MODULE1:

KNOWLEDGE
MANAGEMENT

KBL Srivastava
 Knowledge Management

• Introducing the concept of KM

• Why KM?
• KM system life cycle,
• Aligning KM and business strategy
What is Knowledge?

• Data – collection of unprocessed facts, a set of

discrete facts about events
• Information – organized or meaningful data
• Knowledge – information that is contextual,
relevant, and actionable
• Strong experiential and reflective elements
• Good leverage and increasing returns
• Dynamic
• Evolves over time with experience
• Knowledge is also known as Human Capital
• The primary difference between the terms
information and knowledge is in the level of
understanding of their underlying organizational
data
From Facts to Wisdom (Haeckel & Nolan)

Volume Value
Less is
Completeness More Structure
Objectivity Wisdom Subjectivity

Knowledge

Intelligence

Information
Facts
Information, Knowledge and Wisdom
 Types of Knowledge
• Shallow (readily recalled) and deep
(acquired through years of experience)
• Explicit (codified) and tacit (embedded in the
mind)
• Procedural (psychomotor skills) versus
episodical (chunked by episodes;
autobiographical)
• Chunking knowledge
• Shallow Procedural Knowledge
• Knowledge FROM PROCEDURAL TO EPISODIC KNOWLEDGE
• Knowledge of how to do a task that is essentially motor in
• nature; the same knowledge is used over and over again.
• _______________________________________________
• Declarative Knowledge
• Surface-type information that is available in short-term
• memory and easily verbalized; useful in early stages
• of knowledge capture but less so in later stages.
• _______________________________________________
• Semantic Knowledge
• Hierarchically organized knowledge of concepts, facts,
• and relationships among facts.
• _______________________________________________
• Episodic Knowledge
• Knowledge that is organized by temporal spatial means,
• not by concepts or relations; experiential information that
• is chunked by episodes. This knowledge is highly compiled
• Deep and autobiographical and is not easy to extract or capture.
• Knowledge
Source: Awad, E.M (2007). Knowledge Management
 0-8

Two major types of Knowledge

 Explicit knowledge
 Deals with objective, rational, and technical knowledge
 Examples: policies, goals, strategies, papers, reports
 Structured knowledge that is easy to codify
 Easily manipulated, shared, taught or learned

 Tacit knowledge
 Unstructured knowledge – in the domain of subjective,
cognitive, and experiential learning
 Highly personal, hard to formalize and document
 Cumulative store of the experiences, mental maps,
insights, expertise, know-how, trade secrets, skills set,
understanding, etc.
 Involves a lot of human interpretation
A few Foundation Principles and
Building Concepts
• Knowledge Influences Success
• Knowledge Resides in the Heads of People
• Two Types of Knowledge
• Codified (explicit)
• Personalized (tacit)
• Knowledge Sharing Requires a Conduit to Happen Systemically
• Technology is the conduit
• Knowledge Sharing Requires Trust
• KM embraces both the Knowledge Based organization and the
Learning Organization
• KM has planned architectural frameworks
Reservoirs of Knowledge

Knowledge Reservoirs

People Artifacts Organizational

Entities

Individuals Organizational Units

Practices Technologies Repositories Organizations

Inter-organizational
Groups
Networks

Source: Becerra-Fernandez, et al. (2004). Knowledge Management

 Knowledge Assets
Codified Knowledge Assets (Legally Owned)
Patents
Copyrights Tip of the
Trademarks
Documents
iceberg

• Working Solutions
• Web of Relationships
• Communities of Practice
• Experience
• Expertise and Theoretical Knowledge
• Database

Source: The Knowledge Evolution

Illustrations of the Different Types of Knowledge

Source: Becerra-Fernandez, et al. (2004). Knowledge Management

Conversion processes
(Source: The knowledge creating company, I. Nonaka and H. Takeuchi)

Tacit Socialization Externalization

From

Explicit Internalization Combination

Tacit Explicit
To
 Four Modes of Knowledge Conversion

• Socialization:
• A process of sharing experiences
• Apprenticeship through observation, imitation, and practice
• Externalization:
• A process of articulating tacit knowledge into explicit concepts
• A quintessential knowledge-creation process involving the creation of metaphors,
concepts, analogies, hypothesis, or models
• Created through dialogue or collective reflection
• Internalization:
• A process of embodying explicit knowledge into tacit knowledge
• Learning by doing
• Shared mental models or technical know-how
• Documents help individual internalize what they experience
• Combination:
• A process of systemizing concepts into a knowledge system
• Reconfiguration of existing information and knowledge
The knowledge creation process

15
Source: The knowledge creating company, I. Nonaka and H. Takeuchi
Knowledge Requires Capture, Organization, Access
and Leverage
 OLD WAY  NEW WAY
 Capture form is written,  Capture from is digits in
auditory or graphical cyberspace
representations
 Organization via software
 Organization is via tables of programs designed upon
content, indexes, engineering principles,
classification systems used mathematical equations,
by publishers, libraries, etc word associations in
 Access when physical body cyberspace 24/7/365
goes to where the  Access wherever the
knowledge is located…a physical bodies link via
library, a company, a computers
research laboratory, a
school  Tacit knowledge tapped
using many different
 Tacit knowledge rarely technological tools
tapped
 Leverage is exponential,
 Leverage is a sum game multiples upon multiples
Knowledge Work Activities

Ac q u i r e
An a l yz e
O r g a n i ze
C o d i fy
Co m m u n i
c aUttei l i z e
Re sult
KM in Practice
• Knowledge Teams - multi-disciplinary, cross-
functional
• Knowledge (Data)bases - experts, best practice
• Knowledge Centres - hubs of knowledge
• Learning Organization - personal/team/org
development
• Communities of Practice - peers in execution of
work
• Technology Infrastructure - Intranets, Domino, doc
mgt
• Corporate Initiatives - CKOs, IAM, IC accounting
 Seven Levers of KM
• Customer Knowledge - the most vital knowledge
• Knowledge in Products - ‘smarts’ add value
• Knowledge in People - but people ‘walk’
• Knowledge in Processes - know-how when needed
• Organizational Memory - do we know what we know?
• Knowledge in Relationships - richness and depth
• Knowledge Assets - intellectual capital
Some Cases
• Create/discover - 3M, Glaxo Wellcome
• Codify - BHA, Standard Life, PwC
• Diffuse - H-P, Thos. Miller, Rover, BP
• Use - Buckman, Steelcase, PwC, Andersen etc.
• Process/culture - Cigna, Analog
• Conversion - Monsanto
• Measure/exploit - Skandia, Dow Chemicals
Knowledge to Knowledge Management
• Process of capturing and making use of a firm’s
collective expertise anywhere in the business
• Doing the right thing, NOT doing things right
• Viewing company processes as knowledge
processes
• Knowledge creation, dissemination, upgrade, and
application toward organizational survival
• Part science, part art, part luck
Defining KM
• Knowledge management (KM) is managing the
organization’s knowledge (both explicit and tacit) through
the process of creating, structuring, disseminating and
applying knowledge to enhance organizational
performance and create value
• KM requires a major transformation in organizational
culture to create a desire to share
• Structuring enables problem-solving, dynamic learning,
strategic planning, decision-making
• Leverage value of intellectual capital through reuse
Roots of Knowledge Managment
Business
Transformation
Learning (BPR, TQM, culture)
Organization Innovation

Knowledge
Management

Intellectual Information
Assets/Capital Management
Knowledge-based
Systems
Need for Knowledge Management
• Knowledge has become the key resource, for a
nation’s military strength as well as for its
economic strength.
• It is fundamentally different from the traditional
key resources of the economist – land, labor, and
even capital
• The performance capacity, if not the survival, of
any organization in the knowledge society will
come increasingly to depend on those two factors
(Drucker,1994).
 Forces Driving Knowledge Management
• Increasing Domain Complexity: Intricacy of internal
and external processes, the rapid advancement of
technology.
• Accelerating Market Volatility: The pace of change, or
volatility, within each market domain has increased
rapidly in the past decade.
• Intensified Speed of Responsiveness: The time
required to take action based upon subtle changes
within and across domains is decreasing.
• Diminishing Individual Experience: High employee
turnover rates have resulted in individuals with decision-
making authority having less tenure within their
organizations than ever before.
Benefits of KM
• People don’t have to spend as much time looking for
answers
• People can move quickly on their problem-solving
anywhere and anytime
• People can work more effectively and more efficiently
• Share best practices
• Competitive advantage
• Expertise can be leveraged
• Better decision-making
• Reduced costs, therefore increased profits
• Retain key talent and expertise
• Improve customer retention and/or satisfaction
THE KNOWLEDGE ORGANIZATION
Indicators of knowledge: thinking actively and ahead, not passively and
behind Using technology to facilitate knowledge sharing and innovation

Culture
Competition
Collect
Create
Organize
Techno- Intelligence
logy Maintain Knowledge
Organization

Refine
Disseminate
Knowledge
Management
Leadership
Process
KM Drivers
The middle layer
addresses the KM A knowledge organization derives knowledge
life cycle from customer, product, and financial
knowledge. Also from financial practices
Source: Awad, E.M (2007). Knowledge Management
THE KM CYCLE AND THE
ORGANIZATION
Knowledge management cycle
• Creates knowledge through
new ways of doing things
• Identifies and captures new
knowledge
• Places knowledge into context
so it is usable
• Stores knowledge in
repository
• Reviews for accuracy and
relevance
• Makes knowledge available at
all times to anyone
WHAT KM IS NOT ABOUT
• Reengineering
• Discipline or philosophic calling
• Intellectual capital, per se
• Based on information or about data
• Information value chain or knowledge capture
• Limited to gathering information from the
company’s domain experts or retiring employees
and creating databases accessible by intranets
• Digital networks
 1-30

WHY KNOWLEDGE MANAGEMENT?

• Sharing knowledge, a company creates

exponential benefits from the knowledge as people
learn from it
• Building better sensitivity to “brain drain”
• Reacting instantly to new business opportunities
• Ensuring successful partnering and core
competencies with suppliers, vendors, customers,
and other constituents
• Shortens the learning curve
 1-31

KM System Justification
• Is current knowledge going to be lost?
• Is proposed system needed in several
locations?
• Are experts available/willing?
• Can experts articulate how problem will be
solved?
• Is there a champion in the house?
 Critical Success Factors
• Strong link to business imperative
• Compelling vision and architecture
• Knowledge leadership
• Knowledge creating and sharing culture
• Continuous Learning
• Well developed ICT infrastructure
• Systematic knowledge processes
Soft Infrastructure

• A culture of sharing - vs. information fiefdoms

• Directors of Knowledge (Intellectual Capital)
• Facilitating knowledge processes
• change teams, development workshops etc.
• Developing personal skills
• info management, ‘dialogue’, online techniques
• New measures of human capital, capabilities
KM System Development
Life Cycle
• Evaluate existing infrastructure
• Form the KM team
• Knowledge capture
• Design KM blueprint (master plan)
• Test the KM system
• Implement the KM system
• Manage change and reward structure
• Post-system evaluation
Evaluate Existing Infrastructure
System justification:
• Will current knowledge be lost through retirement,
transfer, or departure to other firms?
• Is the proposed KM system needed in several locations?
• Are experts available and willing to help in building a KM
system?
• Does the problem in question require years of experience
and cognitive reasoning to solve?
• When undergoing knowledge capture, can the expert
articulate how problem will be solved?
• How critical is the knowledge to be captured?
• Are the tasks non algorithmic?
• Is there a champion in the house?
The Scope Factor
• Consider breadth and depth of the project
within financial, human resource, and
operational constraints
• Project must be completed quickly enough
for users to foresee its benefits
• Check to see how current technology will
match technical requirements of the
proposed KM system
The Feasibility Question
A feasibility study addresses several questions:
• Is the project doable?
• Is it affordable?
• Is it appropriate?
• Is it practicable?
Areas of feasibility:
• Economic feasibility determines to what extent a new
system is cost-effective
• Technical feasibility is determined by evaluating hardware
and supportive software within company’s IT infrastructure
• Behavioral feasibility includes training management and
employees in the use of the KM system
The Feasibility Question (cont’d
Traditional approach to conducting a feasibility study:
• Form a KM team
• Prepare a master plan
• Evaluate cost/performance of proposed KM
• Quantify system criteria and costs
• Gain user support throughout the process
Role of Strategic Planning in KM System
Development
Risky to plunge with a new KM system without strategizing.
Consider the following:
• Vision — Foresee what the business is trying to achieve,
how it will be done, and how the new system will achieve
goals
• Resources — Check on the affordability of the business to
invest in a new KM system
• Culture — Is the company’s political and social
environment amenable to adopting a new KM system?
KM Team Formation
• Identify the key stakeholders in the prospective
KM system.
• Team success depends on:
• Caliber of team members
• Team size
• Complexity of the project
• Leadership and team motivation
• Promising more than can be realistically
delivered
KNOWLEDGE CAPTURE
• Explicit knowledge captured in repositories from
various media
• Tacit knowledge captured from company experts
using various tools and methodologies
• Knowledge developers capture knowledge from
experts in order to build the knowledge base
• Knowledge capture and transfer often carried out
through teams, not just individuals
Role of the Knowledge Developer
• The architect of the system
• Job requires excellent communication skills,
knowledge capture tools, conceptual thinking,
and a personality that motivates people
• Close contacts with the champion
• Rapport with top management for ongoing
support
Design of the KM Blueprint
The KM system design (blueprint) addresses several
issues:
• System interoperability and scalability with existing
company IT infrastructure
• Finalize scope of proposed KM system with realized net
benefits
• Decide on required system components
• Develop the key layers of the KM architecture to meet
company requirements. Key layers are:
• User interface
• Authentication/security layer
• Collaborative agents and filtering
• Application layer
• Transport Internet layer
• Physical layer
Testing the KM System
• Verification procedure: ensures that the system is right
• Validation procedure: ensures that the system is the right
system
• Validation of KM systems is not foolproof
Implementing the KM System
• Converting a new KM system into actual operation
• This phase includes conversion of data or files
• This phase also includes user training
• Quality assurance is paramount, which includes checking
for:
• Reasoning errors
• Ambiguity
• Incompleteness
• False representation (false positive and false negative)
Resisters of Change
• Experts
• Regular employees (users)
• Troublemakers
• Narrow-minded superstars
• Resistance via projection, avoidance, or
aggression
 Knowledge Management Strategies
and
Aligning KM Strategy with the Business
Knowledge Management Approaches
• Personalization strategy focuses on connecting
knowledge workers through networks and depends on
tacit knowledge and expertise
• It provides creative, rigorous and highly customized
customer services and products.
• Codification Strategy focuses on technology that enables
storage, indexing retrieval and reuse.
• It provides high quality fast, reliable and cost effective
service.
Knowledge Management Strategies
Knowledge maps to link knowledge to strategy
Systematically mapping, categorizing, benchmarking and
applying knowledge with the help of a KM system can not
make only such knowledge more accessible, but also
focusses and prioritizes KM.

Source: A Tiwana (2002). The Knowledge Management Toolkit

Creating a knowledge map to evaluate
corporate knowledge

Source: A Tiwana (2002). The Knowledge Management Toolkit

Aligning Business Strategy With KM Strategy

Source: A Tiwana (2002). The Knowledge Management Toolkit

Process of linking knowledge with business Strategy
(Tiwana,1999)

Source: A Tiwana (2002). The Knowledge Management Toolkit

Process of linking knowledge with business Strategy (Tiwana,
2002)

Source: A Tiwana (2002). The Knowledge Management Toolkit

Glaxo Wellcome
 A strategy led initiative - learning org. focus
 Workshops to convert rhetoric to action plans
 Using Intranets to share R&D, help approvals
 Library, document management support
 Reoreinted Technical Architecture
 Challenge is creating ‘sharing culture’
Bottom Line - better RoIC
 Glaxo Wellcome - Knowledge Net
Learning History
Process Improvements Team Skills
- Quality etc.

Knowledge New science

Communications Network competencies
Architecture

People Marketing products

- manager skills - customer dialogue
- ‘Yellow pages’ Strategy
- expertise
 Ernst & Young’s Framework for KM
Storage
• Input, Purge
• Archive, Abstract Deploy
• Index, Catalog • On-demand
Acquire • Coordinate
• Engagement • Repeatable
• Content • Event-based
based
• Non • Subscription
engagement • Commercialize
based
Add Value • Monitor usage
• Identify needs • Measure
• External
• Research satisfaction
• Develop
proprietary
• Package

Provide Infrastructure
Organization - Culture - Technology - Public Relations

Source: Ernst & Young, and “A Note on Knowledge Management,” Harvard Business School
9-398-031, 1997
Organizational Impacts of Knowledge Management
 Module 2
Knowledge Management

KBL Srivastava
 Topics
• KM Cycle: Process, Models of KM
cycle
•Knowledge creation and knowledge
architecture
•capturing tacit Knowledge
 KM Cycle
• Effective knowledge management requires an
organization to identify, generate, acquire,
diffuse, and capture the benefits of knowledge
that provide a strategic advantage to that
organization.
• A knowledge management cycle can be perceived
as the route information follows in order to
become transformed into a valuable strategic
asset for the organization via a knowledge
management cycle.
 KM Cycle Processes
• Knowledge Capture
• Knowledge Creation
• Knowledge Codification
• Knowledge Sharing
• Knowledge Access
• Knowledge Application
• Knowledge Re-Use

4
 Meyer and Zack KM Cycle
• KM cycle processes are composed of technologies,
facilities and processes of manufacturing products and
services.
• Information products are repositories comprising
information content and structure, which is unique for
each organization.
• Information Content is the data held in the repository
that provides the building blocks for the resulting
information products such as Banks have content relating
to Personal and commercial accounts
• The major developmental stages of a knowledge
repository and mapped these stages onto a KM cycle.
Meyer and Zack KM Cycle
 Meyer and Zack KM Cycle (1)
• Acquisition deals with issues regarding origin
of raw materials such as scope, breadth,
depth, credibility, accuracy, timeliness,
relevance, cost, control, and exclusivity.
• The guiding principle is that, highest quality
source data is required, else the intellectual
products produced downstream will be lower.
 Stage 2
• Refinement may be physical (like migrating from
one medium to another) or logical (like
restructuring, relabeling, indexing, and integrating).
• Refining includes cleaning up (like sanitizing content
so as to ensure complete anonymity of sources and
key players involved) or standardizing (like
conforming to templates of a best practice or
lessons learned as used within that particular
organization.
• This stage also adds up to the value by creating
more readily usable knowledge objects and by
storing the content more flexibly for future use.
 Stage 3 and 4
• Storage or Retrieval forms a bridge between the
upstream addition and refinement stages that feed
the repository and downstream stages of product
generation. Storage can be physical (file folders,
printed information) as well as digital (database,
knowledge management software).
• Distribution defines how the product is to be
delivered to the end-user (like fax, print, email) and
encloses not only the medium of delivery but also its
timing, frequency, form, language, and so on.
 Stage 5: Presentation
• Context plays an important role in Presentation or Application
stage. The performance of each of the preceding value-added
steps is evaluated here – for example, does the user have
enough context to be able to make use of this content?
• If not, the KM cycle has failed to deliver value to the individual
and ultimately to the company.
• Example: A Basic Database may represent an example of
Knowledge that has been created. Value can then be added by
extracting Trends from this data.
• The original information has been repackaged to provide Trend
analyses that can serve as the basis for Decision Making within
an organization
 Bukowitz & Williams Model

• Bukowitz and Williams portray a knowledge

management process framework that outlines “how
organizations generate, maintain and expand a
strategically correct stock of knowledge to create
value”.
• In this framework, knowledge includes knowledge
repositories, relationships, information technologies,
communications infrastructure, functional skill sets,
process know-how, environmental responsiveness,
organizational intelligence, and external sources.
 Bukowitz and Williams

ASSESS
GET

BUILD/SUSTAIN
USE Knowledge

LEARN CONTRIBUTE OR: DIVEST

12
 Bukowitz and Williams /2
• Get: seeking out information
– Tacit and explicit
– Being selective when faced with information
overload
• Use: combine content in new and interesting
ways to foster innovation in the organization
• Learn: learning from experiences
– Creation of an organizational memory

13
 Bukowitz and Williams/3
• Contribute: motivate employees to post what
they have learned to a knowledge base
– Link individual learning and knowledge to
organizational memory
• Assess: evaluation of knowledge capital:
Identify assets, metrics to assess them and
link these directly to business objectives

14
 Bukowitz and Williams/4
• Build and Sustain: allocate resources to
maintain knowledge base
– Contribute to viability, competitiveness
• Divest: should not keep assets that are no
longer of any business value
– Transfer outside the organization e.g. outsourcing
– Patent, spin off companies etc.

15
 Wiig KM Cycle
• Processes by which we build and use knowledge
– As individuals
– As teams (communities)
– As organizations
• How we:
– Build knowledge
– Hold knowledge
– Pool knowledge
– Apply knowledge
• Discrete tasks yet often interdependent & parallel

16
 Wiig KM Cycle/2

•Personal experience
•Formal education and training
Build Knowledge
•Intelligence sources
•Media, books, peers

Hold Knowledge •In people

•In tangible forms (e.g. books)

•KM systems (intranet, dbase)

Pool Knowledge •Groups of people-
brainstorm

•In work context

Use Knowledge
•Embedded in work processes
 Building Knowledge
• Learning from all kinds of sources to:
– Obtain Knowledge
– Analyze Knowledge
– Reconstruct (Synthesize) Knowledge
– Codify and Model Knowledge
– Organize Knowledge

18
 Holding Knowledge
• In people’s minds, books, computerized knowledge
bases, etc.
– Remember knowledge – internalize it
– Cumulate knowledge in repositories (encode it)
– Embed knowledge in repositories (within procedures)
– Archive knowledge
• Create scientific library, subscriptions
• Retire older knowledge from active status in repository (e.g. store
in another medium for potential future retrieval – cd roms, etc.)

19
 Pooling Knowledge
• Can take many forms such as discussions, expert networks
and formal work teams
• Pooling knowledge consists of:
– Coordinating knowledge of collaborative teams
– Creating expert networks to identify who knows what
– Assembling knowledge – background references from
libraries and other knowledge sources
– Accessing and retrieving knowledge
• Consult with knowledgeable people about a difficult problem,
peer reviews, second opinions
• Obtain knowledge directly from a repository – advice, explanations

20
 Pooling Knowledge - Examples
• An employee realizes he or she does not have the
necessary knowledge and know-how to solve a
particular problem
• (S)he contact others in the company who have had
similar problems to solve, consults the knowledge
repository and makes use of an expert advisory
system to help her out
• She organizes all this information and has subject
matter experts validate the content

21
 Using Knowledge (con’t)
• Synthesize alternative solutions, identify options, create new
solutions
• Evaluate potential alternatives, appraise advantages and
disadvantages of each, determine risks and benefits of each
• Use knowledge to decide what to do, which alternative to
select
– Rank alternatives & test the extent of feasiblity, and
acceptablity
• Implement selected alternative
– Choose and assemble tools needed
– Prepare implementation plan, distribute it, authorize
team to proceed with this solution

22
 Using Knowledge - Examples
• Expert mechanic encounters a new problem
• Gathers info to diagnose and analyze
• Synthesizes a list of possible solutions with the tools
he knows are available to him
• Decides on the best option and uses it to fix the part
• Non-routine tasks are approached in a different way
than familiar, standard ones

23
Five Critical Knowledge Functions for
each KM Cycle Step
• Type of knowledge or skill involved
– Securities trading expertise
• Business use of that knowledge
– Increase the value of a retirement fund portfolio
• Constraint that prevents knowledge from being fully
utilized
– Expert will retire at the end of the year with no successor
• Opportunities, alternatives to manage that knowledge
– Elicit and codify knowledge before person retires
• Expected value-added of improving the situation
– Valuable knowledge is not lost to organization

24
 Integrated KM Cycle

Assess
Knowledge Knowledge
Capture Sharing and
and/or Creation Dissemination

Knowledge
Acquisition and
Update Application Contextualize

Source: Dalkir (2005). Knowledge Management in Theory and Practice.

