1. Tapping Social Networks to Leverage Knowledge and Innovation
Patti Anklam
Hutchinson Associates

2. Acknowledgment
Work with Social Network Analysis at Nortel was bootstrapped through participation in research with the Institute for Knowledge-Enabled Organizations (IKO)*
Rob Cross and Andrew Parker, researchers, provided “above and beyond” support for key projects as well as solo projects during my learning process.
*Formerly Institute for Knowledge Management (IKM)
©2003 Patti Anklam

3. Context: Knowledge Management is about Leveraging Capital
Customer
Structural
Human
“Social capital consists of
the stock of active connections among people;
the mutual understanding, trust, and
shared values and behaviors
that bind the members of human networks and communities and make cooperative action possible.”
Don Cohen & Laurence Prusak In Good Company
Social
Knowledge management is an umbrella term for a collection of disciplines, methods, and tools embedded in an information infrastructure that supports creation and sharing of intellectual assets – tangible and intangible – to achieve business goals
Disciplines Methods Tools
Learning Org. Communities of Practice
Process Focus Emergent Learning
Reward systems Harvesting
Knowledge Harvesting
Knowledge Networking
Knowledge Engineering Information Architecture
Document Management
Portals
Collaboration Systems
eLearning
©2003 Patti Anklam

4. The Science of Networks
Multi-disciplinary research and applications
Physics
Cell biology
Internet and WWW
Economics and social sciences
Epidemiology
Homeland security
Supported by mathematical evidence that networks of all types exhibit similar properties and architecture
Social Network Analysis Applications:
Modeling flows of money, information, influence: implications for identifying critical nodes
Organizational genesis, identity and control: the transformation of banking in Renaissance Florence
A theoretical agenda for economic sociology
Strategy homogeneity and market instability: a Lemmings model of financial crises
Impact of social networks on patterns of acceptance of physicians to new drug treatments
©2003 Patti Anklam

5. Metabolic Network
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Source: Albert Laszlo Barabasi

6. A Social Network
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7. The Al Qaeda Network ©2003 Patti Anklam
Khalid Shaikh Mohammed is not in this list … this was developed by working from the hijackers
©2003 Patti Anklam

8. The Premise of Social Network Analysis for Knowledge Management
Successful organizations understand the need to ensure that knowledge and learning are reaching all the parts of the organization that need them.
Knowledge flows along existing pathways in organizations.
To understand the knowledge flow, find out what the patterns are.
Create interventions to create, reinforce, or change the patterns to improve the knowledge flow.
©2003 Patti Anklam

9. Business objectives for doing an analysis
Increased innovation, productivity, and responsiveness through plugging “know-who” gaps
Smarter decisions about organizational changes and establishment of key knowledge roles
Insight into challenges of knowledge transfer and integration following restructuring, mergers, or acquisitions
©2003 Patti Anklam

10. The Methodology
Interview managers and key staff to understand the specific business problems or opportunities
Identify the network
Survey the individuals in the network to determine existing connections among them
Use computer modeling tools to depict the network
Identify opportunities for improvement or potential problems (interviews and workshop)
Design and implement interventions to change the network
Follow up
©2003 Patti Anklam

11. Data Collection and Survey Methods
Qualitative
Survey members of existing social networks to diagnose problems and identify opportunities
Quantitative:
Transaction analysis ( s, phone calls)
Analysis of information artifacts ( , documents, search strings) to identify similarity of interests
©2003 Patti Anklam

12. Qualitative Survey
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13. Survey Questions SNA for knowledge management questions:
Frequency of knowledge exchange
Value of interactions
Knowledge of each other’s knowledge and skills
SNA for organizational development:
Decision-making paths
Trust
Energy
Development of the questions and delivery of the survey must be sensitive and appropriate to the current context of the organization
©2003 Patti Anklam

14. View of a Network
I frequently or very frequently receive information from this person that I need to do my job.
= President
= Operations
= Product Line A
= Small Accounts
Functio n
= Product Line B
= Product Line C
= HR/Finance
= Large Accounts
©2003 Patti Anklam

15. Removing Managers, Administrators, and HR
I frequently or very frequently receive information from this person that I need to do my job.
= Operations
= Product Line A
= Small Accounts
Functio n
= Product Line B
= Product Line C
= Large Accounts
©2003 Patti Anklam

16. Quantitative Analysis Provides Management Insight
Density. Data provides the percentage of information-getting relationships that exist out of the possible number that could exist. It is not a goal to have 100%, but to target the junctures where improved collaboration could have a business benefit.
Frequently or very frequently receive
©2003 Patti Anklam

17. Junctures in Information Flow Target Opportunities for KM
Density. Data provides the percentage of information-getting relationships that exist out of the possible number that could exist. It is not a goal to have 100%, but to target the junctures where improved collaboration could have a business benefit.
©2003 Patti Anklam

18. Combining Question Results
People want to communicate more with those who they already receive information from.
Communicate More
Information
©2003 Patti Anklam

19. Innovation Group
I frequently or very frequently receive information from this person that I need to do my job.
= Portfolio
= Technology
Team
= KM
©2003 Patti Anklam

20. Innovation Group – Who Knows Who?
I frequently or very frequently receive information from this person that I need to do my job. Separated by “do not know this person.”
Everybody knows these people, or knows who they are
Colors represent geographical locations
©2003 Patti Anklam

