Unit I
KM
What is Data?
• Data comprises facts, observations, or perceptions
• Data represents raw numbers or assertions Example:
• A restaurant sales order including two large burgers and two medium-sized vanilla milkshakes.
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What is Information?
• Information is processed data
• Information is a subset of data, only including those data that possess context, relevance, and purpose
• Information involves manipulation of raw data (using knowledge) – data processing / information
processing
– Information systems must meet organizational / user requirements
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Information - Example
• Consider the numbers indicating the daily sales of burgers, vanilla milk-shakes, and other products of a restaurant
– For the restaurant manager
• information – he can use such to make decisions concerning pricing and raw material purchases.
– For the CEO of the restaurant chain
• data only – he need processing to consolidate such data of all the restaurants for his information.
– For most customers
• data – uninteresting things.
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What is Knowledge?
• A justified true belief (Nonaka and Takeuchi)
• It is different from data & information
• Knowledge is at the highest level in a hierarchy with information at the middle level, and data to be at the lowest level
• It is the richest, deepest & most valuable of the three
• Information with direction, i.e., leads to appropriate actions .
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Beyond Knowledge
• Knowledge – the know how
– actionable information
– e.g., Increasing the production
capacity before X’mas each year to handle the extra sales volume;
• Wisdom – the know why
– e.g., why there is increasing sales volume just before X’mas?
– inclination to adjust Data
Information
Knowledge
Wisdom
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Decision Support System
Relating Data, Information, Knowledge to Events
Knowledge
Information
Data Information
System
Decision
Events Use of information
Knowledge
actions
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Knowledge management (definition)
• From the perspective of any enterprise knowledge management (KM) is the systematic and effective utilization of essential information
• Includes knowledge
– identifying,
– restructuring, and – exploitation.
• KM is connected to organizational memory
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Example: Siemens & ShareNet
• At the beginning it was an effort of few people – the support of management got later
• ShareNet is a web-service, which
– stores knowledge
– enables information search – enables communication
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Additional examples
• Microsoft Office Online
– You can comment on help instructions
• Wikipedia
– You can write own definitions and clarifications – See
http://en.wikipedia.org/wiki:FAQ for more details.
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Knowledge terminology
• Data are a collection of:
– Facts
– Measurements – Statistics
• Information is organized or processed data that are:
– Timely – Accurate
• Knowledge is information that is:
– Contextual – Relevant – Actionable.
Having knowledge implies that it can be exercised to
solve a problem, whereas having information does
not.
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KM approaches
• There are two fundamental approaches to knowledge management: :
– process approach – practice approach
• In addition, Turban et al. mention best
practices and hybrid approaches
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Process Approach
• is favored by firms that sell relatively
standardized products since the knowledge in these firms is fairly explicit because of the
nature of the products & services.
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Practice approach
• is typically adopted by companies that provide highly customized solutions to unique
problems. The valuable knowledge for these
firms is tacit in nature, which is difficult to
express, capture, and manage.
The evolution of KM
Information management and KM
KM has undergone a paradigm shift from a static, knowledge- warehouse approach towards a
network approach dynamic communication- focusing more on tacit people-centric approach based or
knowledge. KM is a dynamic especiqlly on cultural
knowledge sharing. problems and motivational issues in
The use of the Information and
communications technology (ICT) was the main driving force of KM in the eighties.
Its primary objective was to help enterprises to deal with the growing amount of data,
information and its usage and flow across the organization.
These included document management systems (DMS), various knowledge
repositories, artificial intelligence, expert systems, business intelligence, decision support systems and computer-supported cooperative work (CSCW).
Most of these are data and technology centric.
• In the nineties, org. came to realize, that it is insufficient to deal with the use of computer and technology to process information alone.
• The next stage of KM broadens into the social, cognitive and business aspects as exemplified by Nonaka and Takeuchi’s work on knowledge creation and conversion. This intellectual capital movement regards knowledge as an intellectual asset, that an organization should build and monitor.
• At the same time, Peter Senge published his influential book
“The Fifth Discipline – the art and practice of learning organization” which departs from the classical scientific management of Frederick Taylor, who focused on the labor productivity of mechanized industrial work, as opposed to the productivity of knowledge workers in the information era.