KNOWLEDGE CREATION
AND ARCHITECTURE
 KNOWLEDGE CREATION
• KM is not a technology; it is an activity
enabled by technology and produced by
people
• An alternative way of creating knowledge is
via teamwork
• A team compares job experience to job
outcome—translates experience into
knowledge
• Such newly acquired knowledge is carried to
the next job
• Maturation over time with a specific job turns
experience into expertise
 Impediments to knowledge
sharing
Personality
Compensation Organizational
Recognition culture
Ability utilization
Creativity Vocational
Good work environment reinforcers Knowledge
Autonomy sharing
Job security
Moral values
Attitude
Advancement
Variety Company
Achievement strategies and
Independence policies
Social status Work Norms

28
Source: Awad, E.M (2007). Knowledge Management
 KNOWLEDGE ARCHITECTURE
• People core: Evaluate current documents
people use
• Identify knowledge centers
• The technical core: The total technology
required to operate the knowledge
environment
People Content

Technology

29
Identifying Knowledge Content
Centers . Competition
. Job data
openings . Sales volume
. Benefits . Leader sales
information
Human
Resource
s Sales

Customer
. Strategies Service
. Tools
.R&D Marketing
. Advertising . Complaint
rate
. Satisfaction
information

30
Source: Awad, E.M (2007). Knowledge Management
 Technical layer of the KM system
User Interface
1
(Web browser software installed on each user’s PC)

Authorized access control

2 (e.g., security, passwords, firewalls, authentication)

Collaborative intelligence and filtering

3 (intelligent agents, network mining, customization, personalization)
Knowledge-enabling applications
(customized applications, skills directories, videoconferencing, decision support systems,
4 group decision support systems tools)

Transport
5 (e-mail, Internet/Web site, TCP/IP protocol to manage traffic flow)

Middleware
6 (specialized software for network management, security, etc.)

The Physical Layer

(repositories, cables)
7

Databases Legacy applications

(e.g., payroll) Groupware Data warehousing
(document exchange, (data cleansing,
collaboration) data mining)
Source: Awad, E.M (2007). Knowledge Management
 The User Interface Layer
• Tacit knowledge should be made available
face-to-face, e-mail, or by other media
• User interface design focuses on consistency,
relevancy, visual clarity, navigation, and
usability

32
 Technical Access Layer
• Intranet: The internal network of
communication systems modified around the
Internet
• Extranet: An intranet with extensions that
allow clearly identified customers or suppliers
to reach company-related technical
educational information.

33
Technical Access Layer

Internet Intranet
Extranet
Cloud Company employees
•Suppliers
•Vendors
PUBLIC •Partners
AT LARGE •Customers

•News/events •Human resource • Product information

information
•Marketing •Sales information
•Production
•E-commerce •Collaboration/cooperatio
information
•Careers n
•Sales information
•Strategic plans
Source: Awad, E.M (2007). Knowledge Management 34
 Features/Limitations of Firewalls
Protects against:
• E-mail services known to be problems
• Unauthorized interactive log-ins from outside firm
• Undesirable material coming in/leaving firm
• Unauthorized sensitive information leaking
Limitations include:
• Attacks that do not go through the firewall
• Weak security policies
• Viruses on floppy disks
• Traitors or disgruntled employees
35
Collaborative Intelligence and
Filtering Layer (Layer 3)
• Provides personalized views based on stored
knowledge
• Reduces search time for information
• Intelligent agents search across servers to find the
information requested by the client (user)
• Intelligent agents arrange meetings, pay bills, and
even wander through virtual shopping malls,
suggesting gifts and so on

36
 Criteria for an Effective
Collaborative Layer
• Security—very critical
• Portability across platforms
• Integration with existing systems
• Scalability, flexibility, and ease of use

37
 Expert Systems
• Emulate the reasoning of a human expert in a
problem domain
• Can help a person become wiser, not just better
informed
Components include:
• Justifier: explains how and why an answer is given
• Inference engine: problem-solving mechanism for
reasoning and inferencing
• Scheduler: coordinates and controls rule processing

38
Knowledge-Enabling Application
Layer
• Often referred to as value-added layer
• Creates a competitive edge for the learning
organization
• Provides knowledge bases, discussion databases,
sales force automation tools, imaging tools, etc.
• Ultimate goal: show how knowledge sharing
could improve the lot of employees

39
 Transport Layer
• Most technical layer to implement
• Ensures that the company will become a
network of relationships
• Includes LANs, WANs, intranets, extranets,
and the Internet
• Considers multimedia, URLs, graphics,
connectivity speeds, and bandwidths

40
 Middleware Layer
• Focus on interfacing with legacy systems and
programs residing on other platforms
• Designer should address databases and applications
with which KM system interfaces
• Contains a cluster of programs to provide
connections between legacy applications and
existing systems
• Makes it possible to connect between old and new
data formats

41
 Repositories Layer
• Bottom layer in the KM architecture
• Represents the physical layer where
repositories are installed
• Includes intelligent data warehouses, legacy
applications, operational databases, and
special applications for security and traffic
management

42
 Build In-House, Buy, or
Outsource?
• Trend is toward ready-to-use, generalized
software packages
• Outsourcing is also a trend, releasing
technological design to outsiders
• Regardless of choice, it is important to set
criteria for the selection
• Question of who owns the KM system should be
seriously considered

43
CAPTURING TACIT KNOWLEDGE
VARIOUS TECHNIQUES
Knowledge Codification in the KM System Life Cycle
Capture Tools Intelligence
Programs, gathering
books, articles, Shells, tables,
experts tools, frames
maps, rules
KNOWLEDGE
CAPTURE
KNOWLEDGE
(Creation) CODIFICATION
• Logical testing
• User Acceptance
• Testing, Training
Data Bases
TESTING AND
DEPLOYMENT
Explicit Knowledge
GOAL
KNOWLEDGE
INNOVATION

KNOWLEDGE
SHARING

KNOWLEDGE
TRANSFER
DATABASE Collaborative
tools,
Web browser, networks,
Web pages Intranets
KNOWLEDGE
BASE Distributed
Insight systems
45
Source: Awad, E.M (2007). Knowledge Management
 What Is Knowledge Capture ?
• Transfer of problem-solving expertise from some
knowledge source to a repository or a program
• A process by which the expert’s thoughts and
experiences are captured- mind automation
• Includes capturing knowledge from other sources
such as books, technical manuscripts, etc.
• A knowledge developer collaborates with an
expert to convert expertise into a coded program
• Knowing how experts know what they know

46
 Steps involved
• Knowledge capturing is a demanding process
in which knowledge developer collaborates
with the experts to convert expertise into a
coded program. It include three steps-
1. Use of appropriate tools to get information
from the expert
2. Interpreting the information and inferring the
experts knowledge and reasoning process
3. Using the interpretation to build the rules
that represent the expert’s though process or
solution
 Improving the Knowledge
Capture Process
• Knowledge developers should focus on how
experts approach a problem. They must Look
beyond the facts or the heuristics
• Re-evaluate how well knowledge developers
understand the problem domain and how
accurately they are modeling it.
• Elicit the expert knowledge through case situation
and scenarios.

48
 Indicators of Expertise
• Experts are distinguished by the quantity and
quality of knowledge they possess.
• They know more and what they know makes them
more efficient and effective.
• Peers regard expert’s decisions good decisions
• Every time there is a problem, the expert is
consulted
• Expert sticks to the facts and works with a focus
• Expert has a knack for explaining things
• Expert exhibits an exceptional quality in
explanations
49
 Expert’s Qualifications
• Knows when to follow hunches and when to make exceptions
• Sees big picture
• Possesses good communication skills
• Tolerates stress
• Thinks creatively
• Exhibits self-confidence
• Maintains credibility
• Operates within a schema-driven orientation
• Uses chunked knowledge
• Generates motivation and enthusiasm
• Shares expertise willingly
• Emulates a good teacher’s habits
50
 Pros and Cons of Using a Single
Expert
Advantages:
• Ideal when building a simple KM system
• A problem in a restricted domain
• Facilitates the logistics aspect of coordinating
arrangements for knowledge capture
• Problem-related or personal conflicts are easier to
resolve
• Shares more confidentiality with project-related
information than does multiple expert

51
 Pros and Cons of Using a Single
Expert (cont’d)
Drawbacks:
• The expert’s knowledge is not easy to capture
• Single experts provide a single line of reasoning,
which makes it difficult to evoke in-depth
discussion of the domain
• Single experts more likely to change scheduled
meetings than experts who are part of a team
• Expert knowledge is sometimes dispersed

52
 Pros and Cons of Using Multiple
Experts
Advantages
• Complex problem domains benefit from the
expertise of more than one expert
• Working with multiple experts stimulates
interaction
• Listening to a variety of views allows knowledge
developer to consider alternative ways of
representing knowledge
• Formal meetings frequently a better environment
for generating thoughtful contributions
53
 Pros and Cons of Using Multiple
Experts
Advantages
• Complex problem domains benefit from the
expertise of more than one expert
• Working with multiple experts stimulates
interaction
• Listening to a variety of views allows knowledge
developer to consider alternative ways of
representing knowledge
• Formal meetings frequently a better environment
for generating thoughtful contributions
54
 Pros and Cons of Using Multiple
Experts (cont’d)
Drawbacks:
• Scheduling difficulties
• Disagreements frequently occur among experts
• Confidentiality issues
• Requires more than one knowledge developer
• Process loss in determining a solution

55
 Developing a Relationship With
Experts
• Create the right impression- Knowledge developer
must learn quickly and se behavioral ad technical skills to
gain experts attention and respect.
• Do not underestimate the expert’s experience-
understand the experts style: procedure type, storyteller
type, godfather type or salesperson type
• Prepare well for the session- Knowledge developer
should know about the background of experts
• Decide where to hold the session- location and
meeting places should be quiet and interruption free
56
 Styles of expert’s expressions
• Procedure type—methodical approach to the
solution

• Storyteller—focuses on the content of the domain at

the expense of the solution

• Godfather—compulsion to take over the session

• Salesperson—spends most of the time explaining his

or her solution is the best
57
Approaching Multiple Experts
• Individual approach—holding a session with one
expert at a time
• Primary and secondary experts—start with the
senior expert first, on down to others in the
hierarchy. Alternatively, start bottom up for
verification and authentication of knowledge
gathered
• Small groups approach—experts gathered in one
place to provide a pool of information. Each
expert tested against expertise of others in the
group
58
 Analogies and Uncertainties In
Information
• Experts use analogies to explain events
• An expert’s knowledge is the ability to take
uncertain information and use a plausible line
of reasoning to clarify the fuzzy details
• Understanding experience. Knowledge in
cognitive psychology is helpful background
• Language problem. Reliable knowledge capture
requires understanding and interpreting
expert’s verbal description of information,
heuristics, and so on
59
 The Interview As a Tool
• Commonly used in the early stages of tacit knowledge capture
• The voluntary nature of the interview is important
• Major benefit is behavioral analysis
• Interviewing as a tool requires training and preparation
• Great tool for eliciting information about complex subjects
• Convenient tool for evaluating the validity of information
acquired
• Types of Interview: Structured, semi-structured, and
unstructured

60
 Variations of Structured
Questions
• Multiple-choice questions offer specific choices,
faster tabulation, and less bias by the way
answers are ordered
• Dichotomous (yes/no) questions are a special
type of multiple-choice question
• Ranking scale questions ask expert to arrange
items in a list in order of their important or
preference

61
 Things to Avoid
• Taping a session without advance permission from
the expert
• Converting the interview into an interrogation
• Interrupting the expert
• Asking questions that put the domain expert on the
defensive
• Losing control of the session
• Pretending to understand an explanation when you
actually don’t
• Promising something that cannot be delivered
• Bring items not on the agenda
62
 Sources of Error that Reduce
Information Reliability
• Expert’s perceptual slant
• Expert’s failure to remember just what happened
• Expert’s fear of the unknown
• Communication problems
• Role bias
Errors made by Knowledge developer- Age, Race,
Gender

63
 Problems Encountered During
the Interview
• Response bias
• Inconsistency
• Communication difficulties
• Hostile attitude
• Standardized questions
• Lengthy questions
• Long interview

64
 Issues to Assess
• How would one elicit knowledge from experts who cannot say
what they mean or mean what they say?
• What does one say or do when the expert says, “Look, I work
with shades of gray reasoning. I simply look at the problem
and decide. Don’t ask me why or how.”
• How does one set up the problem domain when one has only
a general idea of what it should be?
• What does one do if the relationship with the domain expert
turns out to be difficult?
• What happens if the expert dislikes the knowledge developer?

65
 On-Site Observation
• Process of observing, interpreting, and recording
problem-solving behavior while it takes place
• More listening than talking
• Some experts do not like to be observed
• Fear of ‘giving away’ expertise is a concern by the
one observed
• Process can be distracting to others in the setting
• Continuous shuttle process important

66
 Brainstorming
• Unstructured approach to generating ideas
about a problem
• All possible solutions considered equally
• Emphasis is on frequency of responses during
the session
• Idea generation, followed by idea evaluation
• Computer-aided approach to dealing with
multiple experts

67
 Protocol Analysis(Cases or
Scenarios)
• Think-aloud method
• Expert keeps talking, speaking out loud while solving
a problem
• Effective source of information on cognitive
processes
• Makes expert cognizant of the processes being
described
• Provides wealth of information toward knowledge
representation

68
 Consensus Decision Making
• Clear agreement regarding the best solution to a
problem
• As a tool, it follows brainstorming
• Procedure ensures fairness and standardization in
the way experts arrive at a consensus
• A bit tedious and can take hours
• The rigidity of the consensus method can be a
problem for many experts

69
 The Repertory Grid
• Domain expert viewed as a scientist who categorizes
a problem domain using his or her own model
• Grid used to capture and evaluate the expert’s model
• Experts see problems based on reasoning that has
stood test of time
• A representation of the experts’ way of looking at a
particular problem
• A grid is a scale or a bipolar construct on which
elements are placed within gradations
• Novice Expert

70
 The Repertory Grid (cont’d)
• Benefit: May prompt the expert to think more
seriously about the problem and how to solve it.
• Drawback: Difficult to manage when large grids are
accompanied by complex details
• Because of difficulty in simplicity and manageability,
the tool is normally used in the early stages of
knowledge capture

71
Nominal Group Technique (NGT)
• Mitigates the process losses associated with
multiple experts
• An alternative to the consensus technique
• Provides an interface between consensus and
brainstorming
• Panel of experts becomes a “nominal” group
whose meetings structured in order to effectively
pool individual judgment
• An idea writing or idea generation technique

72
 NGT (cont’d)
• Effective in multiple expert knowledge capture,
especially when minimizing differences in status
among experts is important
• In NGT, each expert has an equal chance to express
ideas in parallel with other experts in the group
• With discussion accommodated in sequential order,
NGT can be a more efficient and productive
approach than brainstorming

73
 Delphi Method (cont’d)
• Controlled feedback
• Statistical group response
• Experts often lack necessary knowledge on
which to base final judgment
• Poorly designed questionnaire could cause all
kinds of problems

74
 Concept Mapping
• A network of concepts, consisting of nodes
and links
• A node represents a concept and a link
represents the relationship between concepts.
An effective way for a group to function
without losing their individuality

75
 Figure: Conceptual Map
Example-

White
horse Bear
d At
chimneys On roofs
Birthday
has
rides
Spain listens
has climbs
lives
in lives in
SAINT helper of BLACK
NICOLAS
PETER

not same as gives brings

Santa
Clause Presents

Source: Awad, E.M (2007). Knowledge Management 76

Figure: Steps in Concept Mapping
1 Preparation of
Project
Participants, focus,
schedule

2
Idea
Generation
6 STEPS (focus for
Utilization brainstorming)
IN
CONCEPT
MAPPING
3
Idea
Structuring
(sorting/rating
4 statements)
5 Statement
Interpretation Representation
(cluster analysis)

Source: Awad, E.M (2007). Knowledge Management 77

 Blackboarding
• A global memory structure database or
repository- 3 factors- knowledge source
(expert), blackboard and control mechanism
• Assumes all participants are experts with
unique experience
• Each expert has equal chance to contribute to
the solution via the blackboard
• Process continues until the problem has been
solved
• Diverse approaches to problem solving
78
 Blackboarding (cont’d)
• Participants share a common language for
interaction
• Flexible representation of information
• Efficient storage and location of information
• Organized participation
• Iterative approach to problem solving

79
 Module 3
Knowledge Management

KBL Srivastava
 Topics
• Knowledge codification
• system development: system
testing and deployment
• knowledge transfer and
knowledge sharing
 What Is Knowledge Codification?
• Organizing and representing knowledge
before it is accessed by authorized personnel
• The organizing part is usually in the form of a
decision tree, a decision table, or a frame
• Converting tacit knowledge to explicit
knowledge in a usable form
• Converting undocumented to documented
information
• Making corporate-specific knowledge visible,
accessible, and usable for decision making
Knowledge Codification in the KM System Life Cycle
Capture Tools Intelligence
Programs, gathering
books, articles, Shells, tables,
experts tools, frames
maps, rules
KNOWLEDGE
CAPTURE
(Creation) KNOWLEDGE
CODIFICATION
• Logical testing
• User Acceptance
• Testing, Training
Data Bases
TESTING AND
DEPLOYMENT
Explicit Knowledge
GOAL

KNOWLEDGE
INNOVATION

KNOWLEDGE
SHARING

KNOWLEDGE
TRANSFER
DATABASE Collaborative
tools,
Web browser, networks,
Web pages Intranets
KNOWLEDGE
BASE Distributed
Insight systems
4
Source: Awad, E.M (2007). Knowledge Management
 Why Codify Knowledge?
• Diagnosis—KM system is given identifiable information
through the users observation or experience. Addressing
identifiable symptoms of specific causal factors
• Instruction/training: promote training among junior staff
based on captured knowledge of senior staff
• Interpretation— Interpretive codified knowledge system
compare aspects of an operation to present standards.
• Planning/scheduling—mapping out an entire course of action
before any steps are taken
• Prediction—inferring the likely outcome of a given situation
and flashing a proper warning or suggestion for corrective
action
Knowledge Dimensions and Bottlenecks

New Knowledge
Diffusion Too Slow

Knowledg Location
e is Not People
Form Time
Shared Do Not
KNOWLEDGE Know
Knowled
DIMENSION Where
ge Not
Shared Knowle
Recorded dge
Difficult
Knowledge Knowled Resides
to Know
Difficult to ge
Who Has
Access Difficult
Knowledg
to
e
Access
(Content)
6
Source: Awad, E.M (2007). Knowledge Management
 Modes of Conversion
• Tacit to tacit knowledge—produces socialization.
Observation and practice are two knowledge capture
tools
• Tacit to explicit knowledge— externalizing via
analogies or metaphors. Resulting explicit
knowledge can then be stored in repositories
• Explicit to tacit knowledge—internalizing explicit
knowledge into tacit knowledge
• Explicit to explicit knowledge—combining or sorting
different bodies of explicit knowledge to lead to
knew knowledge
7
 Things To Consider
• What organizational goals will codified
knowledge serve?
• What knowledge exists in the organization
that addresses these goals?
• How useful is existing knowledge for
codification?
• How would one codify knowledge?

8
 Problems With Codifying Tacit
Knowledge
• Distinctive style of the expert
• Special knowledge capture skills to codify tacit
knowledge effectively
• Certain knowledge is more of an art than a
science and art is difficult to codify into rules
• Dealing with experts is not easy
• Many firms lack the transparency of company-
wide knowledge
• Critical knowledge is often available, but no one
knows where to find it
9
 Tools and Procedures—
Knowledge Maps
• A guiding function
• Identify strengths to exploit and missing knowledge
gaps to fill
• Visual representation of knowledge, not a repository
• A straightforward directory that points to people,
documents, and repositories
• Direct people where to go when they need certain
expertise
• Recognize explicit and tacit knowledge captured in
documents and in experts’ heads

10
 How Knowledge Maps Work
• The map depicts visually the business issue or
problem at hand
• Pace of the group’s collaborative discussions guided
by questions to create shared knowledge
• Facts presented to the group to focus on realities of
the problem
• Nature of the collaborative discussion among peers
should be an open environment, facilitated by a
coach
• Post session follow-up activities are reviewed, and
conclusions are drawn
11
 The Building Cycle
• Once you know where knowledge resides, you
simply point to it and add instructions on how
to get there
• A company’s intranet is a common medium
for publishing knowledge maps
• Building criteria: clarity of purpose, ease of
use, accuracy of content

12
 Building Cycle (cont’d)
• First building step: Develop a structure of the
knowledge requirements
• Second building step: Define the knowledge
required of specific jobs
• Third building step: Rate employee performance by
knowledge competency
• Fourth building step: Link knowledge map to a
training program for career development and job
advancement

13
 Decision Tables
• More like a spreadsheet—divided into a list of
conditions and their respective values and a
list of conclusions
• Conditions are matched against conclusions

14
 Table: Decision Table
Condition Stub Condition Entry
1 2 3 4 5 6

Customer is bookstore Y Y N N N N

Order size > 6 copies Y N N N N N

Customer is librarian/individual Y Y Y Y
IF Order size 50 copies or more Y N N N
(condition) Order size 20-49 copies Y N N
Order size 6-19 copies Y N

Allow 25% discount X

Allow 15% discount X
Allow 10% discount X
THEN Allow 5% discount X
(action) Allow no discount X X

Action Stub Action Entry

15
Source: Awad, E.M (2007). Knowledge Management
 Decision Trees
• A hierarchically arranged semantic network
• Composed of nodes representing goals and links
representing decisions or outcomes.
• Read from left to right, with the root being on the
left
• All nodes except the root node are instances of the
primary goal. See Figure 7.7 next
• First step before actual codification
• Ability to verify logic graphically in problems
involving complex situations that result in a limited
number of actions
16
 Decision Tree
Order Discount
6 or Discoun
size ? ?
Customer is more t is 25%
Bookstore copies Discount
Less Discoun
? t is NIL
than 6
Bookstore copies
Discount Discount
50 or Discount
Policy ?
more is 15%
Not a copies
Order 20-49 Discount
bookstore Discount
size ? copies ?
Customer is is 10%
library or Discount
individual 6-19 Discount
?
copies is 5%
Discount
Less ?
Discount
than 6 is NIL
Source: Awad, E.Mcopies
(2007). Knowledge Management 17
 Frames
• Represent knowledge about a particular idea in one
place
• Handle a combination of declarative and operational
knowledge, which make it easier to understand the
problem domain
• Have a slot (a specific object or an attribute of an
entity) and a facet (the value of an object or a slot)
• When all the slots are filled with values, the frame is
considered instantiated

18
 Production Rules
• Form of tacit knowledge codification in the form of
premise-action pairs
• Rules are conditional statement that specify an
action to be taken if a certain condition is true
• The form is IF… THEN, or IF…THEN…ELSE
• Premise: A Boolean expression that must be
evaluated as true for the rule to be applied
• Action: Second component, separated from the
premise by THEN; executed if the premise is true

19
 Role of Planning (cont’d)
• Arranging for the verification and validation of
the system
• Developing user interface and consultation
facilities
• Promoting clarity and flexibility
• Reducing unnecessary risks
• Making rules easier to review and understand

20
 Inferencing and Reasoning
• Inferencing is deriving a conclusion based on
statements that only imply that conclusion
• Reasoning is applying knowledge to arrive at
solutions
• To reason is to think clearly and logically, to draw
reasonable inferences or conclusions from known or
assumed facts

21
 Case-Based Reasoning (CBR)
• CBR is reasoning from relevant past cases in a
manner similar to humans’ use of past
experiences to arrive at conclusions
• Goal is to bring up the most similar historical
cases that match the current case
• More time savings than rule-based systems
• Requires rigorous initial planning of all possible
variables

22
 Requirements for Knowledge
Development Work
• Computer technology
• Domain-specific knowledge
• Knowledge repositories and data mining
• Cognitive psychology

23
 Skills Requirements of
Knowledge Development
• Interpersonal communication
• Ability to articulate project’s rationale
• Rapid prototyping skills
• Personality attributes such as intelligence,
creativity, persistence, and a good sense of
humor

24
System development: System
testing and deployment
Figure : Knowledge Testing and Deployment
Capture Tools Intelligence
(programs, books, gathering Shells, Tables, Tools,
articles, experts) Frames,
Maps, Rules

KNOWLEDGE
CAPTURE
(Creation) KNOWLEDGE Logical testing,
CODIFICATION user
acceptance
testing, training
DATABASES
TESTING AND
DEPLOYMENT
Explicit
Knowledge
KNOWLEDGE
INNOVATION

KNOWLEDGE
SHARING

KNOWLEDGE
TRANSFER
DATABASE
Collaborative
tools, networks,
intranets
Web browser,
KNOWLEDGE Web pages,
BASE Distributed
systems 26
Insight
Source: Awad, E.M (2007). Knowledge Management
 Key Definitions
• building the system right?”
• User acceptance testing checks the system’s
behavior in a realistic environment. Answers
the question, “Have we built the right
system?”
• Deployment refers to the physical transfer of
the technology to the organization’s operating
unit
 Issues to Consider in Testing
• Subjective nature of tacit knowledge.
Intelligence difficult to measure
• Lack of reliable specifications make
knowledge-based testing arbitrary
• Problem of establishing consistency and
correctness
• Negligence in testing
• Lack of time for system testing
• Complexity of user interfaces
 Attributes in Logical Testing
• Circular
• Completeness
• Confidence
• Correctness
• Consistency/inconsistency
• Redundancy
• Reliability
• Subsumption error

