Ona For Community Roundtable


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A high-level overview of social network analysis, providing background on how it came into the knowledge management field. Includes an example and core concepts pertinent to the audience, online community managers.

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  • Topic: Introduction to Social Network Analysisfor Community Roundtable Members, to provide:Overview of the concepts and usesOrganizational exampleKey points
  • These is the flow of the talk. I will conclude with some notes about work that I am currently doing with a client that is trying to look at social network measures as a way to determine the benefits of using a social networking platform.
  • http://orgnet.com/IHJour_XII_No5_p38_42.pdf
  • Ona For Community Roundtable

    1. 1. Finding the Patterns of Connection<br />Patti Anklam The Community Roundtable, December 9, 2009<br />Social Network Analysis<br />
    2. 2. About Me<br />Knowledge management practitioner, then consultant<br />Using enterprise collaboration tools since 1980<br /><ul><li>Currently specializing in knowledge management, collaboration and social networks</li></li></ul><li>We live in networks all the time: communities, organizations, teams<br />There is science to support the understanding of network structure<br />The structure of a network provides insights into how the network “works”<br />Once you understand the structure, you can make decisions about how to manage the network’s context: this is your “net work”<br />Net Work<br />The Premise: Net Work<br />
    3. 3. Network Science<br />Organizational Work<br />4<br />Practice<br />
    4. 4. 1967: Six Degrees of Separation<br />Omaha<br />Boston<br />Stanley Milgram, Yale University<br />
    5. 5. The Science of Networks<br />Roots in sociology/sociometry (Jacob Moreno, 1930s)<br />Stanley Milgram’s work in 1967 inspired the phrase “six degrees of separation” <br />Mathematicians convened around the topic in 1975<br />INSNA (International Network for Social Network Analysis) founded in 1978 – expanding the discipline to include sociologists, management specialists, anthropologists, and other disciplines<br />Late 1980s and early 1990s, Karen Stephenson, Valdis Krebs, and Gerry Falkowski began doing research at IBM<br />Late 1990s, Rob Cross, Andrew Parker, and Steve Borgatti developed a research program at IBM’s Institute for Knowledge Management<br />Rob Cross’s book, The Hidden Power of Social Networks, was published in 2004. <br />
    6. 6. 2009: Going Mainstream<br />
    7. 7. <ul><li>If it’s a network, you can draw it: -- People (Nodes) -- Relationships (Ties)
    8. 8. Relationships can be analyzed:</li></ul>-- Counted, summed, averaged<br />-- Grouped, segmented<br /><ul><li> Illustrating:</li></ul> -- How separated the network is -- Who the central people are -- How connected the network is<br /> -- Who does the invisible work<br /><ul><li> Patterns matter -- Demographics tell the story</li></ul>Question<br />What we learned from the science<br />
    9. 9. <ul><li>Work (knowledge, decisions, problem-solving, meaning) flows along existing pathways in organizations.
    10. 10. To understand the flow, find out what patterns exist.
    11. 11. Create a conversation to understand what shows up to be working and what is not
    12. 12. Design interventions to create, reinforce, or change the patterns to guide change toward a desired outcome.</li></ul>I frequently or very frequently receive information from this person that I need to do my job. <br />Question<br />What we learned from KM<br />
    13. 13. Patterns of Performance<br />At work:<br />High performers have better networks<br />People with better networks stay in their jobs longer<br />Network-savvy managers are more likely to be promoted<br />People with higher social capital coordinate projectsmore effectively<br />
    14. 14. Patterns of Well-being<br />In life:<br />People with strong networks have a better chance of full recovery from heart attacks<br />We are defined by the networks we are in<br />Obesity studies<br />Smokers<br />New York Times,, May 22, 2008<br />
    15. 15. Patterns of Effectiveness<br />Source: http://www.robcross.org/sna.htm<br />
    16. 16. Scattered clusters<br />Hub-and-Spoke<br />Multi-hub<br />Core Periphery<br />Time<br />Where most network-building begins<br />Self-sustaining network <br />Source of network maps: Valdis Krebs<br />Patterns of Network Growth<br />
    17. 17. Patterns in Connection<br />Strong ties: <br />Close, frequent<br />Reciprocal<br />Weak ties<br />Infrequent interaction<br />No emotional connection<br />Absent ties<br />No personal connection beyond “nodding”<br />Dunbar’s number: 150<br />