• Another parallel development is, the awareness that organizational intelligence and social intelligence, as a broaden framework for understanding knowledge management.
90’s
2000’s
• Now, after a decade, the tides come back. The amount of data and information that are accessible is much more than what we could possibly imagine in the eighties.
• In the new knowledge ecosystems, knowledge and information are embedded in the Big Data generated by cloud services and Social Media, at an unprecedented fast rate.
• How should we handle and turn them into useful business knowledge? Who are able to grab these opportunities to possess this kind of business intelligence? These will be the next challenges of KM, and will be discussed later.
• Knowledge management today is in a very flourishing situation.
Many large organizations have already established senior roles of chief knowledge officer, KM directors, and KM managers. Some also have roles like knowledge consultants, knowledge analysts, change agents.
MAKE award
• The London knowledge network has been awarding organizations with the awards, most admired knowledge enterprise, the so-called MAKE award.
• Rewarding organizations would have good practices and excellent performance in knowledge management.
• Now, MAKE has been running for about 16 years or even longer.
Through those years, many MAKE awards have been generated.
There's now a global award, a regional award and also country and city award as well.
• When we regard to academia, KM is a trans-disciplinary area, and is still trying to develop itself as an emerging discipline. It's doing so slowly but doing well.
• Up to now, to the best of our knowledge there's no full undergraduate program on knowledge management, however, across the world there are several master degrees or post-graduate programs in knowledge management providing training for knowledge professionals to become knowledge consultants.
Transformation of K-workers
• Previous workers are now been converted into knowledge workers.
• Previous workers focus more on repetitive tasks and standard procedures.
• K-workers:
– Work smartly and effectively
– If mistakes are identified by one department, it should be transferred to other departments so that same mistakes are not committed again. Or they should not reinvent the wheel again and again.
– Retain expertise and skills (even after retirement of workers) – Shorten the learning curve (shorter prod. Development cycle) – Enhance productivity (ability to collaborate within the
organization and outside as well).
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Dynamic cycle of knowledge
o Firms recognize the need to integrate both explicit and tacit knowledge into a formal
information systems - Knowledge Management System (KMS)
• Phases of knowledge
1. Create knowledge.
2. Capture knowledge.
3. Refine knowledge.
4. Store knowledge.
5. Manage knowledge.
6. Disseminate knowledge.
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Aims of KM initiatives
• to make knowledge visible mainly through
– Maps
– yellow pages – hypertext
• to develop a knowledge-intensive culture,
• to build a knowledge infrastructure
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KM initiatives
• Knowledge creation or knowledge acquisition is the generation of new insights, ideas, or routines.
– Socialization mode refers to the conversion of tacit knowledge to new tacit knowledge through social interactions and shared experience.
– Combination mode refers to the creation of new explicit knowledge by merging, categorizing, reclassifying, and synthesizing existing explicit knowledge
– Externalization refers to converting tacit knowledge to new explicit knowledge
– Internalization refers to the creation of new tacit knowledge from explicit knowledge.
• Knowledge sharing is the exchange of ideas, insights, solutions, experiences to another individuals via knowledge transfer computer systems or other non-IS methods.
• Knowledge seeking is the search for and use of internal organizational knowledge.
KM has evolve from the combination of 2 factors :
1.The business world’s enthusiasm for “intellectual capital”
2.The appearance of corporate intranet (ideal tool to link and organisation together to share and disseminate
knowledge throughout scattered offices and units
INFORMATION MANAGEMENT
● Focuses on information as a resource or collection.
● Practitioners select, describe, classify, index, and abstract this information to make it more accessible within and outside the
organization.
● IM is concerned to provide transparent and standardized access using technology by storing and organize information.
KNOWLEDGE MANAGEMENT
● Focuses on its users.
● Practioners summarize, contextualize, value-judge, rank, synthesize, edit and facilitate to make information and knowledge accessible
between people within or outside their organization. It concerns with the social interactions with sharing and use of knowledge.
● KM is largely based on tacit interpretation that relate to human behavior and interchange.