29
 Approaches to Logical Testing
Verify Knowledge Base Formation
Structural Verification Anomalies
Circular Rules
Unusable Rules
Verification Redundancy
Duplicate
Redundant Subsumed
Verification of
Rules
Content
•Completeness
•Consistency
•Correctness
Verify Knowledge Base Functionality
Confidence
Reliability

Source: Awad, E.M (2007). Knowledge Management 30

 Key Testing Errors
• Circular errors tend to be contradictory in meaning
or logic
• Redundancy errors offer different approaches to the
same problem; duplication of knowledge
• Unusable knowledge is knowledge that comes up if
the conditions succeed or fail
• Subsumption errors in rules, if one rule is true, one
knows the second rule is always true
• Inconsistent knowledge, where the same inputs yield
different results

31
Steps in User Acceptance Testing
• Select a person or a team for testing
• Decide on user acceptance test criteria
• Develop a set of test cases unique to the
system
• Maintain a log on various versions of the
tests and test results
• Field-test the system
32
 Select Criteria for User
Acceptance Testing
• Accuracy and correctness of outcome
• Adaptability to changing situations
• Adequacy of the solutions
• Appeal and usability of the system
• Ease of use
• Face validity or credibility
• Performance based on expectations
• Robustness
• Technical/operational test
33
 Managing the Testing Phase
• Decide when, what, how, and where to evaluate the
knowledge base
• Decide who should do the logical and user
acceptance testing
• Draft a set of evaluation criteria in advance
• Decide what should be recorded during the test
• Review training cases, whether they are provided by
the expert, the knowledge developer, or the user
• Test all rules for Type I and Type II errors

34
 Issues Related to Deployment
• Selection of the knowledge base problem
• Ease of understanding the KM System
• Knowledge transfer
• Integration alternatives
• The issue of maintenance
• Organizational factors

35
 Selection of the Knowledge Base
Problem
System success may be assured if:
• User has prior experience with computer
applications
• User has been involved in the building of the KM
system
• Payoff from the KM system is high and
measurable
• KM system can be implemented without much
difficulty
• Champion has been supporting the system all
along 36
 Success Factors in KM System
Development
Organizational Trainer skills
Positive user attitude
and motivation Payoff

Top management Strong system

support and funding commitment by IT staff
SUCCESSFUL
Minimal organizational
Quality and ease of KM SYSTEM
politics
training IMPLEMENTA
TION
Organizational climate
Strong champion

Ease of maintenance Ease of Adequate explanatory

and upgrade system facilities
access and
use
Source: Awad, E.M (2007). Knowledge Management 37
 Integration Alternatives
• Technical integration through the company’s LAN or
existing information system infrastructure
• Knowledge-sharing integration when the KM system
is usable company-wide
• Decision-making flow integration when the system
matches the user’s style of thinking
• Workflow reengineering when the KM system
triggers changes in the workplace

38
 Organizational Factors
• Top management support
• Support of the work of the champion
• Ensure a clean and supportive organizational
climate
• De-emphasize role of politics
• Knowledge developer should remain neutral
within the political arena
• Return on investment

39
 User Training and Deployment
• Preparing for KM system training via advance
demos and easy to follow training
• Combating resistance to change
• Watch for knowledge hoarders
• Watch for troublemakers and narrow-minded
“superstars”
• Look for resistance via projection, avoidance,
and aggression

40
 Post-implementation Review
• Watch for quality of decision making
• Reassess attitude of end users
• Review cost of knowledge processing
• Revisit change in accuracy and timeliness
of decision making

41
 Internal and External Factors Affecting
Knowledge-Based System Quality

Framework
People

Domain
PEOPLE TECHNOLOGY
expert
KM
System
Quality
Use
r Knowl
edge
develo
per
ORGANIZATIONAL
CLIMATE

42
Source: Awad, E.M (2007). Knowledge Management
knowledge transfer and knowledge
sharing
 Factors In Knowledge Transfer
• Where knowledge is transferred from
• Media used in knowledge transfer
• Where knowledge is transferred to
Remember:
• Only a limited amount of expertise can be
captured as explicit knowledge
• Knowledge transfer facilitates knowledge
sharing
44
 Fig :A partial View of a KM System For Knowledge
Transfer
Knowledge Transfer Knowledge Application

Applications KB Customer
Services

Expert Knowledge
Repositories Workers

Trainers Knowledge
Applications
Products
Computerized
Educational Patents
Systems Technology

Customer Service Representatives,

Sales Field Service
45
Source: Awad, E.M (2007). Knowledge Management
 Prerequisites for Knowledge
Transfer
• Knowledge sharing recognizes personal
nature of people’s knowledge gained from
experience
• The myth that “once you build it, they will
use it” does not work that well
• For knowledge transfer to work, it takes
change in culture, politics, and attitude

46
Prerequisites for Transfer (cont’d)
• Instill an atmosphere of trust in the
organization
• Fix culture to accommodate change
• Push reasoning before process
• Doing is far better than talking
• Know how the firm handles mistakes

47
 Dimensions of Values and Beliefs
• Authority Fairness
• Collaboration Motivation
• Commitment Mistake tolerance
• Compensation Participation
• Competence Partnering
• Conflict resolution Teams
• Consistency Truth, openness
• Cooperation Self-management
• Creativity Risk tolerance
• Empowerment Change
• Innovation Focus 48
Leadership
• Understanding company mission
• Culturally internalized management practices
• Culturally internalized operational practices
• Culturally driven forces

49
 Factors That Retard Cultural
Values
• Culturally driven forces
• Understanding company priorities
• Questionable values
• Questionable beliefs
• Lack of trust in the approach or process

50
 Employee Job Satisfaction and
Stability of Workplace
• Job satisfaction determined by the match
between an employee’s vocational needs
and job requirements
• Success of knowledge transfer and sharing
depends on how satisfied employees are on
the job

51
 Major Known Vocational Needs
• Ability utilization Recognition
• Achievement Responsibility
• Activity Security
• Advancement Status
• Authority Supervision—human
relations
• Compensation Supervision--technical
• Creativity Variety
• Independence Working conditions
• Moral values
52
A Conceptual Job Adjustment
Model
What the job
offers JOB
employee Yes SATISFACTION
Match ?
No
VOLUNTARY
Employee RESIGNATION
vocational
needs met by
the job

Source: Awad, E.M (2007). Knowledge Management 53

 Transfer Methods
• A team sets out to perform a specific task
• Team outcome captured and fed back to same team
or another team
• New knowledge reinforces or improves performance
of the team next time round
• New knowledge also transferred to a knowledge
base for others to follow

54
Converting Experience Into
Knowledge

GOAL Action Compare action

Perform
OUTCO to outcome
a task
ME
Feedback new
knowledge
Select transfer
method
Face to
face/verbal
Form New
recipient
Knowledge
base

55
Source: Awad, E.M (2007). Knowledge Management
 Transfer Strategies
• Devoting specialized focus on on-site learning
• Absorbing the heuristics as they occur
• Adopting the organization’s culture to
facilitate knowledge transfer and knowledge
sharing

56
Inhibitors of Knowledge Transfer
• Lack of trust
• Lack of time and conference places
• Status of the knower
• Quality and speed of transfer

57
 How Knowledge Is Transferred
• Collective sequential transfer—specialized
team performs same function in other sites

Team
commits to
a project
Feedb
ack

Evaluate
knowledge Revise/redesig
gained n each
member’s Perform
assignment to new project
Evaluate each
member’s action reflect
before the next job knowledge
gained from
previous job 58
Source: Awad, E.M (2007). Knowledge Management
 How Knowledge Is Transferred
(cont’d)
Unique features of collaborate sequential
knowledge transfer:
• Team meetings are usually brief, but held
regularly as time permits
• Meetings held with all participants being equal
• What takes place in meetings kept within the
team
• Focus on the project, not the person or
personality

59
Meetings in Collective Sequential
Transfer
• Set agenda
• Keep it small
• Invite the right people
• Facilitate the process
• Take breaks
• Socialize
• Show accomplishments
60
 How Knowledge Is Transferred
(cont’d)
Tacit knowledge transfer—unique in complex,
nonalgorithmic projects, where knowledge
is mentally stored
TEAM A TEAM B
Tacit
E.g., US team of 11 knowle E.g., Indian team of 18
specialists dge specialists

transfer

LOCATION: LOCATION:
USA INDIA

61
Source: Awad, E.M (2007). Knowledge Management
 Role of Internet in Knowledge
Transfer
• Accommodates knowledge exchange and
communication
• Allows sending messages to multiple persons
simultaneously
• Offers a variety of services
• Integrates systems and networks

62
 Benefits of the Internet
• Doing business fast
• Gathering opinions and trying out new ideas
• Leveling the playing field
• Providing a superior customer service and
support resource
• Supporting managerial functions, spreading
ideas

63
 Limitations of the Internet
• Security and privacy vulnerability
• Exposure to fakes and forgeries
• Hackers threatening the integrity of files and
transactions

64
 Module 4:
Knowledge Management

KBL Srivastava
 Topics
KM system : Analysis design, and
development:
Knowledge infrastructure,
Knowledge audit, and knowledge
team
 Knowledge Infrastructure
• Seven-layer knowledge management
architecture and its underlying infrastructural
elements.
• Examine the Technology that make up these
layers and analyze various components that
can be deployed to transform existing
infrastructure into one that supports KM.
 The seven layers of the knowledge management system
architecture (Source: Tiwana, A.: Knowledge Management Toolkit,
2002)
 The seven layers of the knowledge
management system architecture (Continued)
 KM processes and Technology enablers
• Select the technology components with the objectives
clearly defined beforehand.
• A technology selection map to guide the technology
selection process while keeping the actual need of the
organization in focus.
• The focus of technology is to enhance two areas-
Storage and retrieval and communication.
• Technology helps in capturing and distributing
knowledge, and communication networks help in
transfer and collaboration in KM system.
6
Knowledge process and technology enablers (Source:
Tiwana, A.: Knowledge Management Toolkit, 2002)
 Interface Layer
• The interface layer is the topmost layer in the
KM system architecture.
• This is the only layer with which end users
directly interact with the system.
• The effectiveness of this layer is a main
determinant of the usability of a KM system.

8
 Selection Criteria for the Collaborative Platform
1. Efficient protocols: The network protocols used should not clog
up bandwidth of the network and should allow secure and fast
sharing of content across far-flung locations, including mobile
clients and traveling machines.
2. Portable operation: Companies often have various platforms and
operating system environments in use by different departments.
The collaborative platform must be able to operate in portable
manner across all these platform.
3. Consistent & easy-to-use client interfaces: Do not assume that
the users are technology experts; many of them might come
from nontechnical domains, departments, and backgrounds.
4. Scalability: As the number of users grows, the collaborative
platform should be able to scale up without degradation in
performance
5. Legacy integration: The collaborative platform must be able to
integrate this data into the final interface.
6. Security: Security becomes an important aspect of design with
enterprise being increasingly distributed
7. Flexibility and customizability: The choice of platform should
allow for a reasonable degree of customization and flexibility-9
what the user can see and needs to see
The Web or Lotus notes as platforms?
• It is easier for raw inputs such as spreadsheets,
meeting notes, design documents, etc., to be
converted into a storage-friendly format, but another
problem arises
• Companies need to standardize on specific
platforms and operating systems in a perfect
manner.- Web based or Lotus notes for knowledge
sharing

10
(Source: Tiwana, A.: Knowledge Management Toolkit, 2002)
 Packaging of Knowledge
• Filtering, editing, searching and organizing pieces of
knowledge, collectively called packaging, are essential
though frequently overlooked components of
successful KM.
• To capitalize on the wealth of intelligence available in
an organization, knowledge must be packaged in such
a way that it’s insightful, relevant, and useful.
• To make content useful, package should include:
Identification, Segmenting, Mass customization,
Format, Tests

12
 Issues of Knowledge Delivery
• Another issue is how much information should
be delivered: all or in parts (selectively).
• One can also consider when to deliver
knowledge: when needed (“just-in-time”) or
when created or acquired (“just-in-case”).

13
 Collaborative Intelligence and data
warehouse
• It is important to understand the role of
technology in the context of KM like which of
these technologies fit with the KM system and
how integrated will take place.
• A data warehouse is not very useful
unless the data is organized in to
meaningful information and applied
whenever required.
14
Source: Tiwana, A.: Knowledge Management Toolkit, 2002)
 Genetic Algorithm Tools
• In order to solve a problem or make a decision where
standard rules of thumb fail to work or are
impossible to use, one can try genetic-algorithm-
based solutions, which is a good choice.
• GAs help a decision maker to state that I do not
know how to build a good solution, but I will know it
when I see it!”

16
Source: Tiwana, A.: Knowledge Management Toolkit, 2002)
 Neural Networks
• A neural network is a networked computing
architecture in which a number of processors are
interconnected like the neurons in a human brain
that can learn through trial and error.
• A neural network can identify patterns within such
data without the need for a specialist or expert.
• Although theories on which neural networks are built
might suggest that such nets can deal with “dirty”
data, reality is quite different.

18
Source: Tiwana, A.: Knowledge Management Toolkit, 2002)
 Expert Reasoning & Rule-Based
Systems
• Rule-based systems are diametric opposites of
generic algorithm systems
– In generic algorithm, you can specify universal
conditions under which solutions are considered
good, but you cannot apply expert knowledge on
how to solve the problem.
– In rule-based systems, you can bring in expert
knowledge, but you cannot specify any universal
conditions that denote a good solution.

20
Source: Tiwana, A.: Knowledge Management Toolkit, 2002)
 Granularity in Knowledge Objects
• Because a KM system is intended as a
mechanism for securing corporate knowledge,
it needs to be populated with knowledge
objects.
• A key failure point in the design of a KM
system is not deciding on the right level of
detail at the start.

22
 Granularity in Knowledge Objects

Source: Tiwana, A.: Knowledge Management Toolkit, 2002)

23
 Granularity in Knowledge Objects
• Too high a level of granularity will result in the loss
of knowledge richness and context; too low a level
will cause unnecessary drain on network, storage,
and human resources, raise the cost, and reduce
the value of the object.
• The key lies in selecting the right level of
molecularity of knowledge that will be stored in
your KM system: the level that strikes an optimum
balance between the two opposite extremes of too
much details and too little detail, both of which can
render knowledge only marginally useful.

24
Example of Customer support and
Knowledge levels

Source: Tiwana, A.: Knowledge Management Toolkit, 2002)

 Infrastructural Elements for Searching,
Indexing, & Retrieval
• Indexing and retrieval capability of a KM system
determine the ease with which a user can find
relevant knowledge on the system.
• Four types of navigation strategies can be deployed
in varying combinations:
– Meta searching
– Hierarchical searching
– Attribute searching
– Content searching

26
 Meta searching and Hierarchical
Searching
• The main purpose of a meta search function is to
minimize the time spent in locating a general
category for a piece of potential knowledge within
a repository.
• A hierarchical search strategy organizes knowledge
items in a fixed hierarchy.
• The user can follow or traverse links within such a
structure to efficiently locate the right knowledge
element in a timely manner.
• This method is therefore apt for use in intranets,
since they support hyperlinking by defualt.
27
 Attribute Searching
• Searching by attributes use a value input by the
user.
• The attribute value is matched against closely
related values attached to the documents and
pointers such as skills databases.
• Those that closely match are returned as the final
search results.
• The limits of Attribute Searching
– Excessive query matches
– Breadth tradeoffs
– Failure to understand meanings of words and exact
context of use
28
 Content Searching
• Content searching is the least efficient of the search strategies
discussed here.
• The user enters an arbitrary search term, keyword, or text
string. All items that match are returned with a relevance
score.
• Score assignment is based on the frequency of matches within
each knowledge element such as a document or Website.
Strategies-
• To enable effective searching, use all or several of these
search and retrieval strategies in parallel.
• Using a single search technique can pose sever limitations on
the quality of the search

29
 Tagging Knowledge Elements with
Attributes
• Because searching works primarily on the
basis of textual string matching, it is important
that content – both formal and informal – be
tagged with a proper set of attributes.
• A company must define its own set of
attributes to tag knowledge content with.

30
 Tagging attribute for knowledge
elements in a KM system

Source: Tiwana, A.: Knowledge Management Toolkit, 2002)

 Activities Attribute
• The activities attribute refers to the organizational
activities to which the given knowledge element is
related.
• The value of this attribute must be defined up front,
and individual values need not be mutually exclusive.
• Therefore, your company must have an explicit
model of the activities and processes that are carried
on during the course of “running the business.”

32
 Domain Attribute
• The domain attribute tags the knowledge item to its
subject matter.
• This attribute is the primary attribute that drives the
meta search process.
• Principles of KE cannot be applied here because
those are more concerned with modeling knowledge
at the level of concepts and relations, which is too
micro for our purpose here.

33
 Type attribute
◼ The type attribute is more relevant to formalized
knowledge that is captured in electronic or
textual form such as a document or a report.

– Procedure – Best practice report

– Guideline – Note
– Protocol – Memo
– Manual – Failure report
– Reference – Success report
– Time line – Press release/report
– Worst practice report – Competitive intelligence
report
34
 Products & Services Attribute
• The product and service attribute specifies the
product or service to which the knowledge
element relates.
• The list should be kept specific and should not
overlap.
– Strategic consulting
– Implementation consulting
– E-commerce consulting

35
 Time Attribute
• The time attribute is useful for time-stamping events
and knowledge elements.
• Consequently, creation or use of an explicated
knowledge object must be specified.
• Not all knowledge objects can be assigned a value for
this attribute, so assign a value to this attribute
where possible.

36
 Location Attribute
• Use the location attribute to specify the location of
pointers that track people within and outside the
company.
• Not all knowledge elements will have a value assign
to this attribute, but it can be used to narrow search
by location.
• Make sure that the attribute usage and its values are
actually significantly relevant.

37
 Differences between a KM system and a
data warehouse
• Types of information managed
– A data warehouse focuses more on highly structured content, whereas
a KM system needs to support both informal and formal content and
everything in between.
– A data warehouse can be a part of a KM system, only as a source of
structured data that is input to the complex collaborative filtering
mechanisms of the KM system
• Context
– A data warehouse is arguably a resource of unquestionable value
when you need mine factual data.
– When such data is mined and interpreted, it provides value.
– But the need for interpretation is a fuzzy idea; data warehouses, by
themselves, are devoid of context.
• Size
– Since a data warehouse primarily focus on clean, structured, and
organized data, the size of a data warehouse is always large.
– A KM system might have storage system sizes ranging from very small
to extremely large.
Differences between a KM system and
a data warehouse
• Content focus
– The content focus of a KM system is on highly filtered information and on
knowledge, whereas that of a data warehouse is on scrubbed, raw, clean,
and organized data
• Performance
– Because of the complex nature of retrieval and classification requests
that a KM system must be able to handle, performance requirements
and computing power needed for a KM system are much higher than
those of a data warehouse
• Networks
– A data warehouse does not need to be on a live network to
function properly; however, this live network is imperative for a
KM system that is trying to draw from resources available
throughout the entire enterprise and beyond it – from the
Internet and collaborative, extended enterprise.
 The application layer
• Tools that enable integration of information
across tacit (such as people) and explicit (such
as databases, transaction-processing
repositories, and data warehouse) sources
help create and share context (the process
itself is called contextualization), and facilitate
sense making.

40
Source: Tiwana, A.: Knowledge Management Toolkit, 2002)
41
 Intranets and Extranets
• One of the most important aspects of information
access is that of being able to view content of
documents regardless of file formats, operating
system, or communications protocol.
• Intranets, owing to their consistent, platform-
independent access formats, such as rich HTML, and
a common, consistent protocol (HTTP), make this
possible.

42
 Pointers to expertise
• Electronic yellow pages
• When a key resource person is needed or
when a person with specific skill sets or
expertise is required, keyword and attribute
tag searching can pull up pointers with contact
information about persons who qualify, both
inside and outside the organization.

43
 Document management
• A lot of crucial information often exists primarily on
paper. Companies try to convert this information
into a more easily transferable and searchable
electronic format by scanning these documents.
• Document management fosters the ability to develop
a database of documents and classify them
automatically.

44
 Project management tools
• Although the role of PM tools in the actual creation
of knowledge is limited, these tools can provide a
good basis for organizing and storing documents,
records, notes, etc., coming out of a single project
engagement.
• Many companies populate these tools in a post
project phase, leaving the accuracy of project history
traceability open to questions.

45
 Video Conferencing and Multimedia
• Video conferencing enables people to exchange both
full-motion video and audio across a distributed
network.
• In a KM system, multimedia allows the system to
capture information content that would otherwise
be lost forever.
• Multimedia, especially video content, bypass
limitations of languages – an occasional barrier to
knowledge sharing when you are working in
transational project.

46
 Transparent capture enablers
• Digital whiteboards
• Tools such as this are indispensable in moving
a company from a structured, information-
based focus to a formal and informal,
knowledge-centric focus.

47
 Virtual share spaces
• There must be a way to encourage and enable
informal chat and conversations (even office
gossip) that are a part of work life in most
office settings.
• Virtual meetings
• Document collaboration
• Informal communication

48
 Mind mapping
• Mind maps, very similar to concept maps, can
be used to organize individual or collective
thought and represent it visually.
• Mind mapping can be an excellent knowledge
creation and organization tool, especially with
the advent of excellent software supporting it.

49
 Intelligent decision support systems
• Decision support systems, case-based reasoning
system, and contextual information retrieval systems
provide the needed historical base from past
experience that help make both minor and major
decisions fast and accurately.
• Data mining tools help extract trends and patterns
from transactional repositories, such as data
warehouses.

50
 The promise of peer-to-peer
knowledge networks
• Peer-to-peer networking naturally extends to
support KM because it closely mirrors face-to-
face human communication.
• Peer-to-peer networking is defined as sharing
of resources by direct exchange between
individual systems in a digital network.

51
 Affinity to infinity
• Individuals initially begin to share information,
expertise, best practices, and content in peer
networks because of the affinity that networks
create.
• Each additional member increase the potential value
of the network manifold or, in economic term, create
increasing value.
• Intricate webs of affiliations open multitudinous
possibilities for collaborative knowledge integration
in autonomous groups that can be spontaneously
assembled and disassembled.
52
 Other benefits of Peer-to-Peer
knowledge platforms
• First, decentralization eliminates the overhead of
maintaining resource and expertise profiles for
individuals in corporate yellow pages.
• Second, knowledge sharing relies heavily on rich
media, such as voice, video, and multimedia, to
overcome the limitations of text to share tacit
knowledge.
• Third, their explicit knowledge and profiles of mobile
users can be redundantly replicated across nodes to
ensure ubiquitous availability.

53
 Technological solutions for
motivational issues
• Understanding what motivates people to apply
their expertise is key to avoiding the trap of
building technology marvels that no one uses.
– First, users will contribute and share their insight only
when they value their digital community.
– Second, shred contest is essential to contribute
meaningful to collective task.
– Third, peer-to-peer environments must emphasize
knowledge integration over acquisition or learning.
– Finally, such environments must provide reputation-
building mechanisms to foster a pervasive thread of trust
on which any community stands.

