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Knowledge emerges through the interaction of people in clusters Knowledge emerges through the interaction of people in clusters.

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Presentation on theme: "Knowledge emerges through the interaction of people in clusters Knowledge emerges through the interaction of people in clusters."— Presentation transcript:

1 Knowledge emerges through the interaction of people in clusters Knowledge emerges through the interaction of people in clusters

2 Tacit and Explicit: Measure and Map it KM World Wednesday October 31, 2001 Valdis Krebs, Margaret Logan, Eric Zhelka

3 Knowledge Artifact! Confirmed Tie KNETMAP

4 Knowledge Artifacts Artifacts are the tangible things people create or use to help them get their work done. When people use artifacts, they build their way of working right into them. --- Hugh Beyer and Karen Holtzblatt: Contextual Design: Defining Customer-Centered Systems

5 Artifact Generator

6 Armstrong Enterprise Capital Model EFFECTIVITY ( S-C ) = EFFICIENCY X UTILIZATION X= EFFECTIVITY ( H-S ) = EFFICIENCY X UTILIZATION X= EFFECTIVITY ( H-C ) = EFFICIENCY X UTILIZATION X= HUMAN CAPITAL STRUCTURAL CAPITAL CUSTOMER CAPITAL VALUE IN WAITING

7 Armstrong Enterprise Capital Model

8 Business Reality Hubert Saint Onge...FROM...TO Value added Time Market Demands Organizational Capability Organizational Capability Market Demands Time Value added

9 Korn/Ferry International Report More Than 70 Percent of Employees Report Knowledge is Not Reused Across the Company Importing Knowledge is Key…through effective external partners Changing the focus and behaviour of employees at all levels lies at the core

10 Conductivity vs.

11 Porosity

12 Conductivity Connections Conductivity

13 Conductivity and Porosity Connections Conductivity Time Market Demands Organizational Capability Value Added H. Saint-Onge

14 Organizational Networks Closed Network Exploitation Few independent sources of info Little Diversity (more homogeneous) Local Entrepreneurial/Open Network Exploration Many independent sources of info Great Diversity Global c

15 Network Metrics Network size Number of relationships Clustering Coefficient Redundancy Effective Network size Reach-In* & Reach-Out* Porosity*

16 REACH ….a measure of local access in the network i.e. the number of connections that can be reached in one or two steps. Reveals the influence of a node

17 REACH-In High REACH-In means that many people reference this individual Also applies to knowledge artifacts if it is an influential source document

18 REACH-Out High REACH-Out means this individual connects to other individuals who are also good connectors Applies to knowledge artifacts if many influential source documents are referenced

19 Hubs and Authorities High Reach-In is known as an Authority High Reach-In AND High Reach-Out is known as a Hub

20 Hansens T-Manager Metric A ratio of how knowledge is shared freely across the organization (the horizontal part of the T) against the individual business unit performance (the vertical part).

21 KNETMAP TM A means to monitor the constantly changing dynamics of our enterprise information flows

22 An MRI of your organization... All the key players in the various networks Whos not well connected but should be Use and Re-Use of knowledge artifacts What relationship building beyond the borders looks like

23 What if you could query your organization?

24 How to gather data? Surveys? Voluntary contributions? Daily Question? Weekly Question?

25 Question of the Week TM Sent via Each individual response builds an organizational map With each submission, it becomes clear who the experts are…the picture comes into focus as data is submitted

26 Via From: To: Margaret Logan Subject: Question of the Week. To whom do you go for information on Java technologies? Sent: 10/4/2001 4:53 PM Dear Margaret: Please answer the Question of the Week by clicking on the link below To whom do you go for information on Java technologies? Thank You

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29 Case Study: QofWeek in IT Firm Konverge Digital Solutions Inc. (Toronto) 25 developers, programmers and systems analysts 7 years old

30 Strategic Objectives 30% Growth More reuse of code Higher awareness of extended expert network Customer centricity Faster integration of new staff

31 Question of Week Week 1: To whom do you go to solve complex problems concerning.Net technologies? Week 2: To whom do you go to solve complex problems concerning XML? Week 3: To whom do you go to solve complex problems concerning JAVA?