FROM INFORMATION MANAGEMENT TO
KNOWLEDGE MANAGEMENT
Knowledge Management : The Information – Processing Paradigm 1.The process of collecting, organizing, classifying and dissemination of information to make it purposeful to those who need it Capture knowledge in the mind of in a central repository.
2.Organizing and analyzing information in a companies computer database.
3.Identification of categories of knowledge needed to support overall business strategy
4.Combining, indexing, searching and push technology to help companies organize data stored and deliver only relevant information using Intranet, groupware, data warehouse, networks, and video conferencing.
5.Mapping knowledge and information resources both online and offline Knowledge assets are created through computerized collection, storage and sharing of knowledge
1. Interplay Between Information and Knowledge Information can easily, organized and distributed whereas knowledge resides in one’s mind (human centric)
2. IM and KM Projects: different scopes, approaches and measurement systems KM rely on the willingness of individuals whereas IM rely on technical achievement to enable knowledge sharing
3. Organizational Learning and KM Organization can learn through self-knowledge, dialogue and reuse the existing knowledge into new information
4. Broad Concepts of KM - Time, Context, transformations and dynamics, social space and knowledge culture
5. Protecting Intellectual Capital: IM and KM Perspectives IM used firewall, permission and access level whereas KM used retention policies and circulation of knowledge (senior to junior)
KEY DIFFERENCES BETWEEN INFORMATION
MANAGEMENT AND KNOWLEDGE MANAGEMENT
Tacit knowledge Explicit knowledge
Ability to adapt, to deal with
new an exceptional situations Ability to disseminate, to
reproduce, to access and to reapply throughout the
organization Expertise, know-how, know-why
and care-why Ability to teach, to train Ability to collaborate, to share a
vision, to transmit a culture Ability to organize, to systematize, to translate a vision into a mission statement, into operational
guidelines Coaching and mentoring to
transfer experiential knowledge on one-to-one, face-to-face basis
Transfer of knowledge via products, services and documented processes
Landmarks Fences DMZs
(demilitarized zones)
High-level ethical guideline often built upon the company’s culture
Explicit boundaries that show exactly where an
important ethical lines lies
Concerned with active compliance monitoring
Knowledge as an asset or resource unlike information or data, is not easily understood, classified, shared and measured. It is invisible, intangible and difficult to imitate. Expanding the knowledge base within an organization is not the same as expanding its information base.
What KM is not?
• KM is not reengineering.
• KM is not a discipline
• KM is not intellectual capital
• KM is not based on information
• KM is not about data
• KM is not digital networks
• KM is not about knowledge capture.
KM Life Cycle
• The building of knowledge management system can be viewed as a life cycle that begins with a master plan and justification and ends with a system structured to meet KM requirements of entire company.
• The most critical phase of KM systems life cycle is identifying the immediate, intermediate and long term needs for the prospective system.
• KM System Life Cycle centers around 3 questions:
– What is the problem that warrants a solution by KM system? How important is the problem? What clues indicate that the system should be built? What will user and company will gain from the system?
– What development strategy should be considered? Who is going to build the system?
– What process will be used to build the system?
• Eg. of Life cycle:
– College: Admission, education, graduation
– Air Flight: Boarding, take off, cruising, landing, disembarking – Faculty: Assistant Prof, Associate Prof, Professor
• Structure and order are to KMSLC what chapters and paragraphs are to book.
Challenges in building KM systems
• Culture
– People do not tend to share knowledge
– That is why reward system needs to be in place.
– Incentives should be given to employees who collaborate and share their knowledge.
• Knowledge evaluation
– Assessing the worth of information is a crucial step if a company wants to refine its methods.
– Generation of “best” knowledge is what company wants.
– No reliable metrics are developed still to evaluate “best” knowledge.
• Knowledge processing
– Human element in KM is very critical. Effective KM systems must allow org. not only to store and access information but to document how decisions were reached as well.
• Knowledge Implementation
– One of the important task for KM is to extract meaning from information that will have impact on organizations.
– Lesson learnt in the way of feedback are stored for others facing the same problem in the future.