54
2. Knowledge audit
 Why Audit Knowledge?
• Devising a knowledge-based strategy
• Architecting a KM blueprint
• Seeking to leverage its “people assets”
• Trying to figure a way out of corporate ebbing
• Striving to strengthen its own competitive
weaknesses
• Facing competition from knowledge-intensive
competitors that are far ahead on the learning curve
 Measuring knowledge growth
• Very often, companies do not know where
they stand in terms of the knowledge that
they possess.
• Bohn’s framework provides an excellent
starting point for figuring out where you stand,
relatively, in terms of your firm’s knowledge.
 Measuring Knowledge Growth
(Bohn Stages)

Source: Tiwana, A.: Knowledge Management Toolkit, 2002)

Source: Tiwana, A.: Knowledge Management Toolkit, 2002)
 Measuring knowledge growth
• You can measure the intellectual dimensions
of the following:
– Your company’s initial standing
– Your competitor’s standing
– Your company’s progress along this scale
– Steps and directions to move your company up on
this scale
Stages of Knowledge growth: Status of the organization

Source: Tiwana, A.: Knowledge Management Toolkit, 2002)

Stages of Knowledge growth: Status of the organization

Source: Tiwana, A.: Knowledge Management Toolkit, 2002)

Stages of Knowledge growth: Status of the organization

Source: Tiwana, A.: Knowledge Management Toolkit, 2002)

 From Art to Science
• Progression of a company from one that is highly
dependent on the tacit knowledge of a few
individuals to one in which both explicit and tacit
knowledge are shared and easily accessible can be
best described as a progression from art highly
subjective and dependent on the doer’s tacit
knowledge) to science (repeatable and robust
methodology capable of handling variations).
 The Knowledge Audit Team
• The audit team needs representatives from at least the
following areas.
– Corporate strategist
– Senior management, visionary, or evangelist.
– Human resource manager
– Marketer
– Information technologist
– Knowledge analyst
• Planning a knowledge audit
– Once the rationale is explicitly written down, the team must
identify the optimum level of performance and the highest,
reasonably achievable levels of performance at which each
component of the knowledge assets should operate.
 Conducting the knowledge audit
• The knowledge audit consists of a sequence of
six steps:
– Define the goals
– Determine the ideal state
– Select the audit method
– Document existing knowledge assets
– Track knowledge growth over time
– Analyzing the populated capability quadrants
 Defining the goals
• When you think about goals, thinks of specific
ones, such as:
– We need to increase profits by 40 percent by next
year.
– We need to reduce cost of sales by 12 percent
before the end of the fiscal year.
– We want to improve customer retention by 4
percent within 18 months.
– We want to increase project turnaround speed by
14 days on the average over the next 3 years.
Source: Tiwana, A.: Knowledge Management Toolkit, 2002)
 Determining the ideal state
• In this stage, you and your knowledge audit team
must reach a consensus on what you consider the
beat state that you could wish for and more
reasonably reach, albeit with great difficulty.
– This is the best case scenario against which you will
judge your entire knowledge management initiative later
on.
• Knowledge of what the best value of your
knowledge assets should be, is essential to allow
you to measure the results of your KM efforts
against a relevant and stationary benchmark.
Source: Tiwana, A.: Knowledge Management Toolkit, 2002)
Source: Tiwana, A.: Knowledge Management Toolkit, 2002)
 Selecting the audit method
• The method you use for auditing your company’s or
group’s knowledge determines the degree to which
you will accurately gauge the current (pre-KM) state
of that aspect or knowledge dimension.
• The audit method that your decide to use must
account for at least the following three critical
intangible assets:
– Employee know-how
– Reputation
– Organizational culture
Source: Tiwana, A.: Knowledge Management Toolkit, 2002)
 Documenting knowledge assets
• It is essential to document the knowledge-
based assets that your company has in a
consistent framework.
• The framework makes it easier to compare
with previously measured values and with
corresponding values for your competitors.
Capability framework for positioning knowledge related assets

Source: Tiwana, A.: Knowledge Management Toolkit, 2002)

Capability framework for positioning knowledge related
assets

Source: Tiwana, A.: Knowledge Management Toolkit, 2002)

 Tracking knowledge growth over time
1. How is the stock of this knowledge resource
increasing?
2. Is it increasing? If so, how do we know that it is?
3. How can we ensure that the stock (or knowledge)
continues to increase?
4. Are we making the best use of this knowledge
resource?
5. Do all employees recognize the value of this
resource?
 Tracking knowledge growth over
time
6. How durable is this knowledge asset? Will it
decline over a period of time? How easily can
others (competition) identify and copy this resource?
7. Can the competition easily nurture and grow this
knowledge?
8. Is there any aspect that our competition has
leveraged but we have not?
9. Can we imitate it? Need we?
10. Can this knowledge “walk out of the door”?
11. How is it changing over time?
12. Will our company need it after X (define X) years?
 Strategic Positioning within the
Technology Framework
• Mapping knowledge in each of the areas that you
chose in the earlier stages of the knowledge audit,
as describes in Figure above provides excellent
insight into the way KM and business strategy can be
kept in perfect synchronization.
• This insight can help in determining the strategic
position and competitive advantage possessed by
the firm in terms of the explicit and tacit knowledge
contained within the firm – in people’s head,
databases, resident experience, electronic
discussions, and KM systems.
 The Four Positioning Choices
• The green shaded areas indicate a high competitive
advantage – areas where your knowledge is already
well managed but can possibly be improved.
• The right cells in the matrix represent the two
quadrants where KM holds the most promise for
producing groundbreaking results.
• Knowledge that falls outside these shaded areas
represents those areas where the support of a KM
system and an effective KM strategy is most needed.
Source: Tiwana, A.: Knowledge Management Toolkit, 2002)
 Strategic Positions
• Strategic position A: indicates that your company is
internally safe but externally vulnerable on this front.
• Strategic position B: indicates that your company has
managed to explicate some portion of its knowledge;
however, this is a relatively small percentage of what
your competitors have managed to explicate.
• Strategic position C: A fundamentally weak position,
where your company has to strategic advantage
whatsoever.
• Strategic position D: Most desirable. Currently
successful but need to manage knowledge in such a
manner that their temporary advantage is converted
into a longer term, sustainable competitive advantage.
 3.Design The KM Team
Topics Covered
Design the KM team.
• Identify sources of requisite expertise.
• Identify critical points of failure: requirements, control,
management buy-in, and end user buy-in.
• Structure the knowledge management team:
organizationally, strategically, and technologically.
• Balance technical and managerial expertise, manage
stakeholder expectations.
• Resolve team-sizing issues.
 KM TEAM
A knowledge management system is built on expertise,
knowledge, understanding, skills, and insights brought into the
project by a variety of stakeholders who might have little in
common from a functional standpoint
 Sources of Expertise
• Draw their expertise from several different
sources:
– Internal, centralized IT departments
– Team-based local experts
– External vendors, contractors, partners, and
consultants
– End users and front-line staff

85
The KM team : The right balance
 Local Experts and Intradepartmental
Experts
• Experts within the company, people who come in early
or stay late to play with new tools that become
available. More adoptable to technology
• Many of these experts maybe non-technologists, but
they can gauge the possible usefulness of each feature
of the current system.
• Local experts very often are the first ones to notice the
limitations of current and existing systems and think of
how to upgrades and make changes to meet the
evolving needs of the group.

87
 Internal IT Departments
• It is IT staff who will bring in knowledge of:
– Infrastructural capabilities and limitations
– Connectivity and compatibility among the team based
systems and the overall organizational technology
infrastructure
– Standardization issues across different platforms,
applications and tools
– Technicalities underlying the adaptation of these tools
by various knowledge worker groups within the
organization
– It is critical to select personnel with credibility in the
eventual user group.
– Example- Platinum technology KM system

88
• Act as a bridge and as interpreters between
people from different backgrounds, skill areas,
and specializations.
• Learn faster than the average person.
• Bring value to the overall team synergy.
• Learn the basic lingo and understand the
frameworks.
• Have the ability to deal creatively and
rationally with the problems
 Laterality
• Laterality refers to the ability to cut across
functional boundaries and relate to people
from different areas. (capability to think
divergently)

90
 Laterality
• Groups of such people have also been referred to as
communities of practice; they are characterized by:
– Multifunctional groups that incorporate diverse viewpoints,
training, ages and roles
– Enacting a common purpose by engaging in real work,
building things, solving problems, delivering service, and
using real tools
– Developing intellectual property, knowledge, firm culture,
internal language, and new skills
– Making lasting changes in the people and the competency
that they embody

91
 Role of Consultants
• Selection a consultant should, therefore, be
based on the extent to which the person is willing
to transfer existing skill to company’s employees.
• Some of the other issues that must be considered while
selecting a consultant include:
– The consultant’s reputation for integrity.
– The consultant’s history that demonstrates the ability to
maintain confidentiality about past project.
– Whether the consultant has worked successfully for your own
company on earlier projects.
– Weather the consultant (or consulting company) is working on a
similar project for a competitor.
– Weather your internal team trusts and has confidence in the
consulting company

92
 KM Team Structure

(Source: Tiwana, A.: Knowledge Management Toolkit, 1995)

 Structuring the KM team

(Source: Tiwana, A.: Knowledge Management Toolkit, 2002)

 Structuring the KM team

(Source: Tiwana, A.: Knowledge Management Toolkit, 2002)

 Structuring the KM team

(Source: Tiwana, A.: Knowledge Management Toolkit, 2002)

 Composition of the Team and
Selection Criteria
• Cross-Functional expertise (diversity) in KM teams
should be taken as a given characteristics.
• Teams need to be designed for effectiveness.
• Team’s design has much to do with the nature of the
project itself.
• Functional diversity can lead to only two possible
outcomes, depending on how it’s handled.
– The first and common outcome is destructive conflict and
tension.
– The second, more desirable outcome is characterized by
synergy, creativity and innovation.

97
 Designing the KM Project team
.

(Source: Tiwana, A.: Knowledge Management Toolkit, 2002)

98
 Designing the KM Project team

(Source: Tiwana, A.: Knowledge Management Toolkit, 2002)

 Temporary Versus Permanent Team
Members
• A KM project needs at least a small portion of
the group to be permanent.
• The core team should consist of only the
following participants:
– Knowledge champion or a senior manager.
– IT staff.
– User delegates representing the core business
area that is going to depend on the KM system.

100
 Team Life Span & Sizing Issues
• There are two schools of thought on the
future of KM.
– KM will continue to depend on people to manage
knowledge throughout the lifetime of the
organization
– KM is a self-eliminating field
• Team members on the KM team should be
promised strong rewards and promotions if
the KM initiative truly succeeds.

101
 The Project Leader
• The KM project leader may or may not be the same
person as the CKO.
• KM projects need leadership that helps create a
supportive, unobtrusive, and focused environment
• The project leader must track progress, budgets,
workloads and schedules
• The KM project leader serves as the visionary
• A project leader must resolve internal dynamics,
serve as a translator, and take charge of task
delegation

102
 Internal Dynamics
• The project leader plays an essential part here by
helping team members understand why even trivially
straightforward issues and differences seem to be so
difficult to resolve.
• In their facilitating role, project leaders can pose the
key questions, clarify differences and their
underlying assumptions, then give members of the
KM team sufficient room to actually resolve these
differences.

103
 Translation and Delegation
• The project leader also needs to be able to act as
translator in the startup stages of the project when
the user teams and the IT participants fail to
understand each other’s viewpoints because of
vocabulary differences.
• To determine the actual issues of concern and to
identify the actual knowledge flow problems that
exist within the company, the project leader should
encourage participants to actually collect relevant
data from their own department through meetings,
surveys, interviews and focus groups.

104
 User Participation
• It is the project manager’s role to ensure that
the KM project is going in a direction that
builds toward a system that users actually
need.
• One of the most effective ways of verifying
this linkage is to show a preliminary version of
the KM system to actual users.

105
 Prototypes
• A prototype provides both the developers – in this
case, the KM team – and the users with an idea of
how the system in its final form will function.
• By using such a prototype, even if it is incomplete,
users can see the possibilities of the KM system
understanding of the final product can lead to or
trigger highly desirable refinement of its features,
interface, functionality, and design.

106
(Source: Tiwana, A.: Knowledge Management Toolkit,
2002)
107
 The KM Team’s Project Space
• Members of KM team should be able to
provide adequate answers to these
questions collectively:
1. What is the company’s envisioned strategic
and performance goal?
2. Where does the KM team fit in the
organizational hierarchy?
3. Does the KM project fit vertically or
horizontally in the value chain?
4. What are the financial and time constraints
for the project?

108
 The KM Team’s Project Space
• Members of KM team should be able to
provide adequate answers:
8. What level of commitment does the team have
from the senior management and from the users?
9. Where are the cultural blockades that should be
expected?
10. Has any competitor or noncompeting firm
implemented a project like this?

109
 Managing Stakeholder Expectations
• Formally present this work to various
stakeholder groups
• Such an interaction can help the team
compare the project’s objective with
stakeholder expectations and perceptions

110
 Highways to Failures
• A survey of 8000 software projects in 400 U.S. firms
found that only one in six was successful. Of the
remaining, about one third were never complete, and
over half were over budget, did not finish on time, or
failed to deliver the promised functionality.
• Such failures annually cost U.S. business about $78 billion
in development cost, and another $22 billion in cost
overrun.
• Lack of an active role of the top management has been
identified as the primary reason that many projects fail.
• The second reason is failure of the users to buy in to the
project

111
 Categorizing Risks in building KM system

(Source: Tiwana, A.: Knowledge Management Toolkit, 2002)

112
 Controlling and Balancing
Requirements
• The only thing you can do about customer
buy-in problems is to try selling the project
harder and to gauge end-user needs more
appropriately; the operating environment is
an entirely different story.
• That is where the cultural aspects of a KM
system and the people around it come into
play.

113
 Solving User Buy-In Problems
• To ensure that senior managers actually buy
into the project.
• To ensure that the “bigger-picture” that the
management has in mind is well
accommodated and incorporated

114
 Lessons Learned
• Identify a few key core stakeholders
• Identify sources of requisite expertise
• Select a visionary and experienced project
leader
• Identify critical failure points
• Avoid external consultants if possible
• Balance the knowledge management team's
managerial and technological structure
 Module 5
Knowledge Management
KM system : Analysis, design, and
development

KBL Srivastava
 Topics Covered
• Develop the knowledge management architecture.
• Understand and select the architectural components.
• Design for high levels of interoperability.
• Optimize for performance and scalability.
• Understand repository life-cycle management.
• Understand and incorporate requisite user interface
considerations.
• Position and scope of the knowledge management
system.
• Build-or-buy decision and understand the tradeoffs.
• Future-proof the knowledge management system.
 KM Blueprint
• knowledge management blueprint provides a
roadmap for building and incrementally
improving a knowledge management system
 Analyzing Lost Opportunities
• How companies lose potential opportunities by trying to explicate the wrong
types of knowledge while failing to explicate the right types

Knowledge that could have been explicated, shared, distributed, and applied, but was
never articulated, represents a lost opportunity due to the failure to leverage this asset
The shaded box indicates the correct positioning of a knowledge management system and
knowledge management strategy.
 The KM Architecture

• IT is a great enabler for sharing, application,

validation, and distribution of explicit knowledge.
• Its weakness become apparent when companies try
to use the same techniques and systems to leverage
tacit knowledge.
• With that in mind, the KM architecture should be
seen as an enabler for KM and not a complete
solution: a means and not an end in itself.

5
(Source: Designing the KM Blueprint form
Tiwana, A.: Knowledge Management Toolkit,
2002)

6
 Components of A KM System
• A KM system, in it’s initial stages, can be broken
into several subcomponents:
1. Repositories: Hold explicated formal and informal knowledge and the
rules associated with them for accumulation, refining, managing, validating,
maintaining, annotating (adding context), and distributing content.

2. Collaborative platforms: Support distributed work and

incorporate pointers, skills databases, expert locators, and informal
communications channels

3. Networks: Support communications and conversation include hard

networks and your intranet, your extranets, and soft networks such as shared spaces,
industry-wide firm collaborations, trade nets, industry forums, and exchanges

4. Culture: Enablers to encourage sharing and use of the above

7
 The Knowledge Repository
• An information repository differs from a knowledge
repository in the sense that the context of the
knowledge object needs to be stored, along with
the content itself.
• A knowledge platform may consist of several
repositories, each with a structure that is
appropriate for the particular type of knowledge or
content that is stored.

8
 The Knowledge Repositories

(Source: Tiwana, A.: Knowledge Management Toolkit, 2002)

9
 Elements of knowledge content recorded by the
Repositories

Declarative knowledge such as significant and meaningful

concepts, categories, definitions, and assumptions

• Procedural knowledge such as processes, sequences of events

and activities, and actions

• Causal knowledge such as rationale for decisions, rationale for

rejected decisions or alternatives, eventual outcomes of activities,
and associated informal pieces

• Context of the decision circumstances, assumptions, results of

those assumptions, and informal knowledge such as video clips,
annotations, notes, and conversations
 The Hazards/Liabilities of having
Integrative Repositories
• Having integrative repositories might seem like a good
idea to begin with, they can be the victims of their
own success.
– Once users start adding content to multiple repositories,
without a clear-cut validation or expiration mechanism, it
might result in a situation similar to an information
overload.

• Example: Arthur Andersen Consulting group's

KnowledgeSpace™: With the extensive use of the Lotus
Notes–based repositories, the extant content has grown
into almost 3,000 repositories of Notes discussion
databases. Many of them becoming redundant

11
 Content Centers
• When one is trying to integrate multiple function- or
department- specific repositories into one central
repository, one should pay close attention to content
centers that are typically good candidates for integration.
• Examples of content centers include
• Production department
• Customer services
• Market intelligence and competitive planning
• Employee resources and the human resources department
• Administrative department
• Sales and marketing
• Finance
• Business partners and suppliers
12
A sample checklist for competitive knowledge
1. What others are talking about your competition?
– Public
• Case studies, articles, newspapers, consultants, employee search
firms and consumer groups.
– Trade and professional organizations
• trade publications, industry news, customers, users, venders,
suppliers, and professional ORG.
– Investors and government agencies
• Securities analysts, industry data, government agencies and
litigation information source.
2.What your competition is talking about themselves?
– Public
• Advertising, promotional material, articles, employment
advertisements and press release.
– Trade and professional organizations
• Licenses, manuals, patents, and trade shows.
– Investors and government agencies
• Annual reports, stock issues, and annual meetings
13
 Open and Distributed Systems
• The use of open systems ensures that employees can
obtain information they need from any place and at any
time.
• Adherence to industry standards ranging from HTML,
XML, TCP/IP protocols, and ODBC may help you to
implement the KM system quickly, and easily extend and
customize it in the future.
• Content might be distributed across multiple platforms,
devices, servers, and locations, the ability of the
knowledge management system to build upon this
characteristic is crucial.

14
 Knowledge Aggregation and Mining
• Anyone who has used a search engine on the
Internet can tell you, simple keyword searches often
result in a meaninglessly large number of hits.
• To save users from this, a well-designed KM system
should include a mechanism to cluster search results
appropriately in different prespecified content
categories.

15
 From Skills Databases to Knowledge
Directories
• Skills databases Help to locate subject mater experts
both within and outside their organizational bounds.
While such a mechanism is useful, it needs to be kept
up to date. Example- Microsoft
• A knowledge directory takes the concept underlying
skills databases one step further by linking people to
their skills, experiences, know-how, insights, and
contributions to discussions and debates within the
knowledge management system.

16
 Automated Categorization
• Categorization need not be a manual procedure
and often can be accomplished, in part, by
knowing the nature of the contribution, such as
its context, source, and originator.
• GrapeVine (www.grapevine.com) is an excellent
commercial tool that can help with such
categorization.
• With the availability of context, some meta data
can be tagged automatically

17
 Personalized Content Filtering and
Push Delivery
• Personalized content filtering refers to the process of
categorizing items by their content:
– Images.
– Video.
– Sound.
– Text and etc.
• A user profile defines the content types that are
relevant to each user.

18
 The Collaborative Platform
• Provides the pipeline to enable the flow of explicated
knowledge, its context, and the medium for
conversations
• Provides a surrogate channel for defining , storing,
moving and linking digital objects, such as conversation
threads that correspond to knowledge units
• Enables the content of the KM system with a high
degree of flexibility to make it meaningful, useful, and
applicable across the many possible contexts of use
(and abuse).
• Empowers the users.
• The user can either search for content or subscribe to
content

19
 Collaborative Filtering
1. Active filtering:
– Users manually define filters and pointers to interesting
content and share them across their work group.
2. Automated filtering:
– Statistical algorithms make recommendations based on
correlations between the user’s personal preferences
and content ratings.
• Firefly, GroupLens, GrapeVine, and Tapestry are some
better-known examples of such collaborative filtering
tools.

20
 Community-Centered Collaborative
Filtering
• The network of existing social relationships
between employees can be a valuable basis
for improving the collaborative filtering
process.
• Reputation, trust and reciprocity come into
the picture of collaborative process enhancers
when contributions are (optionally) signed.

21
 Meta Knowledge
• Meta knowledge implies knowing what you
know.
• Creation of meta knowledge is often
extremely context dependent and requires the
use of pattern recognition or analogical
reasoning.
• In order to extract meta knowledge from
knowledge having a KM system is a necessary
condition.

22
 Accommodating Multiple Degrees of
Context
• The significance of rich communications
channels and a high degree of interactivity
cannot be overemphasized.
• If loose social bonding exists between
potential users of the system, ensure that rich
communications are built into your KM system
as an integrated feature, not as a separate
add-on component.

23
 Technology Choices
• It is vital to consider whether that technology or that
vendor will be around for the entire life of the system.
• Will the vendor’s technology capture enough of the
market to ensure the ancillary products and services
remain available?
• Can the technology deliver the consistency that the
application requires?
• Can the technology provide the quality that the market
and your customs demand?

Example: Web provides the capability required to build a

collaborative platform on which a rich multimedia repository for
explicit knowledge and an informal communications channel for
conversation making

24
Integrative and Interactive Knowledge
Applications
• The integrative ability supports the collation
of distributed knowledge repositories
containing explicated or explicitly captured
content.
• Support for interactivity is required to allow
the integration and possible capture, analysis
or even explication of tactic knowledge of the
system’s users.

25
Integrative and Interactive Knowledge Applications

The integrative component of a KM system helps users in critically evaluating,

interpreting, and adapting knowledge to new context, domains, and applications
26
Knowledge Flow Models: Centripetality and Centrifugality
Electronic publishing follows a centrifugal model unlike KM’s centripetal model. In
electronic publishing, consumers rarely fall into the same community of practice or
work group as the authors

(Source: Tiwana, A.: Knowledge Management Toolkit, 2002) 27

 The Interactive Application
Component
• The integrative components of a KM system
primarily support codified and explicitly
captured knowledge.
• The interactive component therefore focused
on enabling interaction among people and
providing a basic channel for sharing tacit
knowledge.

28
(Based on Nonaka and Takeuchi.: Knowledge creating Companies, 1995)
(Source: Tiwana, A.: Knowledge Management Toolkit, 2002)
30
(Source: Tiwana, A.: Knowledge Management Toolkit, 2002)
(Source: Tiwana, A.: Knowledge Management Toolkit, 2002) 32
 Build or Buy?
• When you begin development, your choices are:
– To build a system in-house, using team members from
the internal IT department and the end-user
community of knowledge workers form whom the
system is being built.
– To add external consultants to strengthen the weaker
expertise areas for the option described above.
– To develop the system from scratch (not
recommended).

33
 Build or Buy?
– To buy an off-the-shelf, shrink-wrapped solution
such as Lotus Notes and customize its installation.
– To buy an off-the-shelf solution sold by a
consulting group and modify it to meet your
needs.
– To buy and combine an off-the-shelf set of
application and customize it to fit your needs.
– To build in part, and buy in part
– To combination of the above approaches.

34
 Making a build or buy decisions

(Source: Tiwana, A.: Knowledge Management Toolkit, 2002)

 Making a build or buy decisions

(Source: Tiwana, A.: Knowledge Management Toolkit, 2002)

 Performance and Scalability
• Scalability refers to the ability of the KM system to
support an increasing number of users and a higher
load of transactions.
• Scalability also affects performance of the system at
larger stage.
• Keep the following set of key performance-related
factors in mind when you deciding on the design of a
KM system:
– Plan and account for additional time delays as usage grows.
– Keep repository update time in perspective.
– Keep time delays for navigating between different parts of
the interface to a minimum.

37
 User Interface Design Considerations
• Functionality, Consistency, Visual clarity,
Navigation and control, Relevancy, Feedback.

38
 A Network View of The KM
Architecture
• The team network should not be confused with
idiosyncratic communications network in generic
sense.
• The network constitutes both the technological
network and underlying social and organizational
network in which the technology operates.
• KM system should broadly fall under the category of
shared IT applications and services within the
hierarchy of the IT infrastructure.

39
A network oriented view of KM system

(Source: Tiwana, A.: Knowledge Management Toolkit, 2002)

 Future-proofing The KM System
1. Accept the inevitability.
2. Business drivers.
3. Common standards.
4. Users.
5. Intuitive
6. Metrics and performance.
7. Legacy integration.

41
2. Developing the KM System
Topics Covered
How a knowledge management system can be
actually built along each layer.
Building Blocks: 7 Layers
1. Interface layer
2. Access and authentication layer
3. Collaborative filtering and intelligence layer
4. Application layer
5. Transport layer
6. Middleware and legacy integration layer
7. Repository layer
 The Interface Layer
• The primary point of contact between the users and
KM system content.
• The interface layer must provide a channel for tacit
as well as explicit knowledge flow.
• The essential step in tacit knowledge transfer
between people is the conversion of tacit knowledge
to information and back to tacit knowledge

(Source: Tiwana, A.: Knowledge Management Toolkit, 2002)

 Contextual Expression at the Interface
• There is a lot of context that cannot be represented well
in any type of knowledge base or repository.
• Tacit knowledge can be transferred by purely explicit
mechanism through possible explicit mechanisms through
possible explication; by purely informal mechanisms, such
as conversation, or by technological enablers, such as
electronic whiteboards that fall somewhere in between
these two extremes.

44
(Source: Tiwana, A.: Knowledge Management Toolkit, 2002)
 Platform Independence
• The use of a Web browser as a client also enables
universal access the relevant portion of the KM
system from any location or computer terminal
connected to the Web.
• Content can further be optimized to move through
low-bandwidth networks with the use of cache
memory on the client and server side, by minimizing
the depth/resolution of graphics, and by using
mobile applications written in Java.

45
 Learning From Intranets
• An intranet site must organize information
and assemble it in a consistent, logical, and
systematic manner to make it useful and
successful.
• An intranet front end must allow users to get
to the information that they need in painless
and fast manner with respect to a KM system,.

