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Working Knowledge: How Organizations Manage What They Know

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Presentation on theme: "Working Knowledge: How Organizations Manage What They Know"— Presentation transcript:

1 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

2 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

3 Chapter 5: Knowledge Transfer

4 Strategies for Knowledge Transfer
Structured verses spontaneous MMC and Sematech

5 Water Coolers and Talk Rooms
‘Conversations are the most important form of work’ Human nature New ideas/old problems in unexpected ways

6 Water Cooler Limitations
Stuck on a particular problem Major breakthrough

7 Talk Rooms Popular in Japan Expectations for workers 20 minutes a day
Chat about current work

8 Virtual Offices Discourage informal conversation by nature
Extra effort to make up difference

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

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

11 Considerations Suit organizational and corporate culture
What works in one country isn’t universal Recognize the value of low tech, face to face. Broaden definition of ‘productivity’ “Real work”/reading example Ample slack time for workers

12 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

13 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

14 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

15 Culture of Knowledge Transfer
Frictions Trust Differences Time Selfish reasons Knowledge gap Intolerance for mistakes

16 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

17 Status and Reputation Status of source Reputation of source Why?
Saves time Human nature

18 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

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

20 Case Study: 3M Encourage new ideas All levels of employees Scotch Tape
Post It Notes

21 Chapter 6: Knowledge Roles and Skills

22 Knowledge-Oriented Personnel
Everyone Engineers, managers, secretaries Needs the right corporate culture to flourish McKinsey consulting verses Chaparral steel

23 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

24 Knowledge Management Workers
Assign existing workers to new tasks Assign existing teams to become ‘knowledge managers’ ‘Knowledge engineers’ ‘Technical communicators’

25 Managers of Knowledge Projects
Skilled in Project management Change management Technology management Lots of experience Open to new ideas

26 Chief Knowledge Officer
Build a knowledge culture Create a knowledge management infrastructure Technical Human Make it economically feasible

27 Chief Knowledge Officer
Location of the CKO role Stand alone Work with IS Work with HR

28 Chief Learning Officer
Focus on Training Education Involved in Human Resources

29 Chapter 7: Technologies for Knowledge Management

30 Expert Systems and Artificial Intelligence
Early predictions Expert systems McDonnell Douglas landing project

31 Case Based Reasoning (CBR)
Extract knowledge from a series of cases from the problem domain Success in Customer Service problems

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

33 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

34 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

35 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

36 Broad Knowledge Repositories
Expert locators Problems: get ‘experts’ to give themselves expert title Get experts to post/update bios.

37 Focused Knowledge Environments
Good for expert systems Few experts/many users Hard to update System must remain stable

38 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

39 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

40 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

41 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

42 What Technology Won’t Do
Make things happen by themselves Enhance process of knowledge use

43 Chapter 8: Knowledge Management Projects in Practice

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

45 Knowledge Access and Transfer
Focus on linking possessors and prospective users of knowledge “Yellow Pages”

46 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

47 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

48 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

49 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

50 Factors Leading to Knowledge Project Success
Clarity of vision and language Nontrivial motivational aids Some level of knowledge structure Multiple channels for knowledge transfer

51 Chapter 9: The Pragmatics of Knowledge Management

52 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 Don’t put off what gives you the most trouble Get help throughout the organization ASAP

53 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

54 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

55 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

56 Cross Cutting Themes The value of the human being
Recognizing knowledge management Easy to fail

57 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

58 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."

59 Questions?

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