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: 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 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 valdis@knetmap.com• Margaret Logan

marglogan@knowinc.com• Eric Zhelka eric@konverge.com• 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