46
 Optimizing Video Content
• The essential point to keep in mind while configuring
a server for video delivery is to optimize the video
clip file itself for existing network bandwidth.
• A safe assumption to make as starting point would
be to optimize content for 60 percent of the available
bandwidth, then realign it based on actual usage
pattern.

47
 Universal Authorship
• Users working on different platforms can add content
to the overall repository, irrespective of their platforms
• Another benefit of using a Web-based front end is that
users working on different platforms can add content
to the overall repository in HTML (Hyper Text Markup
Language, most widely used language on Web format),
which is the same across all platforms.
• Therefore, a report created and posted in HTML format
by a salesperson using an Apple computer can be read
by someone using a Windows PC.
 The Access and Authentication Layer
• Some of the issues that must be addressed are:
• Access privileges: Assign rights to permit
different levels of access to data such as read-
only, write, edit, and delete capabilities
• Firewalls: Construct a firewall between the
extranet and Internet. Thoroughly test the
firewall by mock attacks
• Backups: Create backups, staging areas, and
mirror sites

49
 Virtual Private Networks

• VPNs eliminate the need for fixed point-to-point

communication lines. Instead, they operate
within a public network, such as the Internet,
but with security that is as strong as that of
more expensive, leased private lines.

50
Standards and Protocols for Expensive
Networks
• Some of the standards that have been put forth and
endorsed by some companies include the following:
• LDAP: Lightweight directory access protocol
• PPTP: Point-to-point tunneling protocol
• S/MIME. Secure Mime is a standard that lets users send
secure e-mail messages by using certificate-based
encryption and authentication
• Vcard: Virtual Card is a format for storing and presenting
contact or registration information.
• Signed Objects: Signed Objects is a format for automating
trusted software and document distribution defined by
the JavaSoft Java Archive specification.
51
 Biometrics and Other Forms of
Authentication
• Biometrics, voice recognition, and fingerprint
recognition are promising technologies that
will allow users for a company or enterprise-
wide network to get into the system in a
rather transparent manner.

52
 The Collaborative Filtering and
Intelligence Layer
• The collaborative filtering and intelligence
layer is the one that constitutes in
intelligence with in a KM system.
– The process of adding tags and meta tags to
knowledge elements, either through automated
mechanisms or manual procedures, is done at
this level.
– Intelligent agents are perhaps the best thing to
happen to A.I. In terms of viable applications to
the Web

53
From Static to Dynamic Structures
• Each document is connected to other
documents through hyperlinks.
• The links are statically contained in each
document and refer to other documents,
video files, sound files, etc., by URLs.
• Activating a hyperlink means jumping from
one document to another.

54
(Source: Tiwana, A.: Knowledge Management Toolkit, 2002)

55
 From Static to Dynamic Structures
• This approach has created other
problems:
– Navigational encumbrances:
– Extensive collaborative authoring:
– Orphan links:
– Difficult in generating complex views:

56
 Virtual Folders
• Using such a mechanism, users can reach
the same information element in
multiple ways:
– By navigating:
– By searching meta data:
– By searching content:
– By subscription:

57
 Virtual Folders
• This concept is also based on the presumption that
will not add content to the corporate repositories if it
is too complex for them to do so.
• The goal is to make it possible to add the repository
with little or no effort on the part of the user.
• Without such functionality, this work runs the risk of
being perceived as useless at code check-in/check-
out procedures that most programmers have to
unwilling follow.

58
 Automatic Full Text Indexing
• The collaborative filtering layer is
responsible for indexing content in a
manner the permits fast retrieval
through multiple search mechanisms.

59
 Automatic Meta tagging
• Meta tags can be automatically added to
documents and other content, using software
tools that are readily available.
• Such meta tags include information such as:
– Who published the document?
– When was it last modified?
– Who reviewed it?
– Who approved it?
– What is the size of document?

60
 From Client/Server to
Agent/Computing
• In the client/server setup, the network load primarily
exists between the client and the server (indicated
by more interaction lines between the client and the
server).
• On the other hand, in the agent/computing model ,
this load is shifted to the space between the agent
server.
• The overall load on the network, therefore, is
dramatically reduced.

61
 62
(Source: Tiwana, A.: Knowledge Management Toolkit, 2002)
 Benefits of agent mobility
• A mobile agent is not bound to the system on
which it is executed.
• Such and agent is free to move around the
network across multiple hosts.
• Even though it is created in one execution
environment, it can transport its state j and
code with it to the next host within the
network where it continues code execution.

63
 Mobile agent for KM
1. Mobile agents reduce network load:
2. Real-time operations:
3. Protocol encapsulation:
4. Asynchronous and autonomous execution:
5. Seamless integration and heterogeneity:
6. Mobil agents are fault tolerant:

64
 Agents and push models for
knowledge delivery
• Mobile agents embody the Internet push
model.
• Agents can disseminate news, bulletins,
warnings, notifications, and automatic
software and content updates.
• The strength that mobile agents bring to such
knowledge-centered applications lies in their
asynchrony.

65
 The Application Layer
• Application such as skills directories, yellow
pages, collaborative tools, video conferencing
software and hardware, and conventional
decision support tools are placed at his level.

66
 The Transport Layer
• TCP/IP connectivity throughout the organization.
• An up-and-running Web server.
• A POP3/SMTP or MAIL server.
• A VPN to support remote communication, access,
and connectivity.
• Support for steaming audio and video on the central
server(s).

67
 The Middleware and Legacy
Integration Layer
• The legacy integration layer provides such
connections between legacy data and existing
and new systems.
• The middleware layer provides connectivity
between old and new data formats, often
through a Web front end.

68
 The Repositories Layer
• The bottom layer in the KM system
architecture .
• Consists of operational databases, discussion
databases, Web forum archives, legacy data
digital or digitized document archives, and
object repositories.

69
 3. Prototyping and Deployment
Topics Covered
– Moving From Firefighting to Systems Deployment
– Prototyping
– Pre-RDI Deployment Methods
– The Results Driven Incremental Methodology
 3. Prototyping and Deployment
• Systems Deployment
- Besides training costs, companies almost
never budget for nontechnology costs related
to deployment and implementation of KM
systems.
- Deploying any new system is usually a learning
experience.
 Prototyping
• Prototypes are the most underused form of
rejection insurance that a development team
can ever purchase.
• Iteratively improving a system with incremental
prototypes lets the users see, touch, and feel a
system even before it is completed.
 Pilot Deployments (pilot testing)
• A pilot implementation of the KM system on a
small scale can lead to insight that might
prove to be invaluable before the full-blown
system is implemented at an enterprise-wide
level.
• A pilot test reveals significant and often
fundamental design flaws early on in the
deployment process
 Selection of a Pilot Project
• The following tips help to evaluate potential
projects and their viability as pilot projects are :
– Avoid trivial projects.
– Stay away from your company’s lifeblood.
– Favor projects with widespread visibility and
noticeable effects.
– Select a problem that the chosen piece of technology
fits well.
– Set tangible deadlines and metrics for success.
– Select a process-intensive project that can be highly
impacted by the user of a KM system
 Lessons to be learned From Data
Warehouses
• Most companies pursued investments in data
warehouses to improve the quality if information
within the organization and to improve access to
it.
• Many companies start with small versions of a
data warehouse (akin to pilot projects), usually
centered on an application or a data set.
• The danger of implementing and experimenting
with such a pilot is that its success can lead to
rapid proliferation of data marts that are
independent of one another
 Legacy Deployment Methods
• The incremental approach to systems
development and deployment assumes that
functions required of a system, such as a KM
system, cannot be known completely in the initial
stages.
• This approach suggests that developers
implement a part of the system and increment it
rapidly, as new requirement surface.
• This way, the entire system can be implemented
in increments, and changes can be made along
the way.
(Source: Tiwana, A.: Knowledge Management Toolkit, 2002)
77
 Legacy Deployment Methods
• The waterfall model, the parent of the incremental
model for systems development, was the mainstay of
the system development methodologies for years
but has recently fallen out of favor.
• The waterfall model is a bad approach to take for
implementing complex systems.
• If the feedback and learning loop are incorporated
into this model and the project is broken down into
discrete phases that build on another, it give us the
incremental approach model shown in Figure 12-2.

78
Incremental approach model

(Source: Tiwana, A.: Knowledge Management Toolkit, 2002) 79

 The Information Packaging
Methodology
• Architecture and system planning
• Design and analysis
• Technology implementation
• Deployment and metric testing

80
Fig: Information packaging technology

(Source: Tiwana, A.: Knowledge Management Toolkit, 2002)

 The information packaging spiral
methodology

(Source: Tiwana, A.: Knowledge Management Toolkit, 2002)

The Big-Bang Approach To Deployment
• Delivery equals implementation
• Develop the software system in its entirety
and implement everything at once, after the
code compiled
The Big-Bang Approach To Deployment

(Source: Tiwana, A.: Knowledge Management Toolkit, 2002)

 Enterprise level Integration: Boon or
Bane ?
• The flexibility is a boon because it offers
intensively amplified benefits and abundance
of functionality.
• But this boon is also the primary bane.
• The excessive flexibility mean that your have
to tweak to work for your company, and this
necessity changes a software introduction
initiative into an organizational change
initiative.
 The Results-Driven Incremental
Methodology
• The RDI methodology specifies that the
project be broken up into a series of short,
fast-paced development cycles coupled with
intensive implementation cycles, each of
which delivers a measurable business benefit.
• The most obvious benefit of the RDI approach
is that business benefits of the KM system can
be realized much sooner, compare with a
more traditional big bang approach.
(Source: Tiwana, A.: Knowledge Management Toolkit, 2002)
Steps Involved in the RDI Methodology
1. Objective-driven decision support:
2. Incremental but independent results:
3. Software and organizational measures
clearly laid out at each stage:
4. Intensive implementation schedules:
5. Results-driven follow-ups:
 Business Releases
• Each business release should address at least
the following questions:
– What is the targeted business result?
– What is the exact software functionality required
to achieve the results?
– How will the results be measured?
– What complementary changes are need in policies,
incentives, metrics, and procedures?

89
(Source: Tiwana, A.: Knowledge Management Toolkit, 2002)
(Source: Tiwana, A.: Knowledge Management Toolkit, 2002)
 The Traps in Selecting the Release
Sequence
• Expected success.
• Cumulative.
• Highest payoff.
• Balance of the above.

92
Process divisibility and RDI releases
• Divide the technology in such a manner that
successive increments involve the same software
modules but at a deeper level of detail
• Break the technology deployment into pieces, each
of which is implemented at the deepest level of
detail in the first round.
• The RDI methodology provides a technique that
allows for refinement of the current stock of
deployment and process knowledge in ongoing
releases.

93
 RDI’s Role in Tools and Task
Reinvention
• Reinvention of the tools, interfaces, and task
environments, such as the design or aesthetic
fit of the KM system, often goes hand in hand
with reinvention of the job itself.
• It would be ideal if the design of the KM
system were such that the interface was very
similar to the one that existed earily and the
whole process of using the KM system was
almost transparent to the user. However, this
is rarely possible.

94
 Cross-Functional Synergy
• Synergy refers to the ability of the
knowledge management system to allow
different groups of users, representing
different functional departments, to produce
results exceeding those that they would
produce working without the support of
such a system.

95
The complexities of collaboration
• The various levels of complexity that
must be figured into the design of a KM
system include:
– Logistical complexity:
– Technological complexity:
– Organizational complexity:
– Environmental complexity:

96
 Avoiding Overengineering
• Overengineering refers to the act of implementing
system functions that may never be used or adding
details that are unnecessary for deriving the desired
business results.
• Although many managers may preach incrementalism
in system deployment, they rarely practice it.
– Incrementalism and structured methodologies are viewed
as being noncomplementary
– The benefits of incrementalism are perceived to be
marginal that it seems like it is not worth the effort

97
 Developing Clear Communication
Processes
• Explains the expectations and reasoning
behind the introduction and integration of
the KM system with business process.
• This communication leaves no surprises for
the users and makes it easier for them to
accept a culture where continuous is a normal
part of work life.

98
 Human Barriers in Technology
Design
• A combination of appropriate design of
technology and complementary incentives
• An immediate reward for an employee can
compensate for an immediate effort that can
result in long term reward for the firm.
• Linking long-term goals to long-term rewards
rarely works.

99
 One Infinite Loop
• To keep a KM system kicking and alive, it
needs iterative improvements as the business
environment and accompanying processes
evolve over time.

100
 Module 6:
Knowledge Management
Inferences from data,
Data mining
Knowledge portals

KBL Srivastava
 1-Topics on learning from data
• Understand the “Learning” Concept
• How to go for data visualization
• Data Neural Networks as learning models
The Basics
Supervised and Unsupervised Learning
Business Applications
Relative fit with KM
• Association Rules- Market bases analysis, PETCO
• Classification Trees
• Implications for Knowledge Management
 Importance of learning
• Learning is an iterative process where the final
model results from the combination of prior
knowledge and newly discovered information
• Learning tools are critical for development of a
knowledge management environment.
• Data driven tools create the model based on
patterns inferred from the data.
• Thus it is important to understand the concept of
learning from data.
• The goal is to improve the quality of the
communication and decision making in the firm.
 The concept of learning and Goals of
learning
• Learning is a process of filtering ideas and transforming
them into valid knowledge having the force to guide
decisions.
– The unifying concept of learning is the specific mechanism
that helps companies determine the kind of knowledge
required for decision making.
• Goals of the learning process- knowledge that
can be used in business decision making
1. Discovering new patterns in the data
2. Verifying hypothesis formed from previously accumulated
al-world knowledge
3. Predicting future values, trends, and behavior
 The Concept of learning and
Knowledge validation
Knowledge validation is a two step process- Model validation
and consensual approval.
• Model validation involves testing the logical structure of a
conceptual or operational model for internal consistency
and assessing the results for external consistency with
the observable facts of the real world.
• Consensual approval means approval of a special
reference group or the user of the results
Two approaches to learning models-
Top down- one starts with the hypothesis derived from
observation, intuition or prior knowledge- generate ideas, develop
models and evaluate them for validation
Bottom up- no hypothesis testing, learning techniques are used
to discover new patterns by findings key relationship in the data
 Data Visualization
• Exploring the data means looking visually for groups or
trends that are meaningful and useful for the decision
maker
It includes:
• Distribution of key attributes (e.g., target attribute of a
prediction task)
• Identification of outlier points that are significantly
outside expected range of the results
• Identification of initial hypothesis and predictive
measures
• Extraction of interesting grouping data subsets for
further investigation
 Neural networks as learning models
• Modeled after the human brain network
• The technology attempt to simulate biological information
processing via massive networks of processing elements
called neurons.
• Neural nets and computers are different.
• Neural nets are neither digital nor serial, they are analog or
parallel. They learn by examples not by programmed rules
or instructions. Digital computers do not evolve.
• Neurons evaluates inputs, performs a weighted sum,
and compares result to a threshold (transfer function)
level. If sum is greater than threshold, the neuron fires.
• Interconnecting or combining neurons with other
neurons form a layer of nodes or a neural network.
 A Neuron Model
(source: Awad and Ghaziri: Knowledge Management, 2007)
 Supervised and unsupervised learning
• Supervised learning process requires a teacher
represented by a training set of examples.
• Each element in a training set is a pair of input
and desirable output.
• Network makes successive passes through the
examples and the weights adjust toward the goal
state. The network has learned to associate a set
of input patterns with a specific output.
 A Supervised Neural Network Model
Example: Neural network models predicting a firm’s bankruptcy

Source: Awad and Ghaziri: Knowledge Management, 2007)

 Unsupervised Learning (Self
Supervised)
• In unsupervised learning, no external factors
influence adjustment of the input’s weights.
• The neural networks has no advance
indication of correct or incorrect answers.
• Adjusts solely through direct confrontation
with new experience .
• This is also known as self organization.
 Business Applications
• Neural networks are applied in situations
having a need for pattern recognition, where
the data are dynamic.
• This technology have been applied in all
sector.
• Business sector has experienced significant
success in application of neural networks.
 Business Application
• Risk management Appraising commercial loan
applications
The network trained on thousands of
applications, half of which were approved and
the other half rejected by the bank’s loan
officers
From this much experience, the neural net
learned to pick risks that constitute a bad
loan
Identifies loan applicants who are likely to
default on their payments
 Business Application: Some examples
• Predicting Foreign Exchange Fluctuations:
– A set of relevant indicators were identified, then used as
inputs to a neural network
– The system was trained for exchange rates of the US dollar
against Swiss franc and Japanese yen, using data from first
6 months of 1990. Then it was tested over an 8-11= 1week
period
– Results revealed return on capital of about 20%
 Business Application
• Mortgage Appraisals:
Neural network uses the data in the mortgage
loan application
It estimates value of the property based on the
immediate neighborhood, the city, and the
country
The system comes up with a valuation for the
property and a risk analysis for the loan
 Relative fit with KM
• Neural net exhibits high accuracy and response speeds.
• High input preprocessed data is often required for
building a neural net.
• A neural net must start all over with every new
application.
• A critical condition for using neural nets in KM is the
level of knowledge needed to apply the technology. For
example in case based reasoning require a high level of
user knowledge (fast solution), but expert and rule
based knowledge requires low level of knowledge
(slow solution).
 Association Rules
• Association rules techniques generate a set of
rules to help understand relationship that may
exit in data. The main rules are as follows:
• Boolean Rule: If a rule consists of examining the
presence or absence of items, it is a Boolean Rule
• For example, if a customer buys a PC and a 14”
monitor, then he will buy a printer. Presence of
items (a PC and 14” monitor) implies presence of
the printer in the customer’s buying list
 Association Rules
• Quantitative Rule: In this rule, instead of
considering the presence or absence of
items, we consider quantitative values of
items
• For example, if a customer earns between
Rs 40,000 and Rs 50,000 and owns an
apartment worth between Rs250,000 and
Rs500,000, he will buy a Car.
 Association Rules
• Multi dimensional Rule:
• A single dimensional rule, because it refers to a single attribute,
“buying”
• If a customer lives in a big city and earns more than Rs 35,000, then
he will buy a cellular phone
– This rule involves 3 attributes: living, earning, and buying.
Therefore, it is a multidimensional rule

Source: Awad and Ghaziri: Knowledge Management, 2007)

 Association Rules
• Statements of the form. When a customer buys a
PC, in 70% of the cases he or she will buy a
printer; it happens in 14% of all purchases. This
means an association rule consisting of 4
elements:
• Rule body (condition of the rule): When a
customer buys a PC
• A confidence level: In 70 % of cases
• A rule head ( result of the rule): He or she will buy
a printer
• A support ( how often items in the rule body occur
together as a % of the total transactions): It
happens in 14% of all purchases
 Market based analysis
• Example of PETCO: GIS and business analytics)
• Petco is a leading national pet specialty retailer, with more than
1,200 Petco and Unleashed by Petco store locations nationwide.
• Petco staff members began using an Esri GIS-based site selection
solution to mitigate the risks associated with expanding its network
of stores.
• As the number of stores has grown, so have the risks of selecting
inappropriate or marginal locations or new stores that have the
potential to cannibalize the sales of existing stores.
• Petco sought to improve its ability to assess both the sales potential
for new locations and any risks. Investing in the Esri solution
provided scientific analysis that gave Petco leaders more confidence
in their decisions.
 Classification Trees
• Classification tress are powerful tools for
classification and prediction. Rules are explained
to enable people to understand or a databases is
created to see that records fall into a category.
• The concept of tree is derived from graph theory .
• A tree is a network of nodes connected by areas
called branches to avoid loops in the network.
There is a root node- starting node and the
ending nodes are called leaf nodes. These root
and leaf nodes are separated by intermediate
node organizations in layers called levels.
 Examples of Classification Task
• Predicting tumor cells as benign or malignant

• Classifying credit card transactions

as legitimate or fraudulent

• Classifying secondary structures of protein

as alpha-helix, beta-sheet, or random
coil

• Categorizing news stories as finance,

weather, entertainment, sports, etc
 Classification Trees: Rules
Goal: Classify or predict an outcome based on a set of
predictors
The output is a set of rules
Example:
• Goal: classify a record as “will accept credit card offer”
or “will not accept”
• Rule might be “IF (Income > 92.5) AND (Education <
1.5) AND (Family <= 2.5) THEN Class = 0 (nonacceptor)
• Also called CART, Decision Trees, or just Trees
• Rules are represented by tree diagrams
Source: Awad and Ghaziri: Knowledge Management, 2007)
25
 Example of a Decision Tree

Splitting Attributes
Tid Refund Marital Taxable
Status Income Cheat

1 Yes Single 125K No

2 No Married 100K No Refund
No
Yes No
3 No Single 70K
4 Yes Married 120K No NO MarSt
5 No Divorced 95K Yes Single, Divorced Married
6 No Married 60K No
7 Yes Divorced 220K No TaxInc NO
8 No Single 85K Yes < 80K > 80K
9 No Married 75K No
NO YES
10 No Single 90K Yes
10

Training Data Model: Decision Tree

 Another Example of Decision Tree

MarSt Single,
Married Divorced
Tid Refund Marital Taxable
Status Income Cheat
NO Refund
1 Yes Single 125K No
Yes No
2 No Married 100K No
3 No Single 70K No NO TaxInc
4 Yes Married 120K No < 80K > 80K
5 No Divorced 95K Yes
NO YES
6 No Married 60K No
7 Yes Divorced 220K No
8 No Single 85K Yes
9 No Married 75K No There could be more than one tree that fits
10 No Single 90K Yes the same data!
10
Decision Tree Classification Task
Tid Attrib1 Attrib2 Attrib3 Class
Tree
1 Yes Large 125K No Induction
2 No Medium 100K No algorithm
3 No Small 70K No

4 Yes Medium 120K No

Induction
5 No Large 95K Yes

6 No Medium 60K No

7 Yes Large 220K No Learn

8 No Small 85K Yes Model
9 No Medium 75K No

10 No Small 90K Yes

Model
10

Training Set
Apply Decision Tree
Tid Attrib1 Attrib2 Attrib3 Class
Model
11 No Small 55K ?

12 Yes Medium 80K ?

13 Yes Large 110K ?

Deduction
14 No Small 95K ?

15 No Large 67K ?
10

Test Set
 Apply Model to Test Data
Test Data
Start from the root of tree. Refund Marital Taxable
Status Income Cheat

No Married 80K ?
Refund 10

Yes No

NO MarSt
Single, Divorced Married

TaxInc NO
< 80K > 80K

NO YES
Decision Tree Classification Task
Tid Attrib1 Attrib2 Attrib3 Class
Tree
1 Yes Large 125K No Induction
2 No Medium 100K No algorithm
3 No Small 70K No

4 Yes Medium 120K No

Induction
5 No Large 95K Yes

6 No Medium 60K No

7 Yes Large 220K No Learn

8 No Small 85K Yes Model
9 No Medium 75K No

10 No Small 90K Yes

Model
10

Training Set
Apply Decision Tree
Tid Attrib1 Attrib2 Attrib3 Class
Model
11 No Small 55K ?

12 Yes Medium 80K ?

13 Yes Large 110K ?

Deduction
14 No Small 95K ?

