Managing the Digital Firm_6_

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					Data and Knowledge Management
Data Management

Data management comprises all the disciplines related
to managing data as a valuable resource.






Data are organized in a hierarchy
  Traditional approach to data management

Maintaining separate data files for each application.
For example, an employee file would be maintained
for payroll purposes

One or more data files are created for each application.

Duplicated files results in data redundancy.

The problem with data redundancy is the possibility that
updates are accomplished in one file but not in another,
resulting in a lack of data integrity.
A critical success factor: IT applications cannot be
done without using data.

The Difficulties of managing Data:

The amount of data increases exponentially with time

Data are scattered throughout organization and are
collected by many individuals using several methods and

An ever- increasing amount of external data needs to be
considered in making organizational decisions.

Data security, quality, and integrity are critical, yet are
easily jeopardized.
Data Sources

Internal Data Sources: data about people, products,
services, and processes.

Personal Data: IS users or other corporate employees may
document their own expertise by creating personal data.

External Data Sources: Data from commercial databases,
sensors, satellites, etc.

Areas that are considered within data management

To overcome the potential problems with traditional data
management, the database approach was developed.
Data Warehousing

A data warehouse (DW) is a subject-oriented, integrated,
time-variant and non-volatile collection of data intended to
support management decision making.

Data Mining

Data mining is the process of extracting valid, previously
unknown, comprehensible, and actionable information
from large databases and using it to make crucial
business decisions.
Data Modelling

Data modelling is a way of creating a structure for data
that one intends to collect.
 Data Movement

 Data movement is the ability to move data from one place
 to another.

 Database Administration

 To manage the database environment.

  Knowledge Management

A simple collection of data does not represent information,
and equally, a simple collection of information cannot be
considered as knowledge.
Knowledge management is first and foremost a
management discipline that treats intellectual capital as a
managed asset.

It also refers to an organization’s strategic efforts to gain a
competitive advantage by capturing and using the
intellectual assets held by its employees and customers.

Capturing and flowing of an organization's data,
information, and knowledge is delivered to individuals and
groups engaged in accomplishing specific tasks.

The primary goal of knowledge management is to deliver
the intellectual capacity of the firm to the knowledge
workers who make the day-to-day decisions that in
aggregate determine the success or failure of a business.
Knowledge management is not about creating a central

Knowledge management is about embracing a diversity of
knowledge sources

Web sites

and support that knowledge where it resides, while
capturing its context and giving it greater meaning through
its relation to other information in the company.
Approaches to knowledge management

knowledge creation

knowledge storage and retrieval

knowledge transfer

knowledge application

Transfer: Two parties must cooperate in order for
knowledge transfer to occur. The knowledge creator (or
possessor) must be willing and able to share the
knowledge. The knowledge consumer must be willing to
apply the shared knowledge.
Knowledge Management (KM) Conceptual Model
Importance of Knowledge Management

o   Identify information needs
o   Access, store, and retrieve data and information
o   Use search engines and other tools to retrieve information
o   Accumulate knowledge and share with others
o   Harvest knowledge to make better decisions
o   Improving organizational performance
o   Sharing of information and best practices
o   Strategic – Focus on Excellence and Achieving Vision
o   Operational – Focus on Accomplishing the Mission
o   Tactical – Focus on Effective and Efficient Management of
Knowledge Management Is Not:

•   Data Processing
•   Information Management
•   Information Technology
•   E-Learning
•   E-Business

Knowledge Management Is:

• A Management Discipline
• An Enabler for Decision Making, Problem Solving, and
  Continuous Improvement
• The Key to Organizational Management and
  Performance Excellence
Knowledge Management is the totality of activities that an
organization brings to bear to provide:

The right data / information / knowledge:

– to the right persons
– at the right time and place
– in the right quantity and quality

For the purpose of enhancing:

– decision making and
– problem solving
In order to continuously improve:

– business processes,
– products and services,
– customer satisfaction and loyalty,
– employee satisfaction and engagement, and
– overall organizational performance

With the ultimate desired outcome of:

– serving the public, and
– creating or adding value to society overall
• KM deals with knowledge from external as well as
internal sources including documents and databases

• KM systems embed and store knowledge in business
processes, products and services

• KM systems’ objective is to promote growth, transfer and
share of knowledge within the organization

• KM systems aim to assess on a regular basis the
knowledge assets of an organization and its impact.
          Core Knowledge Activities

   Barriers Affecting Knowledge Sharing

Psychological Factors                 Social Factors
•Reciprocity            Knowledge     Organizational culture
•Status                               Social networks
Knowledge Management Enabling Technologies

•   Intranets
•   Document Management Systems
•   Information Retrieval Engines
•   Groupware and Workflow Systems
•   Push Technologies and Agents
•   Help-Desk Applications
•   Brainstorming Applications
•   Data Warehouses and Data Mining Tools

Knowledge Management System

It is the collection of information technologies used to
facilitate the collection, organization, transfer and
distribution of knowledge between employees.
Benefits of Knowledge Management System





The broader application context of KM includes:
learning, education, and training industries.

