Tacit and Explicit: Measure and Map it

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Tacit and ExplicitMeasure and Map it

• KM World
• Wednesday October 31, 2001
• Valdis Krebs, Margaret Logan, Eric Zhelka

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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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Network Analysis Reveals...

• informal leadership of the group
• influencers on products/processes/services
• product/process experts (hubs and authorities)
• fragmentation and structural holes
• communities of practice/interest
• the use and re-use of knowledge artifacts
• the reach of people and the organization

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Community of Practice
KnowledgeArtifact!
Confirmed Tie
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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
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Artifact Generator
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Armstrong Enterprise Capital Model
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Armstrong Enterprise Capital Model
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Conductivity
vs.
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Porosity
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Conductivity
Conductivity
Connections
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Conductivity and Porosity
Value Added
Organizational Capability
Market Demands
Time
H. Saint-Onge
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Organizational Networks
c

• Closed Network
• Low Performance
• Few independent sources of info
• Effective size is smaller
• Little Diversity (more homogeneous)
• Dense

• Entrepreneurial/Open Network
• High Performance
• Many independent sources of info
• Effective size is greater
• Great Diversity
• Not as dense

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Network Metrics

• Network size
• Number of relationships
• Clustering Coefficient
• Redundancy
• Effective Network size
• Reach-In Reach-Out
• Porosity

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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

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REACH-In

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

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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

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Hubs and Authorities

• High Reach-In is known as an Authority
• High Reach-In AND High Reach-Out is known as a
  Hub

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KNETMAPTM

• A means to monitor the constantly changing
  dynamics of our enterprise information flows

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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

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What if you could query your organization... and
get this information daily or weekly?
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How to gather data?

• Surveys?
• Voluntary contributions?
• Daily Question?
• Weekly Question?

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Question of the WeekTM

• Sent via email
• Each individual response builds an organizational
  map
• With each submission, it becomes clear who the
  experts arethe picture comes into focus as data
  is submitted

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Korn/Ferry International Report

• More Than 70 Percent of Employees Report
  Knowledge is Not Reused Across the Company
• Importing Knowledge is Keythrough effective
  external partners
• Changing the focus and behaviour of employees at
  all levels lies at the core

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Case Study QofWeek in IT Firm

• Konverge Digital Solutions Inc. (Toronto)
• 25 developers, programmers and systems analysts
• 7 years old

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Strategic Objectives

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

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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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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

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InFlow Results
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
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InFlow Results
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
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InFlow Results
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
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Two departments...

• Two newly merged IT departments
• With whom will you seek opinions on best
  practices in requirements analysis and writing
  requirement specifications?

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Results after first hour...
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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

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Searchable Expertise

• Retrieve previous QofWeek results on a particular
  issue of expertise
• QofWeek institutionalizes information about
  expertise

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Right-clicking on node links to Yellow Page
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Yellow Page
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Yellow Page contains Artifact List
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Artifact Generator
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Reach IN/OUT and Inside/Outside T
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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

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Next Steps

• Repeat QofWeekTM in 3 months
• Develop better questions based on the indicators
  (see Sveibys Intangible Assets MonitorTM)
• Consider automated requests to expert nodes
  (hubs/authorities) to populate their Yellow Page
  with artifacts related to their expertise

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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

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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

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Addresses known KM Challenges

• Managing tacit and explicit knowledge
  simultaneously
• Locating internal and external expertise
• Managing loss of critical know-how

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Addresses known KM Challenges

• Visualizing the impact of organizational changes
• Encourages knowledge sharing
• Exposes expertise innovation
• Provides context to static data (databases)

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Further Information

• KNETMAP knetmap.com
• Valdis Krebs valdis_at_knetmap.com
• Margaret Logan marglogan_at_knowinc.com
• Eric Zhelka eric_at_konverge.com
• Krebs Toolkit krebstoolkit.com(January 2002)

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Coming soon First quarter 2002
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We thank and acknowledge the support of The
National Research Council of Canada