15 No Large 67K ?
10

Test Set
 2- Data Mining
• Topics covered
- Defining data mining
- Data mining and business intelligence
- Business drivers
- Technical drivers- Role of statistics, machine learning, data
warehouses, OLAP (on line analytical processing), DM architecture
- DM virtual cycle – business understanding and developing DM
application
- Data management- sources, taxonomy, preparation, model
building, parameter setting, tuning, deployment and post
deployment phase action
- DM in practice
- Role of DM in customer relationship management
- Application for KM
 Data Mining
• A body of scientific knowledge accumulated
through decades of forming well-established
disciplines, such as statistics, machine learning,
and artificial intelligence
• A technology evolving from high volume
transaction systems, data warehouses, and the
Internet
• A business community forced by an intensive
competitive environment to innovate and
integrate new ideas, concepts, and tools to
improve operations and DM quality
 Data mining
• Data mining (knowledge discovery in databases):
– Extraction of interesting (non-trivial, implicit, previously unknown and
potentially useful) information or patterns from data in large
databases

• Alternative names and their “inside stories”:

– Knowledge discovery(mining) in databases (KDD), knowledge
extraction, data/pattern analysis, data archeology, business
intelligence, etc
 Why data mining- motivation
• Data explosion problem
– Automated data collection tools and mature database technology lead
to tremendous amounts of data stored in databases, data warehouses
and other information repositories
• We are drowning in data, but starving for knowledge!
• Solution: Data warehousing and data mining
– Data warehousing and on-line analytical processing
– Extraction of interesting knowledge (rules, regularities, patterns,
constraints) from data in large databases
 Why Mine Data? Commercial Viewpoint

• Lots of data is being collected

and warehoused
– Web data, e-commerce
– purchases at department/
grocery stores
– Bank/Credit Card
transactions

• Computers have become cheaper and more powerful

• Competitive Pressure is Strong
– Provide better, customized services for an edge (e.g. in Customer
Relationship Management)
 Why Mine Data? Scientific Viewpoint

• Data collected and stored at

enormous speeds (GB/hour)
– remote sensors on a satellite
– telescopes scanning the skies
– microarrays generating gene
expression data
– scientific simulations
generating terabytes of data
• Traditional techniques infeasible for raw data
• Data mining may help scientists
– in classifying and segmenting data
– in Hypothesis Formation
 Example of data mining
• Database-
- Find all credit applicants with last names of Sinha.
- Identify customers who have purchased more than 30000
rupees in the last months
- Find all customers who have purchased chocolate ice-creams.
- Data Mining-
- Find all credit applicants with poor risk (classification)
- Identify customers with similar buying habits (clustering)
- Find all items which are frequently purchased with ice cream
( association rules).
 Data Mining: Classification Schemes

• Decisions in data mining

– Kinds of databases to be mined
– Kinds of knowledge to be discovered
– Kinds of techniques utilized
– Kinds of applications adapted
• Data mining tasks
– Descriptive data mining
– Predictive data mining
 Decisions in Data Mining

• Databases to be mined
– Relational, transactional, object-oriented, object-relational, active,
spatial, time-series, text, multi-media, heterogeneous, legacy, WWW,
etc.
• Knowledge to be mined
– Characterization, discrimination, association, classification, clustering,
trend, deviation and outlier analysis, etc.
– Multiple/integrated functions and mining at multiple levels
• Techniques utilized
– Database-oriented, data warehouse (OLAP), machine learning,
statistics, visualization, neural network, etc.
• Applications adapted
– Retail, telecommunication, banking, fraud analysis, DNA mining, stock market
analysis, Web mining, Weblog analysis, etc.
 Data Mining Tasks

• Prediction Tasks
– Use some variables to predict unknown or future values of other
variables
• Description Tasks
– Find human-interpretable patterns that describe the data.

Common data mining tasks

– Classification [Predictive]
– Clustering [Descriptive]
– Association Rule Discovery [Descriptive]
– Sequential Pattern Discovery [Descriptive]
– Regression [Predictive]
– Deviation Detection [Predictive]
Data Mining Models and Tasks
 Data mining and business intelligence
• Data mining helps in producing new knowledge and
discovering new patterns to describe the data using
intelligent automated systems.
• BI is a global processes, techniques, and tools that
support business decision making based on IT.
• Approaches can range from a simple spreadsheet to
an advanced decision support system
• DM and the three bodies of knowledge-

Business
Scientific Commu Information
Knowledge nity Technology

Source: Awad and Ghaziri: Knowledge Management, 2007)

Data mining as a component of BI

Source: Awad and Ghaziri: Knowledge Management, 2007)

 Business Drivers of data mining
• Competition. Successfully competing in today’s
economy requires an understanding of customer needs
and behavior and flexibility to respond to market
demands and competitors' challenges
• Information glut. Today’s manager is confronted with
increasingly large volumes of data, collected and stored
in various databases and constitutes a challenge for
decision making
• Serving knowledge workers efficiently. Databases
provide the firm with transactional memory. Memory
is of little use without intelligence--capacity to acquire
and apply knowledge
 Knowledge worker types
and needs
Type Need
• Knowledge analysts • Sophisticated tools for their
investigations and research
analysis

• Knowledge users • Review and analysis of data

• Easy access to information

• Knowledge consumers

Source: Awad and Ghaziri: Knowledge Management, 2007)

 Technical Drivers
• Objective of DM is to optimize use of available data
and reduce risk of making wrong decisions

• Statistics and machine learning considered the

analytical foundations upon which DM was
developed
• ROLE OF STATISTICS
With databases organizing data records of hundreds of
attributes, the statistics methodology of the hypothesize-
and-test paradigm becomes a time-consuming process. DM
helps automate formulation and discovery of new
hypotheses

• MACHINE LEARNING
A scientific discipline considered a sub-field of artificial
intelligence. In contrast, data mining is a business process
concerned with finding understandable knowledge from
very large real-world databases
• DATA WAREHOUSES
Extracting and transforming operational data into
informational or analytical data and loading it into a
central data warehouse
• Major features of DW:
– Subject oriented- Organized around subjects such as customers
vendor’s products compared to operational systems organized
around business functions
– Integration- data are loaded from different operation system
may be inconsistent, and integration help in brining consistency
in naming and measuring the variables
– Time variant- data may be collected from different time period
– Nonvolatile- no redundancy between operational data houses
and data warehouses
 OLAP (Online Analytical Processing)
• Uses computing power and graphical
interfaces to manipulate data easily and
quickly at user’s convenience
• Focus is showing data along several
dimensions. Manager should be able to drill
down into the ultimate detail of a transaction
and zoom up for a general view
• Strengths- Visualization, easy to use
interactive tool, helps to understand the data
• Limitations- does not find pattern
automatically, not a powerful analytical tool.
 Data Warehouse and DM Technological
Framework:

Source: Awad and Ghaziri: Knowledge Management, 2007)

 Evolution of the Decision Making Architecture
• Integration of decision processing into the overall business process
achieved by building a closed-loop system, where decision
processing applications output delivered to users in the form of
recommended actions
• In the e-business environment, many companies look to extend
closed-loop processing to automatically adjust business operations
based on messages generated by a decision engine
• Conventional approach to corporate decision making-

Source: Awad and Ghaziri: Knowledge Management, 2007)

DM in the Context of the New Corporate
Business Model

Source: Awad and Ghaziri: Knowledge Management, 2007)

 DM Virtuous Cycle
• Harnessing power of data and transforming it
into added value for the entire organization

• Capabilities:
Response to extracted patterns
Selection of the right action
Learning from past actions
Turning action into business valu
 DM Virtuous Cycle

Source: Awad and Ghaziri: Knowledge Management, 2007)

 BUSINESS UNDERSTANDING
• The first step in the virtuous DM cycle is
identifying the business opportunity--defining
the problems faced by the firm
• The goal is to identify areas where data can
provide values
• Defining the problem should involve technical
and business experts
 DEVELOP THE DM APPLICATION
• Define the adequate data-mining tasks
• Organize data for analysis
• Use the right DM technique to build the data
model
• Validate the model
 DM tasks
Clustering:
• Clustering finds groups that are very
different from each other, but whose
members are similar to each other
• One does not know what the clusters
will be at the start or by what attributes
the data will be clustered
 DM tasks
Classification
The classification function identifies
characteristics of the group to which each case
belongs

Affinity grouping
A descriptive approach to exploring data that
can help identify relationships among values in
a database. The two most common approaches
are:
• Association discovery
• Sequence discovery
 Different Industries and the DM Goals
DM application in Customer services
Business Challenge--understanding that individual
preferences of customers is the key to satisfying them

DM Goals
• Customer acquisition profile • Customer retention profiling
• Customer-centric selling • Inquiry routing
• Online shopping • Scenario notification
• Staffing level prediction • Web mining for prospects
• Targeting market
 DM application in Financial services
industry
Business Challenge--Retaining customer loyalty
is of the utmost importance to this industry

DM Goals
• Focused statistical and DM applications are
prevalent
• Risk management for all types of credit
and fraud detection
 DM application in Health-care business
Business Challenge
• Keeping pace with rate of technological and
medical advancement
• Cost is a constant issue in ever-changing market
DM Goals
• Early DM activities have focused on financially
oriented applications
• Predictive models have been applied to predict
length of stay, total charges, and even mortality
DM application in Telecommunications industry
Business Challenge
• Keeping pace with rate of technological change
• Deregulation is changing business landscape, resulting
in competition from a wide range of service providers
• Finding and retaining customers
DM Goals
• Customer profiling
• Subscription fraud and credit applications are utilized
throughout the industry
• Concerns about privacy and security are likely to result
in DM applications targeted to these areas
 Data Management
• The most challenging part of a DM project is the data
management stage
• At least 40 percent of a DM project is spent on this
stage
Sources of data:
➢ Flat files
➢ Relational databases
➢ Data warehouses
➢ Geographical databases
➢ Time series databases
➢ World Wide Web
 TAXONOMY OF DATA
Data can be found in several forms:

➢Business transactions
➢Scientific data
➢Medical data
➢Personal data
➢Text and documents
➢Web repositories
 DATA PREPARATION
➢Evaluating data quality
➢ Handling missing data
➢ Processing outliers
➢ Normalizing data
➢ Quantifying data
 MODEL BUILDING
The first step is to select the modeling
technique.
The most popular techniques:
• Association rules
• Classification trees
• Neural networks
 PARAMETER SETTINGS AND TUNING
➢The set of initial and intermediate parameters
must be recorded for eventual use and
comparison
➢The selection of parameters must also be
explained
➢The process of reaching the final values must
be documented
➢Testing and validating samples are used for
this task
 MODEL TESTING AND ANALYSIS OF
RESULTS
➢Reviewing business objectives and success
criteria
➢Assessing success of the DM project to ensure
all business objectives have been incorporated
➢identifying factors that have been overlooked
➢Understanding data-mining results
➢Interpreting the results
➢Comparing results with common sense and
knowledge base to detect any worthwhile
discoveries
 TAKING ACTION AND DEPLOYMENT
➢Summarizing deployable results
➢Identifying users of the discovered knowledge
and finding out how to deliver and propagate
it
➢Defining a performance measure to monitor
obtained benefits by implementation of DM
results
 DM Applications
• Market management applications
• Sales applications
• Risk management applications
• Web applications
• Text mining
DM as a Required Component for Various Applications in Different
Departments

Source: Awad and Ghaziri: Knowledge Management, 2007)

 INTEGRATING DM, CRM, AND
E-BUSINESS
• DM applications are the first line in
understanding the customer and an integral
key to segmenting the market
• An intelligent e-business system enhances
CRM by enabling a level of responsiveness and
proactive customer care not achievable
through other channels
• Through personalization, corporations can
build successful 1-to-1 relationships with
customers
Customer-Centric Data Warehousing Enables Next
Generation E-Commerce

Source: Awad and Ghaziri: Knowledge Management, 2007)

 Implications for Knowledge
Management
• A DM project is definitely not a
straightforward project
• While conducting such a project, companies
may face many problems, obstacles, and
pitfalls that prevent them from gaining returns
on investing in a DM project
 Data-Mining Challenges

Source: Awad and Ghaziri: Knowledge Management, 2007)

Insufficient understanding
of business needs

• Companies should have a business

justification for DM
• The organization will only reap the benefits of
DM if there is a real business case to answer
• It should not be an experiment for
experiment’s sake
 Careless handling of data
• Overquantifying data
• Miscoding data
• Analyzing without taking precautions against
sampling errors
• Loss of precision due to improper rounding of
data values
• Incorrectly handling missing values
Invalidly validating the data-mining
model
• Most data miners will face either abundant
amounts of testing data or extremely scare
amounts
• It is important to never ignore suspicious
findings
• Haste can lead to believing everything data
owners tell us about the data and, even
worse, believing everything our own analysis
tells us
 Believing in alchemy

• Many data owners believe that data mining is a form of an

alchemic process that will magically transform their straw
databases into golden knowledge
• Failure may come from wrong suspicion, technology
misapplication, or unsuitable information
• Managers should consider:
– Select the right application
– In-house development or outsourcing
– Assessing vendor selection
– Team building
 3. Knowledge Portals
• Topics Covered-
- Basics of portal- evolution and key characteristics
- The business challenges – transforming business,
market potential
- Knowledge portal technologies- collaboration,
content management, intelligent agent
- Implications for KM- building enterprise portal,
sponsorship, bandwith, and portal product
selection
 Portals: The Basics
Portals are considered to be virtual workplaces that:
• Promote knowledge sharing among different categories of end users
• Provide access to stored structured data
• Organize unstructured data
Portals are tool that could:
Simplify access to data stored in various application systems
Facilitate collaboration among employees
Assist the company in reaching its customers
Knowledge portals
Allow producers and users of knowledge to interact.
Knowledge portals provide two kinds of interfaces:
The knowledge producer interface
The knowledge consumer interface
 EVOLUTION OF PORTALS
• Search engines
• Navigation sites
• portals evolved to include advanced search capabilities and
taxonomies
• Evolution of the Portal Concept:
 Knowledge Portals Versus Information Portals

Enterprise Information Enterprise Knowledge Portals

Portals  Are goal-directed toward
 Use both “push” and “pull” knowledge production,
technologies to transmit knowledge acquisition,
knowledge transmission, and
information to users through a
knowledge management
standardized Web-based interface
 Are focused on enterprise
 Integrate disparate applications business processes
into a single system  Provide, produce, and
 Have the ability to access both manage information about the
validity of the information
external and internal sources of
they supply
data  Include all EIPs
functionalities
 Business Challenges of knowledge
portals
• To optimize the performance of operational
processes in order to reduce costs and
enhance quality
• Companies need to commercialize their
products at the lowest price possible
 Portals and Business Transformation
• The explosion of key business information
captured in electronic documents
• The speed by which the quantity and kinds of
content is growing
• Challenges:
– Shorter time to market
– Knowledge worker turnover
– More demanding customers and investors
 Why Organizations Launch
KM Programs

Source: Awad and Ghaziri: Knowledge Management, 2007)

The Benefits of Knowledge Portals

Productivity E-mail Traffic

Locating Documents Bandwidth Use
Collaboration Time in Meetings
Better Decisions Phone Calls
Quality of Data Response Times
Sharing Knowledge Redundant Efforts
Identifying Experts Operating Costs
Time to market
 Knowledge Portals components and
technologies
• Components:
- Content management
- Business intelligence
- Data warehouses and data marts
- Data management
• Technologies:
-Gathering
- Categorization
- Distribution
- Collaboration
- Publish
- Personalization
- Search/navigate
 Portal Features and Benefits

Source: Awad and Ghaziri: Knowledge Management, 2007)

 Types of collaborations
• Asynchronous collaboration is human-to-human
interactions via computer sub-systems having no time or
space constraints. Queries, responses, or access occur
anytime and anyplace.
• Synchronous collaboration is computer-based, human-to-
human interaction that occurs immediately (within 5
seconds). It can use audio, video, or data technologies.

Another distinction:
Push technology places information in a place where it is
difficult to avoid seeing it.
Pull technologies require you to take specific actions to
retrieve information
 Layers of The Portal Architecture

Source: Awad and Ghaziri: Knowledge Management, 2007)

 Requirements for Successful
Collaboration Tools
• Comfortable e-mail systems
• A Web browser
• Simple search functionalities
• Collaboration services with a multipurpose
database
• Web services
• Indexing services for full-text search of
documents
• Well-organized central storage locations
 Synchronous and Asynchronous collaboration
Synchronous collaboration Asynchronous collaboration
• Teleconferencing • Electronic Mailing Lists
– Advantages: personal, immediate – Advantages: cheap
feedback – Disadvantages: limited
– Disadvantages: expensive, often communication medium
does not work well across time
zones • Web-Based Discussion Forums
• Computer Video/ – Advantages: same as electronic
Teleconferencing mailing lists except requires
– Computer-based slightly faster Internet connection
teleconferencing and video- – Disadvantages: cultural resistance
conferencing is a rapidly • Lotus Notes
evolving technology that has
tremendous potential for – Advantages: comprehensive
distributed organizations. collaborative solution employing
• Online Chat Forums state-of-the-art technologies for
– Allow multiple users to
communicate simultaneously by communication, document
typing messages on a computer management, and work flow
screen. – Disadvantages: expensive to
deploy when compared with other
collaboration technologies
The World Bank Case: KM Architecture at the
 The World Bank World Bank
spent a fortune on
classifying
knowledge.
 The bank employs
XML–enabled
Oracle data engine
to drive a document
management
system linked to
Lotus
Notes groupware.
 Codification
technologies needs
to be evaluated in
terms of a return on
investment

Source: Awad and Ghaziri: Knowledge Management, 2007)

 Intelligent Agents
• Intelligent agents are tools that can be applied
in numerous ways in the context of EKPs.
• Intelligent agents are still in their infancy.
• Agents are software entities that are able to
execute a wide range of functional tasks
 Intelligent Agents Services
• Customized customer assistance with online
services
• Customer profiling based on business
experiences
• Integrating profiles of customers into a group
of marketing activities
• Predicting customer requirements
• Negotiating prices and payment schedules
• Executing financial transactions on the
customer’s behalf
New Trends in Portals Technologies

Source: Awad and Ghaziri: Knowledge Management, 2007)

 Critical Issues for
Knowledge-Sharing Programs
• Responsiveness to user need
• Content structure. In large systems
• Content quality requirements
• Integration with existing systems
• Scalability
• Hardware–software compatibility.
• Synchronization of technology with the
capabilities of users.
 Portal Vendors

Source: Awad and Ghaziri: Knowledge Management, 2007)

 Portal Vendors

Source: Awad and Ghaziri: Knowledge Management, 2007)

 Module 7:
Knowledge Management
Evaluation of KM effectiveness: Tools
and metrics
Ethical, legal, and managerial issues
1- Evaluation of KM effectiveness: Tools and
metrics
Topics covered-
Return on investment for KM investments.
• Benchmarking as a comparative knowledge metric.
• Evaluating KM ROI by using the balanced scorecard (BSC)
method.
• Use quality function deployment for creating strategic
knowledge metrics.
Alternative metrics: Skandia and FASB
 Traditional metrics: Financial ROI ( return
on investment) and Tobin’s q
• Tobin's q measures the ratio between the firm's market
valuation and the cost of replacing its physical assets.
• It does not tell how it can create further value, prevent
imitation or substitution, and leverage its knowledge
assets to gain a sustainable competitive advantage.
• Measuring returns on investment in KM, two
conventional approaches are in common use: putting a
monetary figure on intellectual assets, and determining
the money saved or earned by using existing knowledge.
 Total Cost of Ownership
• This methodology identifies and measures components
of IT expense beyond the initial cost of implementation.
Drawbacks:
• It leaves out significant cost categories, such as
complexity costs.
• It ignores benefits beyond pure costing.
• It neglects strategic factors.
• It provides little or no basis for comparison with other
department and other companies, such as competing
firms operating in the same markets.
• Life cycle costs are difficult to gauge
 Learning From the Phone:
Justifying the cost
• It is hard to cost-justify and evaluate for a phone.
Similarly, Firms find it difficult to cost justify KM
in the face of other need investments but is
something they want and should have.
• Middle managers feel the need for a strong KM
initiative, convincing senior management to shell
out the couple of million rupees for an initiative
with intangible results can be hard sell.
 Two ways to measure cost
• The short-term gains to demonstrate the need for,
and the extent of the longer-term guess
estimations of value added by KM to the firm's
bottom line and competitive standing.
• Cost based approach-Did it reduce costs? Did we
accomplish more by spending the same?
• Market-value-based approach- improve market
leadership, bring more stability to the company,
increase market share or stock value
• Effect-on-income approach- effect on expense
reduction, customer retention, repeat business,
profit margins, bottom line.
• put a monetary value on the company's intellectual
assets on KM investments
 The Metric is the Limitation
• A recurring problem is posed by a lack of
standard metrics for measuring the impact of
KM.
• No metrics is better than one that is absolutely
wrong.
• A choice of a wrong metric can have more ill
effects than positive ones.
• Metrics, when applied to knowledge work or in
general, are vulnerable.
 Common Traps In Choosing Metrics
• Trap1: Using Too Many Metrics
– A few robust metrics are better than a number of
marginally ones.
– They need to focus on the past, present, and future
simultaneously to be able to relate past performance,
present processes, and future results.
– Use 20 as a cutoff rule of thumb number for the few but
essential metrics that can be simultaneously tracked.
• Trap 2: the Consequences of Delayed Rewards
– Delayed rewards will only bias employees to work toward
metrics that deliver short-term payoffs to them
Common Traps In Choosing Metrics
• Trap 3: Metrics That Are Hard to Control
– Companies often make the grave mistake of
implementing metrics that are beyond the control of
their employees.
• Trap 4: Metrics That tear People away from Business Goal
– The key idea is that the metrics that you select must encourage
individual decisions that also move your company in the same
directions as its long-term goals.
– Some metrics might seem reasonable, but when they are put
into action, they result in counterproductive consequences.
– Many companies hard financial results while neglecting or
ignoring soft results such as employee attitude and
behaviour.
With a good set of lean metrics decisions that improve them are the same
decisions that improve the company’s desired long term outcome

Source: Tiwana, A.: Knowledge Management Toolkit (2002)

 Agency Agent Conflict
• A manager or employee will maximize the
metrics that are actually measured.
• If a manager is told that a high market share
for a product, even though quality (not
measured) might be equally important.
Are the right things not measured
What is desired What is maximized

Source: Tiwana, A.: Knowledge Management Toolkit (2002)

 Real-options analysis
• Real option analysis can reduce uncertainty and
help quantify expected outcomes and risks.
• The strength of options-based analysis lies in its
ability to account explicitly for the value of
flexibility for which traditional metrics cannot
account.
• This approach befriends uncertainty that other
approach fear.
• This approach also encourages managers to think
of every investment in KM as an initial investment
against a unexpected innovation, or regulatory
change.
 Real-options analysis
• A KM project results in an initial cost that is fixed and irrecoverable.
In addition, each increment adds some variable cost to the picture

Source: Tiwana, A.: Knowledge Management Toolkit (2002)

 The option space
• The ratio of the net value to the sum total of such costs for each
independent and decomposable investment is the starting point for
options-based analysis-

Source: Tiwana, A.: Knowledge Management Toolkit (2002)

 The option space
• The option space can further be divided into a half-
dozen segments that represent relative differences,
compared with the adjacent segments.