1. What is knowledge management? Why do
   businesses today need knowledge management
   programs and systems for knowledge

2. What types of systems are used for enterprise-
   wide knowledge management? How do they
   provide value for organizations?

3. How do knowledge work systems provide value
   for firms? What are the major types of
   knowledge work systems?

4. What are the business benefits of using
   intelligent techniques for knowledge

5. What major management issues and problems
   are raised by knowledge management systems?
   How can firms obtain value from their
   investments in knowledge management
                 Management Challenges

1. Designing knowledge systems that genuinely
   enhance organizational performance

2. Identifying and implementing appropriate
   organizational applications for artificial
     The Knowledge Management Landscape

       Important Dimensions of Knowledge

•   Knowledge

•   Wisdom

•   Tacit knowledge

•   Explicit knowledge
            The Knowledge Management Landscape

U.S enterprise knowledge management software revenues, 2001-2006

                          Figure 11-1
      The Knowledge Management Landscape

        Important Dimensions of Knowledge

•    Knowledge:
    – Is a firm asset
    – Has different forms
    – Has a location
    – Is situational
        The Knowledge Management Landscape

    Organizational Learning and Knowledge Management

•     Organizational learning: Creation of new
      standard operating procedures and business
      processes reflecting experience

•     Knowledge management: Set of processes
      developed in an organization to create,
      gather, store, disseminate, and apply
The Knowledge Management Landscape

The knowledge management value chain

             Figure 11-2
     The Knowledge Management Landscape

      The Knowledge Management Value Chain

•   Knowledge acquisition

•   Knowledge storage

•   Knowledge dissemination

•   Knowledge application
     The Knowledge Management Landscape

      The Knowledge Management Value Chain

•   Chief Knowledge Officer (CKO): Senior
    executive in charge of the organization's
    knowledge management program

•   Communities of Practice (COP): Informal
    groups who may live or work in different
    locations but share a common profession
     Types of Knowledge Management Systems

      Types of Knowledge Management Systems

•   Enterprise Knowledge Management Systems:
    General purpose, integrated, and firm-wide
    systems to collect, store and disseminate digital
    content and knowledge

•   Knowledge Work Systems (KWS): Information
    systems that aid knowledge workers in the
    creation and integration of new knowledge in the

•   Intelligent Techniques: Datamining and artificial
    intelligence technologies used for discovering,
    codifying, storing, and extending knowledge
 Types of Knowledge Management Systems

Major types of knowledge management systems

                Figure 11-3
    Enterprise-Wide Knowledge Management Systems

            Structured Knowledge Systems

•     Structured knowledge

•     Semistructured knowledge

•     Knowledge repository

•     Knowledge network
Enterprise-Wide Knowledge Management Systems

 Enterprise-wide knowledge management systems

                  Figure 11-4
Enterprise-Wide Knowledge Management Systems

          KWorld’s knowledge domain

                 Figure 11-5
Enterprise-Wide Knowledge Management Systems

      KPMG knowledge system processes

                 Figure 11-6
    Enterprise-Wide Knowledge Management Systems

                Window on Technology

     DaimlerChrysler Learns to Manage
             Its Digital Assets
•     What are the management benefits of using
      a digital asset management system?

•     How does ADAM provide value for
    Enterprise-Wide Knowledge Management Systems

     Organizing Knowledge: Taxonomies and Tagging

•     Taxonomy: Method of classifying things
      according to a predetermined system

•     Tagging: Once a knowledge taxonomy is
      produced, documents are tagged with
      proper classification
  Enterprise-Wide Knowledge Management Systems

Hummingbird’s integrated knowledge management system

                     Figure 11-7
    Enterprise-Wide Knowledge Management Systems

                 Knowledge Networks

        Key Functions of an Enterprise
              Knowledge Network
•     Knowledge exchange services

•     Community of practice support

•     Auto-Profiling Capabilities

•     Knowledge management services
Enterprise-Wide Knowledge Management Systems

      The problem of distributed knowledge

                  Figure 11-8
Enterprise-Wide Knowledge Management Systems

   AskMe Enterprise knowledge network system

                  Figure 11-9
      Enterprise-Wide Knowledge Management Systems

Portals, Collaboration Tools, and Learning Management Systems

  •    Teamware: Group collaboration software
       running on intranets that is customized for
      Enterprise-Wide Knowledge Management Systems

Portals, Collaboration Tools, and Learning Management Systems

  •    Learning Management Systems (LMS):
       Tools for the management, delivery,
       tracking, and assessment of various types
       of employee learning
    Enterprise-Wide Knowledge Management Systems

               Window on Management

    Managing Employee Learning: New
            Tools, New Benefits
•    What are the management benefits of using
     learning management systems?

