Working Knowledge: How Organizations Manage What They Know

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Working Knowledge How Organizations Manage What
They Know

• By Thomas H. Davenport and Laurence Prusak
• Presented By Jonathan Sage
• Undergraduate Senior in Management Information
  Systems

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Outline

• Chapter 5 Knowledge Transfer
• Chapter 6 Knowledge Roles and Skills
• Chapter 7 Technologies for Knowledge
  
• Management
• Chapter 8 Knowledge Management Projects
• in Practice
• Chapter 9 The Pragmatics of Knowledge
• Management

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Chapter 5 Knowledge Transfer
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Strategies for Knowledge Transfer

• Structured verses spontaneous
• MMC and Sematech

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Water Coolers and Talk Rooms

• Conversations are the most important form of
  work
• Human nature
• New ideas/old problems in unexpected ways

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Water Cooler Limitations

• Stuck on a particular problem
• Major breakthrough

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Talk Rooms

• Popular in Japan
• Expectations for workers
• 20 minutes a day
• Chat about current work

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Virtual Offices

• Discourage informal conversation by nature
• Extra effort to make up difference

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Socializing

• Popular across cultures
• Establish trust
• Focus on rich communication medium, rather than
  lean

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Considerations

• What works in one country isn't universal
• Output culture
• Knowledge is less valuable when widely shared
• Implementation barriers

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Considerations

• Suit organizational and corporate culture
• What works in one country isnt universal
• Recognize the value of low tech, face to face.
• Broaden definition of productivity
• Real work/reading example
• Ample slack time for workers

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Knowledge Fairs and Open Forums

• Create locations and occasions for workers to
  interact informally.
• Knowledge fair bring people together without
  expectations of who should talk to who
• Functionality of structure v. unstructured

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What kinds of knowledge?

• Explicit
• Captured in procedures, documents and DB
• Easy to obtain
• Tacit
• Extensive personal contact
• Partnership, mentoring, apprenticeship.
• Include explicit and tacit

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How to Capture Knowledge

• Programs
• Japanese use old-young model
• Mentoring
• Responsible for colleague one level down
• Technology
• Network of colleagues willing to meet/share
• Videoconferencing
• Record stories/experience to CD/video

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Culture of Knowledge Transfer

• Frictions
• Trust
• Differences
• Time
• Selfish reasons
• Knowledge gap
• Intolerance for mistakes

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Trust and Common Ground

• Proof that change will bring better results
• Language
• Everyday language
• Industry jargon
• Proximity
• New Zealand/Boston Harbor tunnel engineers
• Tech factor

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Status and Reputation

• Status of source
• Reputation of source
• Why?
• Saves time
• Human nature

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Knowledge Transfer

• Transfer Transmission Absorption (and Use)
• Resistance
• Self esteem
• Resistance to change
• US info on fat v. obesity level
• Knowing is not the same as doing

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Velocity and Viscosity

• Velocity
• Enhanced by technology
• Viscosity
• Enhanced by richness of medium
• Inverse relationship
• Mobil Oil example

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Case Study 3M

• Encourage new ideas
• All levels of employees
• Scotch Tape
• Post It Notes

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Chapter 6 Knowledge Roles and Skills
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Knowledge-Oriented Personnel

• Everyone
• Engineers, managers, secretaries
• Needs the right corporate culture to flourish
• McKinsey consulting verses Chaparral steel

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Knowledge Management Workers

• Traditional
• Programmers, system administrators
• New
• Extract knowledge from those who have it
• Format it
• Maintain it
• Need both hard and soft skills

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Knowledge Management Workers

• Assign existing workers to new tasks
• Assign existing teams to become knowledge
  managers
• Knowledge engineers
• Technical communicators

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Managers of Knowledge Projects

• Skilled in
• Project management
• Change management
• Technology management
• Lots of experience
• Open to new ideas

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Chief Knowledge Officer

• Build a knowledge culture
• Create a knowledge management infrastructure
• Technical
• Human
• Make it economically feasible

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Chief Knowledge Officer

• Location of the CKO role
• Stand alone
• Work with IS
• Work with HR

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Chief Learning Officer

• Focus on
• Training
• Education
• Involved in
• Human Resources

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Chapter 7 Technologies for Knowledge Management
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Expert Systems and Artificial Intelligence

• Early predictions
• Expert systems
• McDonnell Douglas landing project

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Case Based Reasoning (CBR)

• Extract knowledge from a series of cases from the
  problem domain
• Success in Customer Service problems

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Implementing Knowledge Technologies

• Considerations
• Data verses knowledge
• On WKID scale
• Hardware requirements (a la large volume
  computers)
• People and interpretations
• Types of people