Conventional Vs KM System Life Cycle
Conventional System Life Cycle Recognition of Need and Feasibility Study
Software requirement specifications Logical Design
Physical Design Testing
Implementation
Operations & Maintenance
KM System Life Cycle
Evaluate existing infrastructure Form KM team
Knowledge Capture Design KM Blueprint
Verify & Validate KM system Implement the KM System
Manage Change & rewards Structures Post system evaluation
Corrections Corrections
Due to lack of standardization in the field of KM, several approaches have been proposed for KMSLC.
KMSLC Hybrid Approach
• Earlier some systems used conventional life cycle approach and some use KMS life cycle approach strictly.
• A hybrid model is mostly used in order to develop and distribute knowledge.
• Since conventional and KM System life cycle
are fundamentally similar, therefore both can
be used to design the hybrid approach.
KM System life cycle (KMSLC)
1. Evaluate existing infrastructure
• System Justification
– Is current knowledge is going to be lost through retirement, transfer or departure to other firms?
– Is proposed KM system needed in several locations?
• For single expert, it is difficult to handle similar problems elsewhere. (eg. Kuwait oil fire took 18 months to put out at the time of Gulf war).
– Are experts willing to help build KM system?
– How critical is the knowledge which needs to be captured?
– Is there a champion in the house?
• Willing support to KM systems aggressively.
• Scope Factor
– Decide on limiting the breath and depth of the project financial, human resource and operational constraints.
• The feasibility Question – Is the project doable?
– Is it affordable?
– Is it appropriate?
– Is it practical?
2. Form the KM team
• After evaluation of the existing infrastructure, a KM team should be formed.
• Identify the key units, departments, branches or divisions as the key stakeholders in the KM system.
• Form a balanced complement of experts in each of these areas to collaborate on multidimensional nature of KM system building process.
• Things to remember:
– Caliber of team members – Team size
– Complexity of project
– Leadership and team motivation..
3. Knowledge Capture
• Explicit knowledge is captured in repositories from documentation, files and other media.
• Tacit knowledge is captured from company experts and from knowledge stored in databases for all authorized employees.
• Various tools and techniques will be discussed later.
• It involves eliciting, analyzing and interpreting the knowledge that a human expert use to solve a particular problem.
• Sometimes interviewing the ultimate user of KMS is just as important as interviewing the expert whose knowledge you are trying to capture.
Knowledge Capture and Transfer
Team performs
specialized tasks Outcome achieved
Evaluate Relationship between Action and
Outcome
Knowledge Transfer Method Selected
Knowledge Developer
Knowledge Stored in Form Usable by Others
in the Organization
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Selecting an Expert
• Knowledge base should represent expertise rather than the expert
• Questions facing knowledge developer:
– 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 would the knowledge developer know what is and what is not within the expert’s area of expertise?
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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
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Central Role of the Knowledge Developer
KNOWLEDGE WORKER
KNOWER CHAMPION
KNOWLEDGE DEVELOPER
KNOWLEDGE BASE Interactive
Interface
Solutions
User Acceptance
Rules
Testing Knowledge
Support Feedback
Prototypes
Progress Reports
Demos
4. Design a KM Blueprint
• This phase is the beginning of designing IT infrastructure and the KM architecture.
• Also referred to as KM system design.
• Aims to scale and interoperate as per the existing system IT infrastructure.
• Develop key layers where ever possible.
• Create databases, data warehouse etc.
5. Test the KM System
• Involves two steps
– Verification procedure – ensures the system is right
• The program do what they are designed to do.
• Right knowledge in right format, when needed, is
available with the set of rules that are supposed to be there.
– Validation procedure – the system in place is the right system i.e. user expectations are met.
• System should be user friendly and that it should be usable and scalable on demand.
• It cross checks reliability of the system.
6. Implement the KM System
• Once appropriate knowledge is captured, encoded in the knowledge base, verified and validated, the next task is to implement or deploy the proposed system.
• Implementation means converting a new KM system into actual operation.
• Conversion is the major step in implementation.
• Quality Assurance: The KM system should be free from:
– Reasoning errors – Ambiguity
– Incompleteness
– False representation
7. Post System Evaluation
• After KM system has been deployed and the operation is “up and running”, the effect of new system on the organization should be carefully evaluated.
• Assessment should be w.r.t. effect on people, procedures and performance of business.
• Several key questions need to be addressed at this post implementation stage:
– How has the KM system changed the accuracy and timeliness of decision making?