•    How do they provide value to Alyeska and
             Knowledge Work Systems

       Knowledge Workers and Knowledge Work

Knowledge workers perform 3 key roles:
•   Keeping the organization current in
    knowledge as it develops in the external

•   Serving as integral consultants regarding
    the areas of their knowledge, the changes
    taking place, and opportunities

•   Acting as change agents
      Knowledge Work Systems

Requirements of knowledge work systems

             Figure 11-10
            Knowledge Work Systems

       Examples of Knowledge Work Systems

•   Computer-aided design (CAD)

•   Virtual reality systems

•   Virtual Reality Modeling Language

•   Investment workstations
               Intelligent Techniques

        Capturing Knowledge: Expert Systems

•   Knowledge Base: Model of human

•   Rule-based Expert System: Collection in
    an AI system represented in the the form of
               Intelligent Techniques

        Capturing Knowledge: Expert Systems

•   AI shell: programming environment

•   Inference Engine: strategy used to search
    through the rule base

•   Forward Chaining: strategy for searching
    the rules base that begins with the
    information entered by user and searches
    the rule base to arrive at a conclusion
Intelligent Techniques

Rules in an AI program

     Figure 11-11
      Intelligent Techniques

Inference engines in expert systems

           Figure 11-12
               Intelligent Techniques

        Capturing Knowledge: Expert Systems

•   Backward Chaining: Strategy for
    searching the rule base in an expert
    system that acts as a problem solver

•   Knowledge Engineer: Specialist who
    elicits information and expertise from other
    professionals and translates it into set of
    rules for an expert system
               Intelligent Techniques

       Examples of Successful Expert Systems

•   Galeria Kaufhof

•   Countrywide Funding Corp.
                 Intelligent Techniques

    Organizational Intelligence: Case-Based Reasoning

•   Case-based Reasoning (CBR):
    Artificial intelligence technology that
    represents knowledge as a database of
    cases and solutions
     Intelligent Techniques

How case-based reasoning works

          Figure 11-13
              Fuzzy Logic Systems

              Fuzzy Logic Systems

•   Rule-based AI

•   Tolerates imprecision

•   Uses nonspecific terms called membership
    functions to solve problems
          Fuzzy Logic Systems

Implementing fuzzy logic rules in hardware

               Figure 11-14
                Neural Networks

                Neural Networks

•   Hardware or software emulating processing
    patterns of biological brain

•   Put intelligence into hardware in form of a
    generalized capability to learn
     Neural Networks

How a neural network works

       Figure 11-15
               Genetic Algorithms

               Genetic Algorithms

•   Problem-solving methods

•   Promote evolution of solutions to specified

•   Use a model of living organisms adapting
    to their environment
         Genetic Algorithms

The components of a genetic algorithm

            Figure 11-16
               Genetic Algorithms

                Hybrid AI Systems

•   Integration of multiple AI technologies into
    a single application

•   Takes advantage of best features of
                Intelligent Agents

                Intelligent Agents

•   Software program that uses built-in or
    learned knowledge base to carry out
    specific, repetitive, and predictable tasks
    for an individual user, business process, or
    software application
         Intelligent Agents

Intelligent agent technology at work

           Figure 11-17
Management Issues for Knowledge Management Systems

             Implementation Challenges

•   Insufficient resources available to structure
    and update the content in repositories

•   Poor quality and high variability of content
    quality because of insufficient mechanisms

•   Content in repositories lacks context,
    making documents difficult to understand
Management Issues for Knowledge Management Systems

             Implementation Challenges

•   Individual employees not rewarded for
    contributing content, and many fear sharing
    knowledge with others on the job

•   Search engines return too much
    information, reflecting lack of knowledge
    structure or taxonomy
Management Issues for Knowledge Management Systems

 Implementing knowledge management projects in stages

                     Figure 11-18
 Obtaining Value from Knowledge Management Systems

     Obtaining Value from Knowledge Management Systems

1.     Develop in stages

2.     Choose a high-value business process

3.     Choose the right audience

4.     Measure ROI during initial implementation

5.     Use the preliminary ROI to project enterprise-
       wide values
                     Chapter 11 Case Study

Can Knowledge Systems Help Procter & Gamble Stay Ahead of the Pack?

     1. Analyze P&G’s business strategy using the
        value chain and competitive forces models.

     2. What business and technology conditions
        caused P&G to change its business
        strategy? What management, organization,
        and technology problems did P&G face?
                     Chapter 11 Case Study

Can Knowledge Systems Help Procter & Gamble Stay Ahead of the Pack?

     3.   What is the role of knowledge management in
          supporting P&G’s business strategy? Explain how
          knowledge management systems help P&G
          execute its business strategy.

     4.   How successful has P&G been in pursuing its
          business strategy and using knowledge
          management? How successful do you think that
          strategy will be in the future? Explain your

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