16
Source: Tiwana, A.: Knowledge Management Toolkit (2002)
 KM investment as portfolio of options
• A series of investment in a KM initiative can be
though of as a series of options that build
toward a portfolio.
• Each investment might have a different level
of risk, strategic intent, and time to fruition.
• The goal is to nurture and manage a KM
initiative as portfolio of well-balanced
investments.
• Real-options analysis can allow manager to
think several moves ahead of their present
investments.
Impact of risk, strategic intent, and time to fruition

Source: Tiwana, A.: Knowledge Management Toolkit (2002)

Value to cost ratio across projects and volatility

Source: Tiwana, A.: Knowledge Management Toolkit (2002)

 Measuring Inputs for Real-Options
Models
• Benchmarking.
• The Benchmarking Process
• Benchmark Lessons
• House of Quality and Quality Function
Deployment
• The Balanced Scorecard Technique
 Benchmarking
• Many large firms have adopted benchmarking as a
significant, systematic technique for measuring the
company’s performance toward its strategic goals.
• Benchmarking can also provide insights into areas
such as:
– Overall productivity of knowledge investments
– Service quality
– Customer satisfaction and operational level of customer
service
– Time to market in relation to other competitors
– Costs, profits, and margins
– Relationships and relationship management
 Benchmarking
• The wise learn many things from their
enemies
– By benchmarking your own business against your
competitor’s, you get information on how to
tweak your company’s performance goals to stay
competitive, in relation to your competitors
– Benchmark Targets
Source: Tiwana, A.: Knowledge Management Toolkit (2002)
• Spendolini has suggested a five-step procedure
for benchmarking efforts. An adaptive version of
this process applied to knowledge work.
• The benchmarking process can be used for self-
comparison.
Benchmark Lessons-
1. Make it valuable.
2. Make it rare.
3. Make it hard to copy.
4. Make it hard to substitute.
Prevalent role models in the benchmarking process

Source: Tiwana, A.: Knowledge Management Toolkit (2002)

Steps of Benchmarking Process

Source: Tiwana, A.: Knowledge Management Toolkit (2002)

Steps of Benchmarking Process

Source: Tiwana, A.: Knowledge Management Toolkit (2002)

 Quality Function Deployment
• The House of Quality approach was developed
by Hauser and Clausing in an original paper
that appeared in the Harvard Business Review.
• The use of this technique is commonly
referred to as Quality Function Deployment
(QFD).
29

Source: Tiwana, A.: Knowledge Management Toolkit

(2002)
 House of Quality Metrics Matrix
• Be careful to select outcome that are clearly
observable without much delay.
• Examples of such outcomes include:
– Improve knowledge sharing to a level where 20% of an
average employee’s work is based on existing knowledge
– Speed up problem solving by a factor of 5% over the next
six months
– Improve quality such that the rate of failure of product X
decrease by 15% within the next 12 months
– Generate more conversation among employees
– Increase customer satisfaction level by 50%
The Balanced Scorecard Technique
• The Balance Scorecard provides a technique to
“maintain a balance between long-term and
short-term objectives, financial and
nonfinancial measures, lagging and leading
indicators and between internal and external
perspectives
 32
Source: Tiwana, A.: Knowledge Management Toolkit (2002)
 The KM Balance Scorecard
1. Translate the KM vision
2. Communicate and link
3. Do a reality check
4. Incorporate learning and feedback
 34
Source: Tiwana, A.: Knowledge Management Toolkit (2002)
 35
Source: Tiwana, A.: Knowledge Management Toolkit (2002)
Source: Tiwana, A.: Knowledge Management Toolkit (2002)
Advantages of KM balanced scorecards
• The ability to provide a snapshot of the
intellectual health of your firm at any point in
time.
• Built-in cause-and-effect relationships that can
help you guide your KM strategy.
• A sufficient number of performance drivers and
metrics.
• Capability to communicate the KM strategy
throughout the firm.
• Capability to link individual goals with the overall
knowledge strategy of the firm
Advantages of KM balanced scorecards
• A direct, and often missing link between long-
term knowledge and competence goals of the
firm and its annual budget.
• Translation of the lofty visions of a firm into
more doable, realistic, manageable, and
specific performance goals.
• Logical integration into the overall strategy of
your business while still making sense
Advantages of KM balanced scorecards
• Objective measurement of the contribution of
knowledge to the more intangible source of
competitive advantage, such as customer
satisfaction and employee skills and
competencies.
• A direct link to financial measures and your
KM system’s effect on the company bottom
line.
Limitations of KM Balanced Scorecards
• On the downside, a well-designed Balance
Scorecard is more difficult to develop than a
similar quality function (QFD) model.
• It is rarely possible to adopt directly another
firm’s Balance Scorecard because subtle
differences exist even between very similar
firms
 Alternative Metrics
• The Skandia Method.
• The FASB Method.
 Skandia: The Early Pioneer
• Skandia - a Fortune 500 Swedish insurance and finance
company
– one of the first companies to issue an Intellectual Capital Report as a
supplement to its Annual Report for shareholders (1994)
– Leif Edvinsson
• as one of the first ever directors of intellectual capital in a firm, was the
principal architect of Skandia’s initiative
• intellectual capital guru
• developer of an IC reporting model called the Navigator
 The Skandia Model (adapted from Roos et al, 1997)
Market Value

Financial Capital Intellectual Capital

value of all physical and monetary assets

Human Capital Structural Capital

‘thinking’ ‘non-thinking’
• competence (knowledge and skills)
• attitude (motivation, behaviour, conduct)
• intellectual agility (innovation, imitation, adaptation)

Customer (Relationship) Organisational Capital

Capital • infrastructure
customers, suppliers, shareholders, • processes
alliance partners, other stakeholders • culture

Innovation Capital
renewal and development value Process Capital

Intellectual Property
Intangible Assets
 Edvinsson’s Navigator

HISTORY
FINANCIAL CAPITAL

CUSTOMER PROCESS
HUMAN CAPITAL TODAY
CAPITAL CAPITAL
IC

RENEWAL AND DEVELOPMENT CAPITAL TOMORROW

 Skandia IC Measures (from Bontis, 2001)

Skandia’s value scheme therefore contains both

financial and non-financial elements to
estimate the company’s market value.
Skandia uses 91 new IC measures (or metrics)
along with 73 traditional (accounting)
measures in the five focus areas.
 Strengths of the Navigator model

• One of the first attempts to create a taxonomy to

measure intellectual capital

• Recognises the importance of

– customer capital
– organisational attributes (e.g. its processes and
development)
in creating value for an organisation
 The FASB Method
• Established in 1973, the Financial Accounting Standards
Board (FASB) is the independent, private-sector, not-
for-profit organization establishes financial accounting
and reporting standards for public and private
companies and not-for-profit organizations that follow
Generally Accepted Accounting Principles (GAAP).
• The mission is to establish and improve financial
accounting and reporting standards to provide useful
information to investors and other users of financial
reports and educate stakeholders on how to most
effectively understand and implement those standards
Recommendations for KM Assessment
• Why doing KM
• Establish a baseline
• Also Consider qualitative approaches along
with quantitative approaches
• Avoid KM metrics that are hard to control
• Measure at the appropriate level
• Link reward to KM assessment results
• Be conservative in your claims
2. Ethical, Legal and Managerial Issues
– Topics Covered-
– Knowledge owners
– Legal issues- liability, basis of liability, copyrights
trademarks, trade names, warranties, strict
liability, Legal disputes in KM, The malpractices
factor
– The ethical factor- Ethical decision cycle, threats to
ethics
– Improving the climate- code of ethics, Privacy
factors
– Challenges
– Implications for KM
 Issues
• Who is the custodian of the company’s
knowledge base.
• How to manage company’s sensitive knowledge
• How to be the corporate conscience of
knowledge
• How to handle tough corporate questions
regarding the consequences of knowledge based
questions.
• What are the ethical legal dilemmas faced by the
company in KM
 Knowledge Owners
• Your knowledge is your own when transferred
from parents or from one craftsman to another
through apprenticeship
• In a corporate environment, owners of
knowledge are the expert, the company, and the
user who acquires the knowledge automation
system.
• Knowledge ownership may be an issue if
- an expert is selling his personal knowledge
- If an expert is unwilling to release his knowledge
gained on the job.
 Releasing Knowledge Gained on the
Job
• Unless an intellectual property agreement is
signed in advance, one’s knowledge on the job
is his or her own
• Ideally, companies have the expert sign a pre-
employment contract, releasing his knowledge
gained during employment to the employing
organization
 Legal Issues
• Regardless of where knowledge originates,
when it is misused or misrepresented, liability
will become an issue
• If a knowledge repository produces the wrong
solution, which causes losses or injury to
others, it triggers litigation
• Users and developers should be aware of legal
ramifications arising from knowledge sharing
and automation.
 Some examples of legal issues
• A physician diagnoses a patient after consultation with
his knowledge based system for treatment, but the
patient dies as a result of misdiagnosis and treatment
• A knowledge based system used by an architect
incorrectly determines the stress requirement of a new
building and later on it collapses killing people.
• A lawyer using a knowledge base legal system advises
his client of the tax forms to file and what to include in
his return to get tax exemption. The client is later
issued notice by the IT department for wrong
information.
 Liability question
• Any knowledge that is misused is a liability.
• The blame may be on knowledge developer
who might have tapped wrong knowledge.
• It may be with repository that produces the
wrong solution
• In day to day business operations tort and
contract laws pose challenges for
organizations and legal community.
 Liability of the Knowledge Developer

• The developer is vulnerable to charges of

personal liability under the doctrine of
respondeat superior
– If the designer is an employee of the company
that sells the software, the firm is involved in the
negligence action
– Either way, the company is responsible for
certifying the system before it is released for
commercial sale
 Liability of the Expert
• Expert involvement and potential liability vary,
since limited cases have been litigated
• If the knowledge automation system is faulty
due to poor expert advice, litigation is bound
to follow
• Experts open up their knowledge to scrutiny,
even when the resulting system is far removed
from the expert’s control
 Liability of the User
• End users of a knowledge based system are
not immune to law.
• Users are directly responsible for proper use
of the system
• By not properly using an available resource,
users could be negligent by omission or
“passive negligence
 The basis of liability
• Tort law is major area of concern with the issues
of strict liability and negligence falling under it.
• A special area of law that remedies wrongs
between parties
• Settles contract problems between the domain
expert and the employer in terms of knowledge
ownership
• A business could be found negligent if it did not
exercise due care in monitoring and safeguarding
its intellectual property
• Misrepresenting a product is subject to litigation
 Knowledge—A Product or a Service?
• If knowledge is what you say, not what you
see, it can be viewed as a service
• If knowledge is codified and packaged as a
mass-marketed item, it is viewed as a product
• Many legal experts want knowledge-based
systems to be considered as services in order
to avoid the strict liability associated with
products
 Knowledge—A Product or a Service?
Knowledge as a Product Knowledge as a service

Off the self software Custom design software

Mass marketing software

Custom designed but affects a large Negligence principles used

number of customers

Proving negligence unnecessary to holding Negligence caused of action more

developer difficult for plaintiff to prove

Uniform commercial code liability limit For liability law of state applies rather
allowable via disclaimer of warranty UCC

Source: Awad and Ghaziri: Knowledge Management (2007)

 Copyrights, Trademarks, and Trade
Names
• An area that falls under intellectual property
law
• Copyright is ownership of original work
created by an author
• Copyright law gives author the right to exclude
others from using the finished work
 Copyrights, Trademarks, and Trade
Names (cont’d)
• In KM, a knowledge repository and the way it
is organized are copyrightable
• Logos and trademarks are also copyrightable
• On the Web, images and banners are
protected by copyright laws
 Copyrights, Trademarks, and Trade
Names (cont’d)
• A trademarks means registration of a
company’s trade name so that others cannot
use it.
• A trademark is also a symbol or a word that
distinguishes a good from other goods
• An outsourced Web site is intellectual
property and belongs to the company under
contract
 Warranties
• An assurance made by seller about the goods
sold
• An express warranty is offered orally or in
writing by the maker of the product
• An implied warranty is part of a sale that has
been made that the good will do what it is
supposed to do—implied warranty of
merchantability
• A DICLAIMER is the seller’s intention to
protect the business from unwanted liability.
 STRICT LIABILITY
• Joint & several liability for developers,
manufacturers & distributors if tort theory
applies

• Protects web visitor regardless of whether

anyone is at fault
• TAXATION ISSUES- Controversial

– Different jurisdiction
– Consumers’ reaction
 Legal disputes in knowledge based
system
• In KM several disputes issues may arise having
legal implications
• An expert owns the knowledge of the work if
there is no prior agreement.
• If a knowledge developer builds the system
and a problem arises, he is subject to charges
of personal liability under the doctrine of
respondent superior.
 Legal disputes in knowledge based
system
• If the developer is a company employee, the
organization is also involved in the negligence
action.
• If a knowledge based system is a product, proving
negligence is unnecessary to hold the developer
liability
• If a knowledge based system is a service contract
law of the state will apply.
• Case involving warranties require the uses to
show who is at fault.
 Web linking domain name issues
• In E- commerce unique knowledge about a product , a
company or service resides in websites.
• Hyperlinks- infrastructure of the internet is designed
around to link text or images addresses automatically
• This jumping from one page to another raises some
legal issues-
- Referencing a linked site without permission from the
site owner
- retrieving or downloading information without
referencing or permission
- Unauthorized use of company’s trademark
- Adding a web programme to a comany’s website
without permission
 Domain name issues
• It represents a company’s intellectual capital.
• There could be dispute as who is the owner of the domain
name.
• InterntNic (Internet network information centre) manages
the domain names on a first come first serve basis,
• Idea about the use of domain mane and trade marks-
- Make sure that domain name does not infringes any trade
mark
- Secure registration for the domain name
- Register your domain name with Internet Nic.
- Get permission before linking to other websites to avoid
liability issues
 The malpractice factor
• Malpractice in KM is negligence applied to
knowledge developers for design defects in
KM system for professional use.
• Knowledge developers must be professionals
to be held liable for malpractice.
• There is no standardization or certification for
knowledge developers
 The Ethical Factor-
• Ethics –Fairness, justice, equity, honesty,
trustworthiness & equality Subjective
• Stealing, cheating, lying or backing out on one’s word
are descriptions of lack of ethics.
Legal

Example
Example
Restricting immigration
Donation a charity
B C
Immoral Moral
A D
Example Example
Robbing a bank Rescuing hostage from a
Falsely reporting foreign country
charitable donations
Illegal 72
 Ethical Decision cycle
• Knowledgeable people are expected to follow
Ethical behaviour and consider a number of
elements to make ethical decisions-
1. The nature and essence of the act- Is it fair reasonable or
conscionable
2. The consequences of the action or inaction on the parties
involved
3. The far reaching consequences of action or inaction on the
organization or society.
Ethical Decision Cycle

Act Evaluation of Action Outcomes

(Intentions) Alternatives (Decisions) (Consequences)

Organizational and
societal feedback
( human Filter
 Major threats to Ethics
• Faster computers & networks
• Sophisticated telecommunications & routers
• Massive distributed databases
• Eases of access to information & knowledge
base
• Transparency of software
 Improving the ethical climates
• Top management support
• Code of ethics
• Strong Ethics training program
• Motivation to focus on honesty & integrity
• Prompt dealing of unethical behaviour
 WHERE TO START
• Bottom-up
– Inculcates ethics behavior at the employee level with full
support of top management

• Top-down
– The actions of the company start with the CEO
– Extend to a variety of stakeholders
 Code of ethics
• A declaration of principles and beliefs that govern
how employees of a corporation are to behave
• Inspirational & disciplinary
• All-compassing & stable over time
• Self- Assessment- A question-and-answer
procedure
• Allows individuals to appraise & understand their
personal knowledge about a particular topic
• An educational experience
 Privacy factor
• Notice
– Right to be told in advance
– Choice
– Final say regarding the use of personal info

• Access
– Access & correct any personal info

• Security/integrity

• Enforcement
– Backed by the courts if any principles are violated
 New technology related ethical
problems
• Traditional rules of conduct are not always
applicable to a new medium

• A question that often arises: Should a device,

a technique or technology be restricted
because people can use it for illegal or
harmful actions as well as beneficial ones?
Example Mobile phones with cameras. Pupils at school take photos of
other pupils in the shower, and publish the pictures on the
Internet
 Information Technology Ethical Challenges

• No Form of licensing for computer professionals

– Results in no real way to enforce ethical standards within the
computing field
– There is movement within the industry to create a licensing process
but there are many issues to be resolved
• What will be included on the exam?
• How often will an IT professional be required to renew the license?
• Developed by several organizations
– Adoption
– Implementation
– Monitoring
– Example: http://www.acm.org/constitution/code.html
 Web Design Related Challenges:
• Implementation of features
– Pop ups
– Blocking/filters
– Aliases and redirecting
– Cookies
– Privacy policies
– Security policies
– Spyware
• Use of other design features
– Javascript
– Graphics - pictures, buttons, logos, icons
– Content
– Design layout
– Accountability/responsibility
– Outdated material, inaccurate material
 Commerce Related Challenges
• Fraud
• Taxation
• Free Trade
• Gambling
• Auctions
• Spamming
– Who were Canter and Siegel?
– Spamming cell phones?
• Term papers for sale
– Atlanta Journal Constitution article
 Workplace Challenges
• Accessibility
• Ergonomics
• Outsourcing
• Telecommuting
• Customer relationships – Vendor relationships
• Should IT professionals be in the ethics business or should other
areas of the business handle these issues?

• Monitoring
– Should your employer have the right to monitor private email
messages?
– What are the two most popular Web sites for American
workers? Playboy and ESPN
 Workplace Challenges
• Employers monitoring employees' email and Internet
use cite legal liability as the primary reason to monitor.
• Some companies that monitor have a written email
Policy, an Internet Policy, and a Software Policy.
• Some employers have disciplined or terminated
employees for violating ePolicy.
• Some organizations having email retention & deletion
policies in place.
• Some companies have been ordered by courts to turn
over employee email related to workplace lawsuits.
• Many organizations have battled sexual harassment
and/or sexual discrimination claims stemming from
employee e-mail and/or Internet use.
•
 Music Downloads
• Risk of getting caught
– Studies have shown that a majority of the people who
share music on the Internet are aware that their actions
are illegal,
– But they also know that the chances of getting caught are
pretty remote''
– Why is “getting caught remote”?
• There are peer-to-peer network subscribers in the US
with tens of millions more in other countries

– The RIAA( Recording industry association of America) is

seeking out people who make their music files available for
others to download.
• The networks have features that allow users to block
others from downloading their files but allow them to
continue to download files
 Challenges: Computing Resource
Abuse
• Computers in the Workplace and the Classroom
– Use or Abuse
– Internet Access
– Instant Messenger
– Laptop use in the classroom
– Email
• Legal document
• Can be modified
• Flaming
– Access: Computer Usage policy, Email policy
• Computer Crime: Viruses, Hackers, Theft
– “These cyber swindles and dot-cons present new challenges to
law enforcement” said John Ashcroft
 Challenges: intellectual Property
• Electronic Copyright
• Licensing
• Interoperability
• Licensing
– Cyberlicenses, Shrinkwrap, Shareware, Freeware
• MP3
– court case against college students
– University Internet Usage policies
• Internet Downloads
– Files
– Graphics
– Text
 Challenges: Intellectual Property
• Patent, trade secrets, and copyright law
– Who owns the program
– Who owns the algorithm
• Software Piracy
– Why shouldn't I use pirated software? Who am I hurting by doing
so?
– Piracy exists in everywhere.
– Loss of revenue hurts everyone.
– All software piracy is illegal and Software piracy is unethical.
– Various studies have found that the software industry loses
approximately $12 billion every year .
– State Industry Study
• CD-RW
 Other Challenges
• Decision making using Expert Systems
• Network Security
• Software accuracy and reliability who is ethically
responsible?
 Some Ideas to Ponder
• Computer ethics today is now a global effort
– The gap among the rich and poor nations, rich and poor citizens exists.
How can it be eliminated or reduced eithically and morally to provide
information and services that will move them to into the world of
cyberspace?
– Will the poor be cut off from job opportunities, education, entertainment,
medical care, shopping, voting - because they cannot afford a connection
to the global information network?
– Whose laws will apply in cyberspace when hundreds of countries are
incorporated into the global network?

• What happened? Where did our knowing right from wrong go too?
– Are we missing an opportunity to introduce ethics at an early age in
children by not integrating these thoughts and practices in video games?
– Should more controls and regulations be introduced into the system? Will
they actually help to improve our moral and ethical behavior?

• Unethical behavior continues to permeate industry, what measures, policies,

codes of conduct be changed to change this behavior?
 Managing KM: Chief Learning Officer
• CLO is charged with KM in the organizations
where the emphasis is on the social aspects.
• CLO is the business leader of corporate learning
and leads the organization’s learning and
development strategy, processes and systems.
• CLO usually focuses on human resource
development, and employees’ learning and
training
• CLO’s role increasingly involves utilizing ITs to
improve KM, often in collaboration with the CIO
 Leadership of Knowledge
Management
• The CEO designates the KM leadership who could
be the Chief Knowledge Officer, Chief Learning
Officer or the Chief Information Officer
• The chief knowledge officer is usually expected to
balance social and technical aspects of KM,
• The chief learning officer and the chief
information officer are generally charged with KM
in organizations where the emphasis is on the
social aspects and technical aspects, respectively
 Chief Knowledge Officer
• CKO is in charge of management of the
organization’s intellectual assets and
knowledge management
• CKOs are technologists because they invest in
IT, and they are environmentalists because
they also create social environments that
stimulate conversations and knowledge
sharing.
 Factors Change Leaders Consider
• Focus less on problems and more on
successes and opportunities
• Adopt an attitude that views challenges as
opportunities
• Work on creating tomorrow’s business instead
of hammering on yesterday’s problems
 The Soft Side of management Always
Wins
• Encourage every team member to create new
knowledge in the interest of the project
• Help knowledge workers do their jobs
• Allow knowledge workers to participate in
major company decisions, which can pay off in
intrinsic and extrinsic benefits for the
company and employees alike
• Encourage knowledge workers and employees
to learn as they earn a living on a regular basis
 Linking Incentives and Motivation
with KM
• Link incentives to a team approach, where
team performance will determine size and
nature of the incentive
• Use awards for teams as well as individuals for
unique contributions
• Flextime allows the team to decide on when
to work, when to quit, and so forth
• Monetary rewards, bonuses, and special
prizes can be a hit with the winning team
• Publicize success throughout the firm
 Implications For KM
• The legal implications of KM is a problematic area
as what rules should govern KM is still debatable.
• Long range effect of continued hoarding of
knowledge as way to safeguard knowledge
• Protect knowledge the knowledge for
competitive advantage- How to stop abuse
• Integrity of knowledge developers, experts and
corporation
• Management focussing on legal and employee
protection on issues surrounding KM ownership
 Module 8: Knowledge
Management

KM in Indian Organizations and

the future of KM
 KM in Indian Organizations
• The competitive forces have forced Indian
organizations to use KM include quality, cost
reduction, improvement in efficiency,
improved delivery, flexibility and innovation
• Many organizations in India have initiated
knowledge management initiatives.
 KM in Indian Manufacturing
Organizations
• A survey conducted in manufacturing
organizations showed the reasons for using KM as
follows:
- ensuring competitive advantage;
- creating new knowledge for the organization;
-managing resources effectively;
- developing new technologies and products.
• Planning, organizing, shop-floor operations, and
R&D are the main areas where the use of KM is
important for manufacturing firms
Source: M.D. Singh Ravi Shankar Rakesh Narain Adish Kumar , (2006),"Survey of knowledge
management practices in Indian manufacturing industries", Journal of Knowledge
Management, Vol. 10 Iss 6 pp. 110 - 128
 KM in Indian Manufacturing
Organizations
• A survey indicated that manufacturing sector has been
benefitted from KM in the following ways:
- Ensuring the availability of the right kind of technology;
- Using best practices;
- Devolving ways to narrow the gap between marketing,
- manufacturing and R&D;
- Using IT tools;
- Developing new capabilities;
- Deploying the right people in right place;
- Using knowledge maps; and
- Improving quality and productivity and plant capacity, etc.
Source: M.D. Singh Ravi Shankar Rakesh Narain, and Adish Kumar , (2006),"Survey of
knowledge management practices in Indian manufacturing industries", Journal of
Knowledge Management, Vol. 10 Iss 6 pp. 110 - 128
 KM practices in Indian Public and
Private sector
• In India, KM in public sector is still in its early stages and has
a long way to go in order to keep pace with private sector
counterparts
• Private sector fared better compared to public sector on
existence of KM system and mechanism, involvement of
stakeholders for ideas, mechanism of transferring best
practices and knowledge, and importance of tacit knowledge
• Private sector better use of internal benchmarking
effectively to identify improvement opportunities through
identifying employee knowledge gaps, take action to bridge
gaps and create reusable repositories.
Source: Deepak C., & Himanshu JJ. (2010),"Knowledge management initiatives in Indian
public and private sector organizations", Journal of Knowledge Management, Vol. 14 (6) pp.
811 - 827
 KM practices in Indian Public and
Private sector
• Private sector encourage more job rotation,
apprenticeship, mentorship, etc. to maximize the
sharing of tacit knowledge and its conversion into
explicit form
• Private sector create a strong linkage between KM and
improved business performance
• Private sector demonstrate greater tolerance for
uncertainty and ambiguity that encourages employee
ability to experiment, innovate and create new ideas
• Private sector developed strong metrics and
knowledge audit systems to determine the return of its
knowledge investments
Deepak C., & Himanshu JJ. (2010),"Knowledge management initiatives in Indian public and
private sector organizations", Journal of Knowledge Management, Vol. 14 Iss 6 pp. 811 - 827
 KM practices in Indian Public and
Private sector
• Public sector encourages more formal discussion,
facilitates systematically knowledge sharing
• Public sector have a more coherent strategy for
KM and they are more advanced in areas of
knowledge sharing
• Both public and private sector organizations in
India need to improve on various dimensions of
KM like process, leadership, culture, technology
and measurement.
Deepak C., & Himanshu JJ. (2010),"Knowledge management initiatives in Indian public and
private sector organizations", Journal of Knowledge Management, Vol. 14 Iss 6 pp. 811 - 827
 Knowledge Management : Some
Suggestions
• KM should become a policy in the company.
• The involvement of top management is
needed in allocating the necessary resource
flow to initiate and sustain KM practice.
• KM awareness and commitment
• People need to be aware of the importance of
documentation
 KM Practices Problems and concerns
• Individuals are not visibly rewarded for knowledge
sharing.
• Lack of knowledge sharing and tendency to hoard
knowledge
• KM is not given due importance in the performance
appraisal system
• Culture is not facilitating sharing and learning in the
organization to a very high extent
• The maturity level of employees towards the concept
of KM is inadequate
• No formal mechanism to transfer the knowledge
gained through seminars, training programmes,
deputation abroad to the workplace
KM Practices in IT Companies, Sinigh and Soltano, TQM, 21(2),145-157,
2010.
 KM Practices in IT Companies:
• IT companies are extensively using software like CAT
and CAD as a knowledge tool while designing.
• LAN can be used more extensively for information
sharing and also centralized cataloguing of reports can
go a long way in managing knowledge.
• There is a need to document experiences gained from
earlier projects so that this learning can be applied on
future projects
• Companies must be able to capture, validate and
distribute new knowledge fast enough to change
strategic direction and resource allocations, if they are
to prosper in turbulent environments.
KM Practices in IT Companies, Sinigh and Soltano, TQM, 21(2),145-157, 2010.
 Reasons for Launching Knowledge Management Programme in IT
Companies

Satwana Choudhury:2011, 3rd International Conference on Information and Financial

 Sources of Knowledge Acquisitions in IT Companies

Satwana Choudhury:2011, 3rd International Conference on Information and Financial Engineering,

IPEDR vol.12 (2011) © (2011) IACSIT Press, Singapore
 Managing Ideas for Innovation in IT Companies