21. Concepts Represented by Mathematics
Distance: degrees of separation (also referred to as the diameter of a network)
Ties/Degree: in-degree and out-degree represent the number of connections, or ties, to and from a person
Centrality: the extent to which a network is organized around one or more central people
Density: the percentage of connections that exist out of the total possible that could exist
©2003 Patti Anklam

22. Comparative Metrics Provide Benchmarks
©2003 Patti Anklam

23. Using the Results of SNA
Categories of Interventions
Organizational
Leadership work
Restructuring and process redesign
Staffing and role development
Knowledge Management
Tools and technologies (expertise locators, discussion forums, and so on)
Collaborative knowledge exchange and getting acquainted sessions
So that is one example within a given group. We are also finding it very powerful to look at networks that cross organizational boundaries of some form but still need to integrate. For example, new product development teams, top leadership networks or merger and acquisition scenarios.
One projects we did in conjunction with the Systems Research Center at Boston University was to look at a top leadership network of a Global Healthcare company.
Individual action
Personal and public
Personal and private
©2003 Patti Anklam

24. Addressing Concerns
Social Network Analysis practitioners are committed to use SNA in ethical ways, sensitive to individuals.
Interviews are used to validate results with managers before displaying to wide audiences
Results are presented in context
©2003 Patti Anklam

25. Learning from Research
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26. Common Patterns Identified
Clusters: dense subgroups
Connectors: individuals who link to many people in an informal network (in some cases, bottlenecks)
Boundary spanner: individuals who connect networks to other parts of an organization
Information broker: connects clusters within an informal network
Outliers: people less well connected, may be termed “peripheral specialist”
Adapted from “The People Who Make Organizations Go or Stop” Rob Cross and Laurence Prusak Harvard Business Review, June 2002
©2003 Patti Anklam

27. Some Principles from the Science
The structure of networks is not random
Six degrees of separation are but one proof point
Small worlds abound
Ties may be weak or strong
Strength is a factor of frequency and proximity
Weak ties are often more useful than strong ties
The rich get richer
Nodes with many links tend to get more links
Structural holes represent opportunities
Small World Fact: regardless of size of a random network, roughly five random shortcuts reduce the average path length by a factor of 1/2. This would mean that in a big organization, if the average separation among employees is something like 6, then if you introduce five people at random to others (presumably others that they do not know), then the number of degrees of separation will fall to roughly 3.
©2003 Patti Anklam

28. Tie Strength and Community Memberships
Social networks and communities:
People who have more ties join more groups
The more ties people have to others in the same group, the longer they stay in the group
The more ties people have to others outside of the group, the less time they stay in the group
Strong ties to many people in the same group increase the duration of membership longer than weak ties
Weak ties to nonmembers increase the rate of joining new groups
McPherson et al, “Social Networks and Organizational Dynamics”, 1995
©2003 Patti Anklam

29. Let’s Look at Some More Examples
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30. Knowledge Problem? Group
I am likely or highly likely to be more effective if I could communicate more with this person.
= Process
= KM
= Technology
Group
= Manager
©2003 Patti Anklam

31. Communication Problem?
I frequently or very frequently receive information from this person that I need to do my job.
= Europe
= Asia Pacific
HR Group
= Americas
= Manager
©2003 Patti Anklam

32. Quality Problem? Frequently Get Information Need to Communicate More
If everyone is already getting a lot of information from each other, why do they feel the need to communicate more?
©2003 Patti Anklam

33. Summary
©2003 Patti Anklam

34. Why Do an Analysis? Six Myths about Informal Networks*:
To build better networks, we have to communicate more
Everybody should be connected to everybody else
We can’t do much to aid informal networks
How people fit in is a matter of personality (which can’t be changed)
Central people who have become bottlenecks should make themselves more accessible
I already know what is going on in my network
*Rob Cross, Nitin Nohria, and Andrew Parker, MIT Sloan Management Review, Spring 2002
©2003 Patti Anklam

35. SNA Moves People to Action
Provides concrete view of flows and relationships:
Makes concrete how work is happening in comparison to the formal structure.
Makes visible the aspects of a group that we can work with.
Qualitative and Quantitative aspects:
Graphics are very meaningful to people.
Data enable metrics, provide meaningful information when there are very large numbers of people
The combination “cracks the code” of delivering this type of diagnostic data to managers
Proven uses in:
Planning for reorganization (or post-reorganization)
Identifying key people prior to mergers or acquisitions
Succession planning and retention
Knowledge creation and sharing
Improving organizational effectiveness
Uses of social network analysis: There was a book published around 1991 or so entitled Evaluating Medical
Information Systems. One of the chapters was on applying social network
analysis to both physician referrals as well as to explaining
physicians' adoption of and attitudes toward medical information
systems. The book's editors were J. Anderson and C. Aydin. I and
Anderson wrote the chapter.
©2003 Patti Anklam

36. SNA Applications
Target knowledge management programs based on opportunities identified in junctures
Identify and reward individuals for “invisible” work
Identify key individuals for retention
As part of team kick-off for cross-functional or cross-organizational projects
To identify lead users for change management programs
©2003 Patti Anklam

37. Technologies for Identifying and Creating Social Networks
Categories of software
Discovery Systems – Verity, Lotus, Autonomy
Expertise Location – Tacit, Kamoon
Technologies
Natural language processing techniques used in indexing content detect similarity of concepts in an increasingly sophisticated way
Visualization tools aid in navigation of hierarchies and clusters of documents
Recommender systems suggest documents and people to contact based on a worker’s current task
©2003 Patti Anklam

38. More Information SNA Reading List:
©2003 Patti Anklam