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Knowledge emerges through the interaction of people in clusters

Knowledge emerges through the interaction of people in clusters. Knowledge emerges through the interaction of people in clusters. Tacit and Explicit: Measure and Map it. KM World Wednesday October 31, 2001 Valdis Krebs, Margaret Logan, Eric Zhelka. KNETMAP™. Knowledge Artifact!.

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Knowledge emerges through the interaction of people in clusters

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  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. KNETMAP™ KnowledgeArtifact! Confirmed Tie

  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 ( H-S ) = EFFICIENCY X UTILIZATION X = STRUCTURAL CAPITAL HUMAN CAPITAL EFFECTIVITY ( H-C ) = EFFICIENCY X UTILIZATION VALUE IN WAITING EFFECTIVITY ( S-C ) = EFFICIENCY X UTILIZATION X = CUSTOMER CAPITAL X =

  7. Armstrong Enterprise Capital Model

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

  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 Conductivity Connections Conductivity Connections

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

  14. Organizational Networks c • 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

  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. Hansen’s 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. KNETMAPTM 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 • Who’s 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 WeekTM • Sent via email • 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 email From: john@konverge.com To: Margaret Logan Subject: Question of the Week. Sent: 10/4/2001 4:53 PM Dear Margaret:Please answer the Question of the Week by clicking on the link belowTo whom do you go for information on Java technologies?Thank You

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

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

  29. 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?

  30. 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 KNETMAPTM

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

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

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

  34. 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 emailed the question at 9AM...

  35. Results after first hour...

  36. Not fully integrated yet Boundary spanners

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

  38. Who are the 6 incoming links?

  39. The six incoming links...

  40. Extended neighbourhood...

  41. 30 node extended neighbourhood

  42. 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’

  43. Searchable Expertise • Retrieve previous QofWeek results on a particular issue of expertise • QofWeek “institutionalizes” information about expertise

  44. Right-clicking on node links to Yellow Page

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