Satwana Choudhury:2011 3rd International Conference on Information and Financial Engineering, IPEDR
vol.12 (2011) © (2011) IACSIT Press, Singapore
Task ahead for KM in IT Companies
• Knowledge Management systems, to be
effective should have easy-to-use interface,
solid reliability, accessibility throughout the
target segment and utilities to mine relevant
information.
• The target segment (like employees,
customers, investors) needs to be taken into
account before creating KM systems. The full
commitment to KM from the top management
is very critical in its implementation
 Issues: Managing knowledge workers
in a Knowledge Economy
• Embodies experience, innovation, creativity,
and transformation of experience into
knowledge for leveraging products or services
• Transforms business and personal experience
into knowledge through capturing, assessing,
applying, sharing, and disseminating it within
the organization to solve specific problems or
to create value
 1.Understanding Personality and
Professional Attributes of Knowledge
Workers
• Holding unique values
• Aligning personal and professional growth with
corporate vision
• Adopting an attitude of collaboration and sharing
• Innovative capacity and a creative mind
• Clear understanding of the business he is a part
• Willing to learn, unlearn, and adopt new ways
that result in better ways of doing a job
• In command of self-control and self-learning
• Willing to grow with the company
 2. Developing the core competencies
of knowledge workers
• Thinking skills—having a vision how the
product or the company can be better
• Continuous learning—unlearning and
relearning in tune with fast-changing
conditions
• Innovative teams and teamwork—via
collaboration, cooperation, and coordination
• Innovation and creativity—”dreaming” new
ways to advance the firm
 Developing the core competencies of
knowledge workers
• Risk taking and potential success—making
joint decisions with calculated risk
• Decision action taking—be willing to embrace
professional discipline, patience, and
determination
• Culture of responsibility toward knowledge—
loyalty and commitment to one’s manager or
leader
 3. Facilitating a culture.
• Those in charge of KM initiatives need to
create a culture of knowledge sharing to
implement KM.
• The time tested and effective way to transfer
knowledge is for people to find others who
have it and talk to them
• Employee attitude towards radical change in
the ongoing system
 4. Having a KM Strategy
• Actively Managing Knowledge
• Expert Individuals Provide Insights
• Development of Critical Capabilities
• Ease of Availability of Information
• Sensing and Understanding meaning and values
• Structure the Institutionalized Goals
• Link individual learning with organizational
learning by creating processes and system
• Identify Knowledge Assets and easy to use
interface
 Current issues of Knowledge
management in Indian Companies
• Cultural diversity and wide disparities in the
extent of up-to-date infrastructure make
managing knowledge challenging in developing
countries like India.
• Cultural diversity and infrastructural gap issues
are also related to a variety of government,
educational, political, social, and economic
factors.
 Current issues of Knowledge
management in Indian Companies
• Environmental factors interact with
organizational variables and information
technology to enable or constrain knowledge
management processes in the creation and
protection of knowledge resources.
• Leadership, structure, and culture that are
contextual to each organization and the
environment in which they operate.
• Firms should align information technology,
people, structure, and organizational influences
to make knowledge processes and socio-cultural
management sustainable.
 Most admired knowledge economy
( MAKE) Award
• The Global MAKE study is a measure of the rate
at which an organization is transforming its tacit
and explicit corporate knowledge into new
enterprise intellectual capital and increased
shareholder value (or in the case of non-profit
and public organizations, stakeholder capital).
• Accenture and Microsoft are the only
organizations which have been recognized as
Global MAKE Winners every year since the MAKE
research studies began in 1998
Most admired knowledge economy (MAKE)
Award
Knowledge performance dimensions which form the MAKE framework
• developing knowledge workers through senior management
leadership.
• developing and delivering knowledge-based
products/services/solutions.
• maximizing enterprise intellectual capital.
• creating an environment for collaborative enterprise knowledge
sharing.
• creating a learning organization.
• delivering value based on stakeholder knowledge.
• transforming enterprise knowledge into shareholder/stakeholder
value.

• Three Indian firms are there as winners I 2016- Tata

group, Infosys and Wipro India Limited. These
companies have won it a number of times
 Key Findings of MAKE Global Study
2016
• Successfully managing enterprise knowledge
yields big dividends
• Higher Return on Revenues
• Excel at creating knowledge-driven
organizational cultures, and developing
knowledge workers through supportive senior
management leadership
• Leadership challenges in hiring, training and
developing knowledge workers.
 Key Findings of MAKE Study
• Firms continue to struggle to create value
through managing other ‘people’ issues,
including improving the skills and capabilities of
knowledge workers, creating learning
organizations, and working in partnership with
customers/stakeholders.
• Many organizations are failing to address the
rapidly changing digital world of social media and
the customization of the customer experience in
order to create a competitive edge.
 Some case examples
• Tata Steel
• Infosys
• Wipro
• BHEL
• ONGC
 KM in TATA STEEL
KM strategy at TATA Steel
Steps in implementing KM in TATA
Steel
Development of KM in TATA STEEL
 Benefits of KM at TATA Steel

Tata Steel has been conferred the prestigious Indian ‘Most Admired
Knowledge Enterprises’ (MAKE) Award many times
Future of KM at TATA Steel
 KM at Infosys
• In 1999, Infosys introduced a formal KM system In order to
maintain uniformity in knowledge dissemination,
• Kshop, a knowledge portal was launched in year 2000.
• KM group introduced KCU’s (knowledge currency units) in
2001 to encourage employees to use and contribute to Kshop
• KCU scheme was modified and emphasized on knowledge
sharing and visibility rather than monetary rewards in April
2002.
• After these changes the quality contributions to Kshop
increased. By 2005, Infosys had highly sophisticated KM
system in place.
• Nov 2005, “Infosys inducted into Global MAKE Hall of Fame”.
(3 consecutive years)
 The KMM - Knowledge Management Maturity
Model
• The 3 prongs- People, Process, and Technology
-Each level has a set of prerequisites the organization
is required to meet.

-A given maturity level implies a certain level of

organizational capability
❖from level 4 onwards, quantitatively

-Each maturity level is characterized in terms of the

efficacy of each stage of the knowledge life cycle:
– Knowledge Acquisition
– Knowledge Dissemination
– Knowledge Reuse
Level–Organizational Capability Mapping
Level–Organizational Capability
Mapping
 Level 1: Default
❖Conviction in anything other than survival-
level tasks low.
❖Belief in formal training being the sole
mechanism for learning; all learning is reactive
❖Organization’s knowledge is fragmented in
isolated pockets, and stays in people’s heads
 Level 2: Reactive
• The organization shares knowledge purely on
need basis
• Routine and procedural knowledge shared
 Key Result Areas - Level 2
• Knowledge Awareness (People)
– Awareness of knowledge as a resource that must be managed explicitly
(“somebody-else-should-do-it” syndrome!)
– Senior management recognizes need for formal knowledge management.
– Knowledge ‘database administrator’ role
• Content Capture (Process)
– Knowledge indispensable for routine tasks is documented.
– Database of knowledge exists (usually disparate formats)
– Content compilation done reasonably well but creation still ad-hoc
– Content management responsibility dispersed through organization.
• Basic Information Management (Technology)
– Rudimentary knowledge-recording systems in existence
• diverse data formats, fragmented data, low data integrity, high data obsolescence
– Systems support routine and procedural sharing.
– Online and technology-based learning mechanisms put in place - largely
reactively.
 Level 3: Aware
• Content fit for use for all functions; knowledge meets need
• Beginnings of integrated approach to managing knowledge life-
cycle.
• Enterprise-wide knowledge-propagation systems in existence –
awareness and maintenance are moderate.
• Internal expertise is leveraged in technologically complex
and unfamiliar areas, or where it is imperative.
• The organization collects and understands metrics for KM; KM
activities begin to be translated into productivity gains
• Managers recognize role in, and encourage, knowledge-sharing.
• The organization is able to see a link between KM processes and
results.
 Key Result Areas - Level 3
• Central Knowledge Organization (People)
– Dedicated KM group for infrastructure management and content
management.
– Processes and roles well-defined not below CMM level 4.
• Knowledge Education (People)
– Training in KM processes for KM group;
– Formal training program for contributors, users, facilitators, champions, etc.
with feedback
• Content Structure Management (Process)
– Ability to structure, categorize, access content
• Integrated logical content architecture exists.
• Knowledge content is augmented with pointers to people.
• Knowledge is structured
– a taxonomy of knowledge topics
• Content management process defined.
– creation, editing, streamlining, publishing, certification and maintenance
• Process is owned by a central knowledge organization
• Knowledge Technology Infrastructure (Technology)
– Single-point access to knowledge available across the organization (the
knowledge is not integrated –only access is available)
 Level 4: Convinced
• Enterprise-wide knowledge-sharing systems in place –
quality, currency, utility, usage high
• Knowledge processes scaled up across the
organization.
• Organizational boundaries breakdown as knowledge
barriers
• Quantification of benefits of knowledge sharing and
reuse at org unit level – business impact clearly
recognized
• Feedback loops are qualitatively better and tighter.
• Ability to sense and respond proactively to
environmental changes
 Key Result Areas - Level 4
• Customized Enabling (People)
– Training (all modes) available at time and point of need
• Knowledge Infrastructure Management (Technology)
– Technology infrastructure for knowledge-sharing is
seamless; the knowledge content is integrated into a
whole.
• Content Enlivenment (Process)
• Content enlivened with expertise;
• Experts across organization committed to respond
• High sync between knowledge in, knowledge out
• Knowledge Configuration Management (Process)
• Organization-wide process for integrating and managing the
knowledge content configuration.
• Knowledge life-cycle processes are mapped
 Key Result Areas - Level 4
• Quantitative Knowledge Management
(Process)
– Knowledge creation, sharing reuse levels are
measured quantitatively
• variance across the organization low.
– Benefits of knowledge sharing and reuse at the
individual project / function level quantified.
– Capability baselines are created and used.
– Content management process uses quantitative
data.
 Level 5: Sharing
• Culture of sharing institutionalized; sharing
becomes second nature to all.
• Organizational boundaries irrelevant
• Knowledge ROI integral to decision-making
• Continuous tweaking of the kdge processes
• Ability to shape environmental change;
organization becomes a knowledge leader
 Key Result Areas - Level 5
• Expertise Integration
– Content and (human) expertise available as an integral package.
• appropriate expertise is available to help understand content and tailor it to
specific need.
• Knowledge Leverage
– Ability to measure contribution of knowledge to competence
– Availability of knowledge inputs needed to perform tasks is
guaranteed in quantitative terms.
– Knowledge processes continuously tweaked: performance
measures used to improve content management and
technology infrastructure
• Innovation Management
– Organization has the ability to assimilate, use and innovate
based on ideas both external and internal. Processes exist for
leveraging new ideas for business advantage.
– Knowledge base considerations explicitly used in taking on a
new customer / project
 KM at Wipro
• The KM initiative at Wipro began in 2000
• The KM process at Wipro has three stages-
- First stage assesses the competencies of people,
establishes the desired competency levels and current
gaps and designs relevant training to bridge the gap.
- Second stage attempts, through the use of technology, to
retain the knowledge within the organization in a
manner that can be accessed by people on demand.
- Last stage involves people continuously using the existing
knowledge base, augmented with research to deliver
higher value to the customer.
 KM at Wipro
• Leadership in KM- Top management commitment, with the
CEO driving the initiative to ensure the success of this
movement in Wipro
• Building a collaborative learning environment and culture
for KM.
• Infrastructure for KM -leveraged its existing IT
infrastructure to capture, store and share Knowledge across
the enterprise
• Developing metrics for measurement of continuous
improvement
 KM at WIPRO

Create
Capture
Tacit Knowledge
Explicit Knowledge - Discussion
- Doc Repositories
Knowledge
- Reusable Comp. Use Groups
Organize -Yellow Pages
-Chat Rooms
Access

Bus. Processes

Infrastructure
KM Team

KM
Measure
-ments
Key
Business KM Vision & Strategy
Drivers
Organizational Values & Culture
 KM initiative dedicated team
• Structure- KM head directly reporting to CEO
 Wipro’s My Workmate™
• solution on cloud can help organizations
• delivering services in support of the full lifecycle of end-to-end
• integrated Knowledge Management program – from Knowledge
assessments, Strategy, Processes, KM, Technology Roadmap to
Implementation, Roll-out, Sustenance, and Enhancement.
Benefits of KM programme at WIPRO
 KM at BHEL
• The KM process at BHEL- robust and flexible document
management system which provides role-based access to
documents
• Leadership in KM-Top management has given it utmost importance
by creating team for the implementation of the KM framework
• Building a collaborative learning environment and culture for KM.
facilitating, a culture of information sharing, relationship building
and trust.
• Has created a knowledge bank through its efficient
• document management system
• Infrastructure for KM - A web portal for document and data, user
interface through a single window
• Developing metrics for measurement of continuous improvement -
- internal metric to track how its central repository is being used by
individuals and teams across departments.
 KM at ONGC
• ONGC created an independent platform for
KM resulting in launch of a KM portal
• People share their knowledge through
informal interactions
• Launch of Gyanodayan to share experiential
knowledge of individuals and teams and strive
to increase it tacit and explicit knowledge base
• Developing a comprehensive KM system and
plans to set up a virtual knowledge park
 Objective of KM programme at ONGC
• Skills knowledge gap identification
• Communities of practice- Drilling the limit to
achieve the reduction in drilling time and squeeze
the last bit info from seismic for maximizing the
information value of seismic data
• Best practice and lessons learned on offshore
structures and well stimulation services
• Leveraging the vast knowledge of its workers in
the field of exploration and petroleum
technology for increased productivity, cost , time
and effort reduction, improved quality
 Major KM initiative from ONGC
• Introduction of company wide e learning
system
• Knowledge up gradation programme- Unnati
prayas, superunnati prayas, sangsaptak
• Formalization of roles, positions and
responsibility
• Formalization of industry – academia
programme
Dimensions of Organizational Impacts
of KM

Organizational
People Processes Products
Performance

Knowledge Management

Becerra-Fernandez, et al. -- Knowledge Management 1/e 2004 Prentice Hall

 Impact of KM on employee, their
learning and adaptability
• KM can facilitate employee learning
• KM also causes employees to become more flexible,
and enhances their job satisfaction
• Employees cab better adapt when they interact with
each other
• They are more likely to accept change
• They are more prepared to respond to change
• Employees sharing knowledge with one another
resulting in reduced turnover rates and increased
revenue and profit
• Leads to better job satisfaction
Becerra-Fernandez, et al. -- Knowledge Management 1/e -- © 2004 Prentice Hall
Impact of KM on process Improvement
• KM enables improvements in organizational
processes such as marketing, manufacturing,
accounting, engineering, and public relations
• These impacts can be seen along three major
dimensions
– Effectiveness- performing the most suitable processes
and making the best possible decisions
– Efficiency- performing the processes quickly and in a
low-cost fashion
– Degree of innovation of the processes- performing the
processes in a creative and novel fashion

Becerra-Fernandez, et al. -- Knowledge Management 1/e -- © 2004 Prentice Hall

 Impact of KM on Processes
Improvement
• Impact on Process Effectiveness
– KM can enable organizations to become more effective by helping
them to select and perform the most appropriate processes
– KM enables organizations to quickly adapt their processes according to
the current circumstances, thereby maintaining process effectiveness
in changing times
• Impact on Process Efficiency
– Managing knowledge effectively can also enable organizations to be
more productive and efficient
• Impact on Process Innovation
– Organizations can increasingly rely on knowledge shared across
individuals to produce innovative solutions to problems as well as to
develop more innovative organizational processes

Becerra-Fernandez, et al. -- Knowledge Management 1/e -- © 2004 Prentice Hall

 Impact of KM on Products
Value added products - help organizations offer new
products or improved products that provide a significant
additional value as compared with earlier products
benefit from KM due to the effect the latter has on
organizational process innovation
Knowledge based products- significant impact on
product that are knowledge based like those in
consulting or software development etc.
can sometimes play a significant role in traditional
manufacturing firms
Becerra-Fernandez, et al. -- Knowledge Management 1/e -- © 2004 Prentice Hall
 Impacts of KM on Organizational
Performance
• Direct Impacts
– Knowledge is used to create innovative products
that generate revenue and profit
• Indirect Impacts
– Use of KM to demonstrate intellectual leadership
within the industry, which, in turn, might enhance
customer loyalty
– Use of knowledge to gain an advantageous
negotiating position with respect to competitors
or partner organizations
Becerra-Fernandez, et al. -- Knowledge Management 1/e -- © 2004 Prentice Hall
 Economy of Scale and Scope
• A company’s output is said to exhibit economy
of scale if the average cost of production per
unit decreases with increase in output
• A company’s output is said to exhibit economy
of scope when the total cost of that same
company producing two or more different
products is less than the sum of the costs that
would be incurred if each product had been
produced separately by a different company
Becerra-Fernandez, et al. -- Knowledge Management 1/e -- © 2004 Prentice Hall
 The future of KM
• KM systems to support humane decisions and
to deal with “ wicked ” problems
• Corporate managers need to institute
safeguards for insuring the security and
adequate use of their corporate knowledge.
Protecting Intellectual Property (IP)
• IP can be defined as any results of a human
intellectual process that has inherent value to
the individual or organization that sponsored
the process.
• It includes inventions, designs, processes,
organizational structures, strategic plans,
marketing plans, computer programs,
algorithms, literary works, music scores, and
works of art, among many other things.
Becerra-Fernandez, et al. -- Knowledge Management 1/e -- © 2004 Prentice Hall
IP losses can happen in many ways:
• Employee turnover.
• Physical theft of sensitive proprietary documents, either by
outsiders or by insiders.
• Inadvertent disclosure to third parties without a non-
disclosure agreement.
• Reverse engineering.
• The Web repository security is breached and unauthorized
access to the proprietary documents takes place.
• Unauthorized parties intercept electronic mail, fax,
telephone conversation or other communications for the
purpose of illicitly acquiring knowledge.
• Attempts by insiders or outsiders to corrupt documents or
databases with false data, information, or knowledge.

Becerra-Fernandez, et al. -- Knowledge Management 1/e -- © 2004 Prentice Hall

 How to protect the organization from
IP losses
• Non-disclosure Agreements

• Patents

• Copyrights

• Trade Secrets
 KM: A new Paradigm for Decision-
Making
• The development of MIS, Decision Support
Systems, and KMS has been influenced by the
works of five influential philosophers, namely,
Leibniz, Locke, Kant, Hegel, and Singer.
• Recent developments in KMS have also enabled
to extend the reach of those involved in the
solution, through group support systems.
• As globalization expands, the number of
stakeholders affected by the organization
increases
 Decision making based on multiple
perspectives
• Technical perspective
• Personal and individual perspective
• Decision making based on multiple
perspectives
• Organizational and social perspective
• Ethics and aesthetics perspective
 Emerging KM Practices
• Knowledge management practices that enable
knowledge sharing and collaboration include-
• Web 2.0’s role in KM
• Social networks
• Wikis and Blogs
• Open source development community
• Virtual worlds
• The three worlds of IT `
 Emergence of Web 2.0
• Coined by Tim O’Reilly in 2004 to describe
development and evolution of Web-based
communities and hosted services such as
social- networking sites, video sharing sites,
wikis, blogs, and folksonomies
• AJAX- Asynchronous JavaScript and X ML
allows web applications to perform more like
a desktop application
 Emergence of Web 2.0
• Collective Intelligence- The content from Web 2.0
comes from users, e.g. product reviews
• Web 2.0 is of strategic value
• Will maintain or increase investment in: social
networking, P2P networking, Web services
• Enterprise 1.0 vs 2.0- 1.0 - IT = channel for
distribution and platform for viewing 2.0 - IT
provides tools search, links, authoring, tagging,
signals, and extensions
 Social Networking
• Friendster (2002) Allowed users to share photos,
videos, comments and messages among friends
• “Who’s viewed me?” Grew (based on user
relationships not via traditional marketing
• MySpace (2003) “A place for friends” —
included features of Friendster Added music
(knowledge) discovery
• Aligned itself as channel for music distribution
among indie and major recording label artists
Highest visited domain in the USA in 2006
 Social Networking (continued)
• Linked In (2003) Professional social network
• Orkut (2005) Google owned used mostly to
India and Brazil
• YouTube (2005) Google owned Allows users to
upload, share and comment on videos
Enabled proliferation of video authoring and
sharing for the masses
 Social Networking (continued-)
• Facebook (2004) Designed as replacement )for
photo directories (facebooks) given to freshmen
• Exclusivity — must join as part of a network
• Added security Initially comprised (only of
students from elite Universities
• Now includes most schools, geographies, and
workplaces
• News feed — supports stickiness. Many repeat
visits throughout the day
 Social Networking (continued)
• Knowledge sharing research
• Knowledge held by Entities –people, organizations
or information systems
• Relationships characterized by-
• Type — friendship, advice or professional
• Strength — intensity or reciprocity
• Density — ratio of actual to possible ties in a
network
• Position of the individual in the network-Central or
peripheral
 Social Networking (continued)
• Enterprise uses of social networking
• Contact management
• Marketing
• Knowledge sharing
• Talent management
• Advantages - Self-organizing
• Leverages real-world connections
 Wikis
• Collections of web pages editable by anyone
• User generated content
• Usually a volunteer effort
• Collaborative
• No formal/professional review
• Approval stems from collective wisdom
• Most famous example — Wikipedia -Largest
online encyclopedia — 3 million English articles
 Web Logs (Blogs)
• Web diary / journal Posts in reverse
chronological order
• Used by people / organizations to
communicate with public, to mainstream
media for breaking news, Chronicle of events
• Army of Dude Twitter — a microblogging
service
• Facilitates online presence for maintaining
weak ties
 Open Source Development
• Originated after the development of ARPA net
• Free exchange of code for researchers
• Open source
- Coined by Tom O’reilly
- Collaborative
- Development of software
- Exchange done through internet
- Free software foundation- A legal entity under
which the open source movement operates
 Open source development
• Open source initiatives- Principles
-Free distribution
- Source Code
- Derived works
- Integrity of Author’s work
- Nondiscrimination for persons/groups
- Nondiscrimination on field
- Distribution of license
- License Must Not Be Specific to a Product
- License Must Not Restrict Other Software
- License Must Be Technology-Neutral
 Open Source Development
• Successful Open Source Projects:
Linux — x86 porting of Unix
Apache — world’s most used web server
BIND — underpinning of Domain Name system
MySQL — relation database management system
Bugs found and fixed more quickly
New versions are more rapidly deployed
Motives for participation:
Need for product
Enjoyment, desire to create and improve
Reputation and status within the community
Affiliation Identity Values and ideology
Training, learning, reputation outside the community and
career concerns
 Virtual Worlds
Metaverse
• parallel online abstraction of the offline world
• Interaction via avatars Second Life and Developed by Linden Labs
• Highly interactive 3D, real-time, social network
• Virtual economy with currency — Linden Dollars
• Used for recreation, marketing, political campaigning, socializing,
entertainment, and commerce
• Avatars interact in a 3D world known as the Grid

Project Wonderland

Developed by Sun Used for virtual

Collaboration Runs on java
Use of open document format
Use XML to change virtual world
 Looking at the future
• The future of knowledge management, will
be highlighted by three continuing trends:
– (1) KM will benefit from progress in
information technologies
– (2) KM will continue the shift toward
integrating knowledge from a variety of
different perspectives
– (3) KM will continue to make trade-offs in
numerous important areas.
 Moving beyond information
technologies
• KM will benefit from continual, and even more dynamic, progress
in information technologies
• Improvements in cost/performance ratios of IT have caused the
cost of digitizing information to approach zero, and the cost of
coordinating across individuals, organizational sub-units, or
organizations to approach zero as well.
• "evolutionary agents" may be dramatically different in their
abilities to:
– build theories and create a world of their own
– assume any virtual identity they wish
– possess free will
– develop a moral code and a value system of their
own
 Integrating knowledge from different
perspectives
• KM will continue the shift toward bringing
together, and effectively integrating, knowledge
from a variety of different perspectives.
• KM originated at the individual level, focusing
on the training and learning of individuals.
• The impact of KM is expected to continue with
its use across networks of organizations and
across governments, enabling collaborations
across historical adversaries and integrating
knowledge across highly diverse perspectives
and disciplines
 Making trade-offs in important areas
• Same communication technologies that support
the sharing of knowledge within an organization
also enable the knowledge to leak outside the
organization to its competing firms.
• It is essential to maintain a balance between
using technology as substitutes for people (e.g.,
software agents) and using technology to enable
collaboration from a wider range of people
within and across organizations.
 Conclusions
• The future of KM is one where people and
advanced technology will continue to work
together, enabling knowledge integration
across diverse domains, and with
considerably higher payoffs.
• The future of KM will clearly be exciting due
to the new opportunities and options, but
interesting challenges definitely lay ahead for
knowledge managers.