knowledge emerges through the interaction of people in clusters
DESCRIPTION
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!. - PowerPoint PPT PresentationTRANSCRIPT
Knowledge emerges through the interaction of people
in clustersKnowledge emerges through the interaction of people
in clusters
Tacit and Explicit:Measure and Map it
KM WorldWednesday October 31, 2001
Valdis Krebs, Margaret Logan, Eric Zhelka
KnowledgeArtifact!Confirmed Tie
KNETMAP™
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
Artifact Generator
Armstrong Enterprise Capital Model
EFFECTIVITY ( S-C ) = EFFICIENCY X UTILIZATIONX =
EFFECTIVITY ( H-S ) = EFFICIENCY X UTILIZATIONX =
EFFECTIVITY ( H-C ) = EFFICIENCY X UTILIZATIONX =
HUMAN CAPITAL STRUCTURAL CAPITAL
CUSTOMER CAPITAL
VALUE IN WAITING
Armstrong Enterprise Capital Model
KnowledgeAssets
Flow
Human Capital Conductivity
Customer Capital Porosity
Structural Capital Use & Re-Use
Business Reality
Hubert Saint Onge
...FROM ...TOValueadded
Time
MarketDemands
OrganizationalCapability
OrganizationalCapability
MarketDemands
Time
Valueadded
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
Conductivity
vs.
Porosity
Conductivity
Connections
Connections
Con
duct
ivit
yC
o nd u
c tiv
i ty
Conductivity and Porosity
Connections
Con
du
cti v
ity
Time
MarketDemands
OrganizationalCapability
ValueAdded
H. Saint-OngeH. Saint-Onge
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
Network Metrics
• Network size
• Number of relationships
• Clustering Coefficient
• Redundancy
• Effective Network size
• Reach-In* & Reach-Out*
• Porosity*
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
REACH-In
• High REACH-In means that many people reference this individual
• Also applies to knowledge artifacts if it is an influential source document
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
Hubs and Authorities
• High Reach-In is known as an “Authority”
• High Reach-In AND High Reach-Out is known as a “Hub”
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).
KNETMAPTM
A means to monitor the constantly changing dynamics of our
enterprise information flows
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
What if you could query your organization?
How to gather data?
• Surveys?
• Voluntary contributions?
• Daily Question?
• Weekly Question?
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
Via email
From: [email protected] 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 below
To whom do you go for information on Java To whom do you go for information on Java technologies?technologies?
Thank You
Case Study: QofWeek in IT Firm
• Konverge Digital Solutions Inc. (Toronto)
• 25 developers, programmers and systems analysts
• 7 years old
Strategic Objectives
• 30% Growth
• More reuse of code
• Higher awareness of extended expert network
• Customer centricity
• Faster integration of new staff
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?
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
InFlow Results QofW 1
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
To whom do you go to solve complex problems
concerning .Net technologies?
InFlow Results QofW 2
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
To whom do you go to solve
complex problems concerning XML?
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
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...
Results after first hour...
John Macdonald
Nick Smith
Jean Paul RabettPeter Browning
Derrick Hudgell
John Carr
Tony Meo
Terry McCraith
Patrick Caiger-Smith
Ian Sams
Ralph YoungDerek Hatley Gaby Haddad
Matt Roy
Cristian Niculescu
Andrew Moore
Jim Laidler
Ross Brent
Richard Tkaczyk
Brian Walker
Pak Tse
Michael Thelander
Martin TaylorJason Solanki
Helen Rayner
Otto Mendez
Mark ManturowiczBohdan Lewyckyj
Rick Jiang
Ian DingAaron ChengLaurence Lai
Paul Novello
Angelo Del Duca
Hugh McGrory
Andrew Hutton
Jules Oille
Laura Childs
Eric Zhelka
Vid Mehta
Julia Elefano
Susie GuoWilson Hu
Ivona Zimonjic
Mahamed Idle
Brian Bennett
Louisa Hu
Alex Hodyna
Alex Bozzo
Dino Bozzo
Sherwin Shao
Jeremy Brown
Yuchun Huang
Agnelo Dias
Temi Grafstein
Margaret Logan
Karl Erik Sveiby
Alek Wiltshire
Verna Allee
Hubert Saint-OngeLeif Frankling
Jim Armstrong
Valdis Krebs
Charles Armstrong
Not fully integrated yet
Boundary spanners
Right-click on a node for a drop-down menu...
Who are the 6 incoming links?
The six incoming links...
Extended neighbourhood...
30 node extended neighbourhood
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’
Searchable Expertise
• Retrieve previous QofWeek results on a particular issue of expertise
• QofWeek “institutionalizes” information about expertise
Right-clicking on node links to Yellow Page
Yellow Page
Yellow Page contains Artifact List
Artifact Generator
Reach IN/OUT and Inside/Outside T
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
Next Steps
• Repeat questions in 3 month cycles• Develop better questions based on the indicators (see Sveiby’s
Intangible Assets MonitorTM)• Consider automated requests to expert nodes (hubs/authorities)
to populate their Yellow Page with artifacts related to their expertise
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 email is highly effective
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
Addresses known KM Challenges
• Managing tacit and explicit knowledge simultaneously
• Locating internal and external expertise
• Managing loss of critical know-how
Addresses known KM Challenges
• Visualizing the impact of organizational changes
• Encourages knowledge sharing
• Exposes expertise & innovation
• Provides context to static data (databases)
Further Information
• KNETMAP knetmap.com• Valdis Krebs [email protected]• Margaret Logan
[email protected]• Eric Zhelka [email protected]• Krebs Toolkit krebstoolkit.com(January
2002)
Coming soon… First quarter 2002
We thank and acknowledge the support of IRAP,
The Industrial Research Assistance Programof
The National Research Council of Canada