– Has the new system caused organizational change? How constructive the change is?
– Has the new KM system affected the attitude of the end users?
– Was it worth it?
– Has the new KM system changed the cost of operating the business? How significant it was?
– Do the solutions derived from the new system justify the cost of investment?
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KM success factors
• There should be a link to a firm’s economic value-business processes should be connected to KM
– For example
• Development of new products process
• Customer service process
• Technological infrastructure and knowledge infrastructure
• Organizational culture should be ready for KM
• Introducing a system to employees
– (In the first phase prototypes and demos are useful, if the ideology of KM is new for a firm)
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KM failures
• Failure rate range from 50% to 70%
– Major objectives are not reached
• Some reasons
– Information may not be easily searchable
– Inadequate or incomplete information in a system – Lack of commitment
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Example again: Siemens & ShareNet
• Employees were supported and encouraged to adopt KM
– Communication – Training
– Rewards
• Top management’s full support
• Maintenance team which was responsible for
the validity of knowledge
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Implementing solution like at Siemens
• Knexa-see features at
http://www.knexa.com/features.shtml
CAPTURING TACIT KNOWLEDGE
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Knowledge Codification in the KM System Life Cycle
KNOWLEDGE CAPTURE
(Creation)
KNOWLEDGE TRANSFER
KNOWLEDGE SHARING TESTING AND DEPLOYMENT KNOWLEDGE
CODIFICATION
KNOWLEDGE BASE DATABASE
DATABASES
Collaborative tools, networks, Intranets
Shells, tables, tools, frames maps, rules Capture Tools
Programs, books, articles, experts
Web browser, Web pages Distributed systems Intelligence
gathering
KNOWLEDGE INNOVATION
Insight GOAL Explicit Knowledge
Logical Testing, User Acceptance Testing, Training
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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
• 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. Three steps are involved:
– Using appropriate tool to elicit information from the expert.
– Interpreting the information and inferring the expert’s underlying knowledge and reasoning process.
– Using the interpretation to build the rules that represent the expert’s thought process or solutions.
• Knowing how experts know what they know is the bottom line
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Improving the Knowledge Capture Process
• Knowledge developers should focus on how experts approach a problem
• Look beyond the facts or the heuristics
• Re-evaluate how well knowledge developers understand the problem domain and how accurately they are modeling it.
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Indicators of Expertise
Without a good match of expert and project, knowledge developer could spend hours questioning someone whose knowledge might be unusable in the proposed system.
Several indicators of expertise are:
• 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
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Expert’s Qualifications
• Knows when to follow hunches and when to make exceptions
• Sees big picture
– Follow information and readily determine which factors are important and which are not.
• Possesses good communication skills
• Tolerates stress
• Thinks creatively
• Exhibits self-confidence
• Maintains credibility
• Operates within a schema-driven orientation
– More structured approach to a problem. Eg. Chess player description.
• Uses chunked knowledge
– Recalling information.
• Generates motivation and enthusiasm
• Shares expertise willingly
• Emulates a good teacher’s habits
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Pros and Cons of Using a Single Expert
Advantages:
• Ideal when building a simple KM system
• A problem in a restricted domain (area).
• Facilitates the logistics aspect of coordinating arrangements for knowledge capture.
– More easy to schedule meetings.
• Problem-related or personal conflicts are easier to resolve
• Shares more confidentiality with project-related information than does multiple expert
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Pros and Cons of Using a Single Expert (cont’d)
Drawbacks:
• The expert’s knowledge is not easy to capture.
– If the expert has difficulty explaining and communicating procedures, the project can die quickly.
• 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.
– Relying on a single expert for complex systems can create blind spots or system with no users.
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Pros and Cons of Using Multiple Experts
Advantages
• Complex problem domains benefit from the expertise of more than one expert.
– For example: building a KM system to predict the next direction of a given stock in BSE requires a talent of pool of highly reasoned traders.
• Working with multiple experts stimulates interaction.
– Enriches the quality of knowledge capture.
• 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.
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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
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Developing a Relationship With Experts
• Create the right impression
• Do not underestimate the expert’s experience
• Prepare well for the session
• Decide where to hold the session
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Styles of expert’s expressions
• Procedure type—methodical approach to the solution. Emphasis on structure rather than content.
