Tapping Social Networks to Leverage Knowledge and Innovation Patti Anklam Hutchinson Associates patti@byeday.net

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

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

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

Metabolic Network ©2003 Patti Anklam Source: Albert Laszlo Barabasi

A Social Network ©2003 Patti Anklam

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 http://www.orgnet.com/MappingTerroristNetworks.pdf

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

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

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

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

Qualitative Survey ©2003 Patti Anklam

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

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

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

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

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

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

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

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

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

Comparative Metrics Provide Benchmarks ©2003 Patti Anklam

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

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

Learning from Research ©2003 Patti Anklam

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

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

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

Let’s Look at Some More Examples ©2003 Patti Anklam

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

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

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

Summary ©2003 Patti Anklam

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

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

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

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

More Information SNA Reading List: http://patti.byeday.net/sna/ email: patti@byeday.net ©2003 Patti Anklam