Lecture 2 - KNOWLEDGE MANAGEMENT SYSTEMS LIFE CYCLE

Chapter 2: Knowledge Management Systems Life Cycle

KNOWLEDGE MANAGEMENT

SYSTEMS LIFE CYCLEADP1 Develop Knowledge Management Solutions

Lecture Two


Motivation

For any task, from as simple as planning a trip, working on a maths problem,

The process involves a number of steps until you come up with a solution.

In developing a large software system used in industry, the process also follows a number of defined steps which are accepted as best practices by practitioners. 2-2


Motivation Cont

How many of you have taken a programming unit either here or

elsewhere before?

What would be the steps you would take in completing a programming assignment?

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Motivation Cont

read the problem statement mentally think about how to solve it select a programming language (if

decided, select what kind of data structures)

translate into program code compile, run and test modify if program doesn't function

as expected Satisfied!!

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This week’s Topics

Challenges in building KM Systems

Compare CSLC and KMSLC

User’s vs. Expert’s Characteristics

Stages of KMSLC

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CHALLENGES IN BUILDING KM SYSTEMS

Culture — getting people to share

knowledge Knowledge evaluation

— assessing the worth of knowledge across the organization

Knowledge processing

— documenting how decisions are reached

Knowledge implementation

— organizing knowledge and integrating it with the processing strategy for final deployment

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Conventional System Life Cycle

Recognition of Need and Feasibility Study

Logical Design (master design plan)

Physical Design (coding)

Testing

Implementation (file conversion, user training)

Operations and Maintenance

Functional Requirements Specifications

Iterative

KM System Life Cycle

Evaluate Existing Infrastructure

Knowledge Capture

Design KMS Blueprint

Verify and validate the KM System

Implement the KM System

Manage Change and Rewards Structure

Form the KM Team

Post-system evaluation

versus

Iterative


Key Differences

Systems analysts deal with information from the user; knowledge developers deal with knowledge from domain experts

Users know the problem but not the solution; domain experts know both the problem and the solution

Conventional SLC is primarily sequential; KM SLC is incremental and interactive.

System testing normally at end of conventional system life cycle; KM system testing evolves from beginning of the cycle

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Key Differences (cont’d)

Conventional system life cycle is process-driven or “specify then build”

KM system life cycle is result-oriented or “start slow and grow”

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Key Similarities Both begin with a problem and

end with a solution Both begin with information

gathering or knowledge capture

Testing is essentially the same to make sure “the system is right” and “it is the right system”

Both developers must choose the appropriate tool(s) for designing their respective systems

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Stages of KMSLC

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Evaluate Existing Infrastructure

Knowledge Capture

Design KM Blueprint

Verify and validate the KM System

Implement the KM System

Manage Change and Rewards Structure

Form the KM Team

Post-system evaluation

Iterative Rapid Prototyping


(1) Evaluate Existing Infrastructure

System justifications: What knowledge will 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 tacit reasoning to solve?

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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

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Role of Strategic Planning

Risky to plunge into a KMS without strategy

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Knowledge developer should consider:

Vision

Resources

Culture


(2) Form the KM Team

Identify the key stakeholders of the prospective KM system.

Team success depends on:› Ability of team members › Team size› Complexity of the project› Leadership and team

motivation› Not promising more than

can be realistically delivered

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(3) 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

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Selecting an Expert

› How does one know the expert is in fact an expert?

› How would one know that the expert will stay with the project?

› What backup should be available in case the project loses the expert?

› How could we know what is and what is not within the expert’s area of expertise?

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(4) Design the KM Blueprint

The KM blueprint addresses several issues:

Finalize scope of proposed KM system with realized net benefits

Decide on required system components

Develop the key layers of the KM software architecture to meet company requirements

System interoperability and scalability with existing company IT infrastructure2-18


(5)Testing the KM System

Verification procedure: ensures that the system has the right functions

Validation procedure: ensures that the system has the right output

Validation of KM systems is not foolproof

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(6) Implement the KM System

Converting a new KM system into actual operation

includes conversion of data or files also includes user training Quality assurance is important, which

includes checking for:› Reasoning errors› Ambiguity› Incompleteness› False representation (false positive and

false negative)2-20


(7) Manage Change and Rewards Structure

Goal is to minimize resistance to change› Experts

› Regular employees (users)

› Troublemakers

Resistances via projection, avoidance, or aggression

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(8) Post-system Evaluation

Assess system impact in terms of effects on:› People

› Procedures

› Performance of the business

Areas of concern:› Quality of decision making

› Attitude of end users

› Costs of Knowledge processing and update

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Key Questions

Has accuracy and timeliness of decision making improved?