• 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. Dancing around the topic approach.
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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
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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
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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
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Types of Interviews
• Structured: Questions and responses are definitive.
Used when specific information is sought
• Semistructured: Predefined questions are asked but allow expert some freedom in expressing the
answers
• Unstructured: Neither the questions nor their responses specified in advance. Used when exploring an issue
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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
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Guide to a Successful Interview
• Set the stage and establish rapport
• Properly phrase the questions
• Question construction is important
• Listen closely and avoid arguments
• Evaluate session outcomes
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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
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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
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Problems Encountered During the Interview
• Response bias
• Inconsistency
• Communication difficulties
• Hostile attitude
• Standardized questions
• Lengthy questions
• Long interview
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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?
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Questions for Discussion
• Do you think knowledge capture can be fully or easily automated? Why or why not?
• If you were asked to select an expert, how would you proceed? What characteristics would you look for?
What other factors would you consider?
OTHER KNOWLEDGE CAPTURE
TECHNIQUES
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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
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Brainstorming
• More than one expert unlike on-site observation
• 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
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Brainstorming Procedure
• Introduce brainstorming session
• Give experts a problem to consider
• Prompt experts to generate ideas
• Watch for signs of convergence
• Call for a vote or a consensus to reach
agreement
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Electronic Brainstorming
• Computer-aided approach to dealing with multiple experts
• Begins with a pre-session plan that identifies objectives and structures the agenda
• Experts gain leverage from anonymity
• Allows two or more experts to provide opinions through PCs without having to wait their turn
• Protects shy experts and prevents tagging comments to individuals
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Protocol Analysis
• Think-aloud method.
• Expert follows a protocol to solve a problem
• Expert keeps talking, speaking out loud while solving a problem. Eg: Physician diagnosing patients.
• Effective source of information on cognitive processes
• Makes expert aware of the processes being described
• Two experts might give same answer, but they might use different problem solving techniques
• How the solvers approached the problem one step at a time.
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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
• Voting can be used sometimes in order to reach a consensus.
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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
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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
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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 which is based on writing rather than discussion.
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NGT (cont’d)
• Technique can be time consuming
• Has been known to promote impatience among experts who must listen to discussions with other experts
• With experts sharing expertise, things can jell in adopting the best solution
• NGT is ideal in situations of uncertainty
regarding the nature of the problem
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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
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Delphi Method
• A survey of experts
• A series of questionnaires used to pool experts’
responses in order to solve a difficult problem
• Each expert’s contributions shared with rest of experts by using results of one questionnaire to construct the next questionnaire
• Three important features of this method:
– Anonymous response
– Controlled feedback
– Statistical group response
• Poorly designed questionnaire could cause all
kinds of problems
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Concept Mapping
• A unique tool to represent knowledge in graphs.
• A network of concepts, consisting of nodes and links
• A node represents a concept and a link represents the relationship between concepts. See Fig. 1.5
• Helpful in designing a complex structure, ideas, diagnose problems etc.
• An effective way for a group to function without losing their individuality
• Structured conceptualization where one or more experts produces a clear pictorial view of their ideas and their concepts.
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Figure 1.5 Conceptual Map—An Example
helper of Bear
d
Santa Clause
White horse Birthday
At
chimneys On roofs
Spain
climbs listens
lives in
brings gives
not same as has
has
lives in rides
SAINT
NICOLAS BLACK
PETER
Presents
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Figure 1.6 Steps in Concept Mapping
STEPS IN CONCEPT MAPPING
1 Preparation of Project
Participants, focus, schedule
2 Idea Generation
(focus for brainstorming)
3 Idea Structuring
(sorting/rating statements)
4 Statement Representation (cluster analysis)
5 Interpretation
6Utilization
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Blackboarding
• Imagine bringing a group of experts together in a room with a large blackboard.
• 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
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Blackboarding (cont’d)
• Characteristics of Blackboarding
– Diverse approaches to problem solving
– Participants share a common language for interaction
– Flexible representation of information
– Efficient storage and location of information – Organized participation
– Iterative approach to problem solving