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Broad Knowledge Repositories

• Usually in document form
• Internet is best example
• Consider false/odd information
• Human internet brokers
• Better than technology
• Emergence of private intranets

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Broad Knowledge Repositories

• Lotus Notes
• Good overall tool, but Web has better outlook for
  future performance/utility
• Steep learning curve
• Becomes difficult to use/find relevant knowledge
  at high volumes

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Broad Knowledge Repositories

• Web based
• Intuitive
• Multiple formats and media supported
• HTML for ease of linking
• Thesaurus
• Expands results/accuracy in online searches
• On keyword searching
• Positive original articles have good knowledge
• Negative potentially inaccurate results

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Broad Knowledge Repositories

• Expert locators
• Problems get experts to give themselves expert
  title
• Get experts to post/update bios.

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Focused Knowledge Environments

• Good for expert systems
• Few experts/many users
• Hard to update
• System must remain stable

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Focused Knowledge Environments

• Constraint Based Systems
• High levels of data, less quantitative than
  neural network
• Narrow problem domains
• Capture and model constraints that govern complex
  decision making
• Usually object oriented
• Easy to update

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Real Time Knowledge Systems

• Case Based Reasoning
• Looks at past problems to solve current
• Used in customer service and support process
• Best when one or two experts construct cases and
  maintain over time
• Know when to add, remove, verify cases

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Longer Term Analysis Systems

• Neural Networks
• Requires time and knowledge in statistics
• Lots of quantitative data and powerful computers
• Keeps user in the dark in terms of explaining the
  results

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Longer Term Analysis Systems

• Data Mining
• Large amounts of data to knowledge
• Humans needed to
• Initially structure the data
• Interpret the data to understand the identified
  pattern
• Make a decision based on knowledge
• Generate hypothesis for analysis

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What Technology Wont Do

• Make things happen by themselves
• Enhance process of knowledge use

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Chapter 8 Knowledge Management Projects in
Practice
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Knowledge Repositories

• Knowledge in documents in one place
• Types
• External knowledge
• Structured internal knowledge
• Informal internal knowledge
• Tacit knowledge
• Community based electronic discussion

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Knowledge Access and Transfer

• Focus on linking possessors and prospective users
  of knowledge
• Yellow Pages

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Knowledge Environment

• Measure or improve value of knowledge capital
• Build awareness and cultural receptivity
• Change behavior as it relates to knowledge
• Improve the knowledge management process

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Projects with Multiple Characteristics

• Development of an expert network
• Development of internal document repositories
• Efforts to create new knowledge
• Development of lessons learned knowledge bases
• A high level description of the KM process
• Use of evaluation and compensation system to
  change behavior

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Success in Knowledge Management Projects

• Growth in resources attached to project
• Growth in volume of knowledge content and usage
• Project is an organizational initiative
• Organization wide familiarity of knowledge
  management
• Evidence of fiscal return

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Factors Leading to Knowledge Project Success

• Knowledge oriented culture
• Technical and organizational infrastructure
• Senior management support
• Link to economics or industry value
• Modicum of process orientation

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Factors Leading to Knowledge Project Success

• Clarity of vision and language
• Nontrivial motivational aids
• Some level of knowledge structure
• Multiple channels for knowledge transfer

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Chapter 9 The Pragmatics of Knowledge Management
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Common Sense About Knowledge Management

• Start with high value knowledge
• Start with a focused pilot project, let demand
  drive additions
• Work along multiple fronts at once
• Dont put off what gives you the most trouble
• Get help throughout the organization ASAP

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Getting Started in Knowledge Management

• Results first, boast later
• Start where its needed most
• Start where knowledge is a factor
• Start outside of your area of expertise
• Do just enough to test the concept
• Start on multiple fronts

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Leveraging Existing Approaches

• Select the right anchor
• Leading with technology
• Leading with quality/reengineering/best practices
• Leading with organizational learning
• Leading with decision making
• Leading with accounting

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Knowledge Management Pitfalls

• If we build it
• Put the personnel manual online
• None dare call it knowledge
• Every man a knowledge manager
• Justification by faith
• Restricted access
• Bottoms up

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Cross Cutting Themes

• The value of the human being
• Recognizing knowledge management
• Easy to fail

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Comments on Working Knowledge

• Material seems dated
• Several examples from small pool of instances
• No quantitative figures to back up claims
• Overall, authors did a good job of introducing
  material

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Additional Insight of Working Knowledge

• American Way"Thomas H. Davenport and Laurence
  Prusak provide much more than another treasure
  map to the knowledge-management fields....They
  offer impressive lodes of actions you can
  actually start on Monday morning."

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Questions?