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

KNETMAP™ Confirmed Tie Knowledge Artifact! KNETMAP™ Confirmed Tie Knowledge Artifact!

Knowledge Artifacts “Artifacts are the tangible things people create or use to help them 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 Artifact Generator

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

Armstrong Enterprise Capital Model Armstrong Enterprise Capital Model

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

Korn/Ferry International Report • “More Than 70 Percent of Employees Report Knowledge is Not 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. Conductivity vs.

Porosity Porosity

Conductivity Connections Conductivity Connections

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

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

Network Metrics • • Network size Number of relationships Clustering Coefficient Redundancy Effective Network 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 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 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 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 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 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 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 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? What if you could query your organization?

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

Question of the Week. TM • Sent via email • Each individual response builds Question of the Week. TM • 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: 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 technologies? Thank You

Case Study: Qof. Week in IT Firm • Konverge Digital Solutions Inc. (Toronto) • Case Study: Qof. Week 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 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 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?

In. Flow 3. 0 • • Organizational Network Analysis software Used by int. /ext. In. Flow 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

In. Flow Results Qof. W 1 To whom do you go to solve complex In. Flow Results Qof. W 1 To whom do you go to solve complex problems concerning. Net technologies? Qo. W 1 : Reach (In) 0. 690 0. 655 0. 621 0. 586 0. 448 0. 379 0. 310 0. 138 0. 103 0. 069 0. 034 Agnelo Dias Young Yang Yuchun Huang Wilson Hu Edna De La Paz Jeremy Brown Eric Zhelka John Morning Howard Thompson Louisa Hu Arik Kapulkin Dino Bozzo Steve Chapman Angelo Del Duca Hugh Mc. Grory John Macdonald Leif Frankling Sherwin Shao Susie Guo

In. Flow Results Qof. W 2 To whom do you go to solve complex In. Flow Results Qof. W 2 To whom do you go to solve complex problems concerning XML? Qo. W 2 : Reach (In) 0. 783 0. 739 0. 652 0. 609 0. 478 0. 348 0. 261 0. 217 0. 130 0. 043 Agnelo Dias Wilson Hu Jeremy Brown Dino Bozzo Young Yang Alex Bozzo Louisa Hu Eric Zhelka Alex Hodyna Sherwin Shao Yuchun Huang Arik Kapulkin Brian Bennett Howard Thompson Blake Nancarrow Julia Elefano Laura Childs Mahamed Idle Susie Guo

In. Flow Results Qof. W 3 To whom do you go to solve complex In. Flow Results Qof. W 3 To whom do you go to solve complex problems concerning JAVA? Qo. W 3 : Reach (In) 0. 750 0. 708 0. 458 0. 417 0. 292 0. 208 0. 125 0. 083 0. 042 Young Yang Agnelo Dias Wilson Hu Eric Zhelka Jeremy Brown Alex Hodyna Dino Bozzo Sherwin Shao Steve Webster Arik Kapulkin Brian Bennett Howard Thompson John Macdonald Louisa Hu Alex Bozzo Laura Childs Yuchun Huang

Case 2: Two departments. . . • Two newly merged IT departments • Question: 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 9 AM. . .

Results after first hour. . . Results after first hour. . .

Not fully integrated yet Boundary spanners Not fully integrated yet Boundary spanners

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

Who are the 6 incoming links? Who are the 6 incoming links?

The six incoming links. . . The six incoming links. . .

Extended neighbourhood. . . Extended neighbourhood. . .

30 node extended neighbourhood 30 node extended neighbourhood

Use and Re-Use [of knowledge artifacts] • Encourages better objectivity • Encourages better documentation 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 Qof. Week results on a particular issue of expertise Searchable Expertise • Retrieve previous Qof. Week results on a particular issue of expertise • Qof. Week “institutionalizes” information about expertise

Right-clicking on node links to Yellow Page Right-clicking on node links to Yellow Page

Yellow Page Yellow Page

Yellow Page contains Artifact List Yellow Page contains Artifact List

Artifact Generator Artifact Generator

Reach IN/OUT and Inside/Outside T Reach IN/OUT and Inside/Outside T

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

Next Steps • Repeat questions in 3 month cycles • Develop better questions based Next Steps • Repeat questions in 3 month cycles • Develop better questions based on the indicators (see Sveiby’s Intangible Assets Monitor. TM) • 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 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 Benefits to Membership • Encourages networking • Excellent feedback system • T-metric a useful indicator for both intra-company and intercompany relationship building • New employees integrate faster

Addresses known KM Challenges • Managing tacit and explicit knowledge simultaneously • Locating internal 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 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 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 Coming soon… First quarter 2002

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