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35 InFlow 3.0 Organizational Network Analysis software Used by int./ext. consultants since 1993 Network Visualization Network Metrics –Centrality –Structural equivalence –Cluster analysis –Small-world analysis –Network vulnerability Two-way data flow with KNETMAP TM

36 InFlow Results QofW 1 QoW 1 : Reach (In) 0.690Agnelo Dias 0.655Young Yang 0.655Yuchun Huang 0.621Wilson Hu 0.586Edna De La Paz 0.448Jeremy Brown 0.379Eric Zhelka 0.310John Morning 0.138Howard Thompson 0.138Louisa Hu 0.103Arik Kapulkin 0.069Dino Bozzo 0.069Steve Chapman 0.034Angelo Del Duca 0.034Hugh McGrory 0.034John Macdonald 0.034Leif Frankling 0.034Sherwin Shao 0.034Susie Guo To whom do you go to solve complex problems concerning.Net technologies?

37 InFlow Results QofW 2 QoW 2 : Reach (In) 0.783Agnelo Dias 0.739Wilson Hu 0.652Jeremy Brown 0.609Dino Bozzo 0.609Young Yang 0.478Alex Bozzo 0.478Louisa Hu 0.348Eric Zhelka 0.261Alex Hodyna 0.261Sherwin Shao 0.261Yuchun Huang 0.217Arik Kapulkin 0.130Brian Bennett 0.130Howard Thompson 0.043Blake Nancarrow 0.043Julia Elefano 0.043Laura Childs 0.043Mahamed Idle 0.043Susie Guo To whom do you go to solve complex problems concerning XML?

38 InFlow Results QofW 3 To whom do you go to solve complex problems concerning JAVA? QoW 3 : Reach (In) 0.750Young Yang 0.708Agnelo Dias 0.708Wilson Hu 0.458Eric Zhelka 0.417Jeremy Brown 0.292Alex Hodyna 0.292Dino Bozzo 0.208Sherwin Shao 0.125Steve Webster 0.083Arik Kapulkin 0.083Brian Bennett 0.083Howard Thompson 0.083John Macdonald 0.083Louisa Hu 0.042Alex Bozzo 0.042Laura Childs 0.042Yuchun Huang

39 Case 2: Two departments... Two newly merged IT departments Question: With whom will you seek opinions on best practices in requirements analysis and writing requirement specifications? We ed the question at 9AM...

40 Results after first hour...

41 Not fully integrated yet Boundary spanners

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43 Right-click on a node for a drop-down menu...

44 Who are the 6 incoming links?

45 The six incoming links...

46 Extended neighbourhood...

47 30 node extended neighbourhood

48 Use and Re-Use [of knowledge artifacts] Encourages better objectivity Encourages better documentation Can be built into the mindset of programmers Indicator for peer code approval A form of signature

49 Searchable Expertise Retrieve previous QofWeek results on a particular issue of expertise QofWeek institutionalizes information about expertise

50 Right-clicking on node links to Yellow Page

51 Yellow Page

52 Yellow Page contains Artifact List

53 Artifact Generator

54 Reach IN/OUT and Inside/Outside T

55 What We Learned 4% respondents entered data (contacts) incorrectly first time (by not understanding the question or by second-guessing the purpose) subsequent QofWeeks went smoothly Need to make data gathering simple and painless

56 Next Steps Repeat questions in 3 month cycles Develop better questions based on the indicators (see Sveibys Intangible Assets Monitor TM ) Consider automated requests to expert nodes (hubs/authorities) to populate their Yellow Page with artifacts related to their expertise

57 Conclusions We can establish quantitative measures for any type of network 52 weekly questions construct a unique organizational profile in one year Gathering survey data via is highly effective

58 Benefits to Membership Encourages networking Excellent feedback system T-metric a useful indicator for both intra-company and inter- company relationship building New employees integrate faster

59 Addresses known KM Challenges Managing tacit and explicit knowledge simultaneously Locating internal and external expertise Managing loss of critical know-how

60 Addresses known KM Challenges Visualizing the impact of organizational changes Encourages knowledge sharing Exposes expertise & innovation Provides context to static data (databases)

61 Further Information KNETMAP knetmap.com Valdis Krebs Margaret Logan Eric Zhelka Krebs Toolkit krebstoolkit.com(January 2002)

62 Coming soon… First quarter 2002

63 We thank and acknowledge the support of IRAP, The Industrial Research Assistance Program of The National Research Council of Canada


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