Has KMS caused organizational changes?

What are users’ reactions towards KMS?

Has KMS changed the cost of operating the business?

Have relationships among users affected?

Does KMS justify the cost of investment?

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End of Lecture 2

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Basic Knowledge-Related Definitions

Common Sense

Inborn ability to sense, judge, or perceive situations; grows stronger over time

Fact A statement that relates a certain element of truth about a subject matter or a domain

Heuristic A rule of thumb based on years of experience

Knowledge Understanding gained through experience; familiarity with the way to perform a task; an accumulation of facts, procedural rules, or heuristics

Intelligence The capacity to acquire and apply knowledge

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Types (Categorization) of Knowledge

Shallow (readily recalled) and deep (acquired through years of experience)

Explicit (already codified) and tacit (embedded in the mind)

Procedural (repetitive, stepwise) versus Episodical (grouped by episodes)

Knowledge exist in chunks 2-26


What makes someone an expert?

An expert in a specialized area masters the requisite knowledge

The unique performance of a knowledgeable expert is clearly noticeable in decision-making quality

Knowledgeable experts are more selective in the information they acquire

Experts are beneficiaries of the knowledge that comes from experience

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1. Purpose 2. Statement of Scope & Objectives

2.1 System functions2.2 Users and characteristics2.3 Operating environment2.4 User environment2.5 Design/implementation constraints2.6 Assumptions and dependencies

3. Functional Requirements3.1 User interfaces3.2 Hardware interfaces3.3 Software interfaces3.4 Communication protocols and interfaces

4. Nonfunctional Requirements4.1 Performance requirements4.2 Safety requirements4.3 Security requirements4.4 Software quality attributes4.5 Project documentation4.6 User documentation


Users Versus ExpertsAttribute User ExpertDependence on system High Low to nil

Cooperation Usually cooperative Cooperation not required

Tolerance for ambiguity Low High

Knowledge of problem High Average/low

Contribution to system Information Knowledge/expertise

System user Yes No

Availability for system builder Readily available Not readily available

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Rapid Prototyping Process?

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Build a Task

Structurea Task

Structure the Problem

Make Modifications

Reformulate the Problem

RepeatedCycle(s)

Repeated Cycle(s)


Layers of KM Architecture

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User Interface(Web browser software installed on each user’s PC)

Authorized access control(e.g., security, passwords, firewalls, authentication)

Collaborative intelligence and filtering(intelligent agents, network mining, customization, personalization)

Knowledge-enabling applications(customized applications, skills directories, videoconferencing, decision support systems,

group decision support systems tools)

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

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

The Physical Layer(repositories, cables)

. . . . .

Databases Data warehousing(data cleansing,

data mining)

Groupware(document exchange,

collaboration)

Legacy applications(e.g., payroll)

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2

3

4

5

6

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Knowledge Capture and Transfer Through Teams

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Team performsa specialized task

Knowledge transfer method selected

Evaluate relationship between action and outcome

Outcome Achieved

Knowledge Developer

Knowledge stored in a form usable by others in the organization

Feedback


An illustration

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Zero Low Medium High Very High

Value

InformationData

H T H T TH H H T H

…T T T H T

pH = 0.40pT = 0.60RH = +$10RT = -$8

nH = 40nT = 60

EV = -$0.80

Knowledge

CountingpH = nH/(nH+nT)pT = nT/(nH+nT)

EV=pH RH+ pT RT


CHALLENGES IN BUILDING KM SYSTEMS

Culture — getting people to share

knowledge Knowledge evaluation

— assessing the worth of knowledge across the organization

Knowledge processing

— documenting how decisions are reached

Knowledge implementation

— organizing knowledge and integrating it with the processing strategy for final deployment

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Vision

Foresee what the business is trying to achieve, how it will be done, and how the new system will achieve goals

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Resources

Check on the affordability of the business to invest in a new KM system

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Culture

Is the company’s political and social environment open and responsive to adopting a new KM system?

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Lecture 2 - KNOWLEDGE MANAGEMENT SYSTEMS LIFE CYCLE

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