
      • supports any of the other subsystems or act as an independent component
enterprise decision support
Enterprise Decision Support
  • Executive Information Support
    • Capabilities of EIS
      • Drill down
      • Critical success factors and key performance indicators
      • Status access
      • Trend analysis
      • Ad hoc analysis
      • Exception reporting
      • Intelligent EIS
      • Integration with DSS
enterprise decision support continues
Enterprise Decision Support (continues …)
  • DSS, EIS, and the Internet
    • The DSS/EIS builder can access Web pages and view data that are related to the DSS project; thus saving time
    • The Web supports interactive DSS-related queries and ad hoc report generation.
    • Users have the capabilities of advanced DSS application without requiring special software
enterprise decision support continues1
Enterprise Decision Support (continues …)
  • Supporting Idea Generation and Creativity
    • Generate ideas or be creative in order to generate alternative solutions for semi structured and unstructured situations
    • Idea-generation software
      • stimulates a single user or a group to produce new ideas, options, and choices
      • encourages and pushes, something like a personal trainer
      • increases the flow of ideas to the user
enterprise decision support continues2
Enterprise Decision Support (continues …)
  • Group Decision Support Systems (GDSS)
    • an interactive computer-based system that facilitates the solution of semi structured and unstructured problems by a group of decision makers
    • supports face-to-face meetings - decision room
    • as well as meetings where members are in different locations
    • to improve the productivity of decision making meetings, either by speeding up the decision-making process or by improving the quality of the resulting decisions, or both
data visualization technologies
Data Visualization Technologies
  • Data Visualization
    • presents data by technologies such as digital images, geographical information systems, graphical user interfaces, multidimensional tables, and graphs, virtual reality, three-dimensional presentations, and animation
    • allows people to spot problems that have existed for years, undetected by standard analysis methods
    • can be integrated among themselves to create a variety of presentations
data visualization technologies continued
Data Visualization Technologies (continued …)
  • Visual Interactive Decision Making
    • Visual interactive modeling (VIM)
      • use computer graphic displays to represent the impact of different management or operational decisions on goals such as profit or market share
      • user can intervene in the decision-making process and see the results of the intervention
    • Visual interactive simulation (VIS)
      • the end-user watches the progress of the simulation model in an animated form using graphics terminals
      • users may interact with the simulation and try different decision strategies
geographical information system gis

Spatial Imaging Function

Design and Planning Function

Database Management Function


Decision Modelling Function

Geographical Information System (GIS)
  • GIS Categories

Surveying and Mapping

Design and Engineering

Facilities Management

Strategic Planning and Decision Making

Demographic and Market Analysis

Transportation and Logistics

gis continued


What the Application Does

PepsiCo, Inc.

Helps select new Taco Bell and Pizza Hut restaurants, by combining demographic data and traffic patterns

CIGNA (health insurance)

Answers such questions as “How many CIGNA-affiliated physicians are within an 8-mile radius of a business?”

Western Auto (a subsidiary of Sears)

Wilkening & Co.

(consulting services)

Designs optimal sales territories and routes for their clients, reducing travel costs by 15 percent

Creates a detailed demographic profile of store’s neighborhood to determine the store’s best product mix

Sears, Roebuck & Co.

Supports planning of truck routes

Health maintenance organizations

Tracks cancer rate to determine clinics’ expansion strategy and allocation of expensive equipment

Wood Personnel Services (employment agency)

CellularOne Corp.

Maps its entire cellular network to identify clusters of call disconnects and to dispatch technician accordingly

Maps neighborhoods where temporary workers live; used for locating marketing and recruiting efforts in cities

GIS (continued …)
  • GIS Applications and Decision Making
gis continued1
GIS (continued …)
  • Emerging GIS Applications
    • help reengineer the aviation, transportation, and shipping industries
    • enables vehicles or aircraft equipped with a GPS receiver to pinpoint their location as they move
    • include railroad car tracking and earth-moving equipment tracking
knowledge management and organizational knowledge bases
Knowledge Management and Organizational Knowledge Bases
  • What is Knowledge Management?
    • Knowledge assets - regarding markets, products, technologies, and organizations that a business owns or needs to own
    • Best practices - collection of the most successful solutions and/or case studies
    • Intellectual capital - collection of knowledge amassed by an organization over the years
    • Knowledge system - collects knowledge, stores it in a database, maintains the database, and disseminates the knowledge to users
    • competitive intelligence - collection of competitive information
it support of knowledge management activities
IT Support of Knowledge Management Activities
  • Knowledge identification - determines what knowledge (information) is critical to decision making
  • Knowledge discovery and analysis - using search engines, databases, and data mining, the proper knowledge must be found, analyzed, and put into proper context
  • Establishment of organizational knowledge bases - it stores organizational knowledge and best practices
  • Knowledge distribution and use - target audiences are defined and technologies are put into place to enable knowledge delivery when needed
implementing knowledge management
Implementing Knowledge Management
  • Reorganize as knowledge-based organizations
  • Created a new position, chief knowledge officer (CKO)
    • crating knowledge management infrastructure
    • build a knowledge culture
    • make it pay off
  • Facilitate organizational learning
    • learn from their experiences in order to survive
knowledge discovery and analysis
Knowledge Discovery and Analysis
  • The Foundations of Knowledge Discovery
    • identify valid, novel, potentially useful data, and understand patterns in data
    • supported by : massive data collection, powerful multiprocessor computers, and data mining algorithms
    • tools : data warehousing and data access, multidimensionality, data mining, massive databases, and online analytical processing
online analytical processing olap
Online Analytical Processing (OLAP)
  • Analysis by end users from their desktop, online
  • Analyze the relationships between many types of business elements
  • Involve aggregated data
  • Compare aggregated data over hierarchical time period
  • present data in different perspectives
  • Involve complex calculations between data elements
  • Respond quickly to users requests
data mining for decision support
Data Mining for Decision Support
  • Data Mining searches for valuable business information in a large database and “mines a mountain for a vein of valuable ore”
  • Two capabilities
    • automated prediction of trends and behaviors
    • automated discovery of previously unknown patterns and relationships
data mining continues
Data Mining (continues ...)
  • Data Mining Tools
    • Neural computing
      • learning approach by which historical data can be examined for patterns by a computer
    • Intelligent agents
      • retrieving information from the Internet or from intranet-based databases
    • Association analysis
      • using a specialized set of algorithms that sort through large data sets and expresses statistical rules among items
data mining continues1
Data Mining (continues ...)
  • Ethical Issues
    • prohibited valuable data-mined information
    • accountability for incorrect decisions
    • human judgment
  • Legal Issues
    • discrimination of age or gender for promotions
    • data security from external snooping or sabotage
    • data ownership
what s in it for me
What’s in IT for Me?
  • For Accounting
    • play major role in the justification of the creation of a knowledge base and in its auditing
  • For Finance
    • responsible for justifying major investments
    • can be helped by most DSS applications in financial management and analysis
  • For Marketing
    • use the organization’s knowledge base and will participate in its creation
what s in it for me continued
What’s in IT for Me? (continued …)
  • For Production/Operations Management
    • can get help in scheduling, logistics, maintenance
  • For Human Resources Management
    • use the knowledge base to find out how past cases were handled
    • resolve the issue of compensating employees for contributing their knowledge