    18. 18. Organizational Networks:“The Office Chart that Really Counts”*<br />*http://www.businessweek.com/magazine/content/06_09/b3973083.htm<br />Map: MWH Global, Vic Gulas<br />
    19. 19. SNA in Organizations (1999)<br />…a targeted approach to improving collaboration and network connectivity where they yield greatest payoff for an organization – Rob Cross & Andrew Parker<br />… a mathematical and visual analysis of relationships / flows / influence between people, groups, organizations, computers or other information/knowledge processing entities– Valdis Krebs<br />
    20. 20. Here’s the case of the collaborative cabinet:<br />Professional services firm reorganized three months prior, with a goal to enhance collaboration across<br />Three product lines<br />Two industry segments<br />The executives “talked a good game” about collaboration<br />But Sr. VP wasn’t seeing it<br />Offsite meeting was planned to work on “improving collaboration”<br />
    21. 21. The Sr. VP, his direct reports and all of their direct reports responded to a questionnaire<br /><ul><li>Preparation:
    22. 22. Message from Sr. VP about importance of survey
    23. 23. Data collection:
    24. 24. Excel Spreadsheet</li></li></ul><li>Questions: The Art of an SNA<br />Problem (Examples)<br />Relationships of Interest<br />Improve collaboration<br />Finding key connectors in organizations and communities<br />Leadership development<br />Performance benchmarking<br />Mergers and acquisitions<br />Knowledge in the retiring workforce<br />Know-about<br />Information flow<br />Communication<br />Energy<br />Problem-solving<br />Decision-making<br />Sense-making<br />… many more<br />Shares new ideas with<br />Knows expertise of<br />Works closely with<br />Seeks help for problem-solving<br />
    25. 25. Questions: The Art of an SNA<br />Problem (Examples)<br />Relationships of Interest<br />Improve collaboration<br />Finding key connectors in organizations and communities<br />Leadership development<br />Performance benchmarking<br />Mergers and acquisitions<br />Know-about<br />Information flow<br />Communication<br />Energy<br />Problem-solving<br />Decision-making<br />Sense-making<br />… many more<br />Shares new ideas with<br />Knows expertise of<br />Works closely with<br />Seeks help for problem-solving<br />
    26. 26. The Network Map<br />I frequently or very frequently receive information from this person that I need to do my job. <br />Function<br />= Small Accounts<br />= Large Accounts<br />= Product Line A<br />= Product Line B<br />= Product Line C<br />= Operations<br />
    27. 27. …and show patterns of individual roles<br />Patterns of Groups<br />Network Measures<br />Density = 15%<br />Cohesion = 2.6<br />Centrality = 6<br />
    28. 28. …and show patterns of individual roles<br />Peripheral specialists<br />Information broker<br />Central connector<br />Well-positioned<br />Influencer<br />Patterns of Individual Roles<br />Structural Hole<br />
    29. 29. Density analysis shows group-to-group patterns<br />SmA<br />Ops<br />PL A<br />PL B<br />PL C<br />LgA<br />10<br />5<br />8<br />8<br />9<br />10<br />Small Accounts<br />72%<br />2%<br />11%<br />0%<br />2%<br />5%<br />Operations<br />4%<br />85%<br />10%<br />5%<br />7%<br />12%<br />Product Line A<br />8%<br />3%<br />77%<br />0%<br />1%<br />4%<br />Product Line B<br />0%<br />13%<br />2%<br />73%<br />0%<br />17%<br />Product Line C<br />2%<br />16%<br />1%<br />3%<br />54%<br />17%<br />Large Accounts<br />2%<br />18%<br />5%<br />16%<br />12%<br />73%<br />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.<br />Frequently or very frequently receive<br />
    30. 30. Moving into Action<br />Often the presentation of the results provides sufficient self-awareness for the group to move into action<br />Typical actions fall into three broad categories:<br />Make an organizational shift or adjustment: role change, role addition, relocation, etc.<br />Increase the knowledge capacity of the organization: provide opportunities for people to meet, to find one another on the web, add blogs, etc.<br />Focus on individual behaviors of key people to distribute knowledge sharing across the organization<br />
    31. 31. Impact of this Analysis Project<br />Organizational response: change the context<br />Established new roles for liaison<br />Clarified role of “single point of contact”<br />Develop the networks of relationships<br />Within groups: face-to-face<br />Across groups: put people on teams together<br />Establish cross-group presence at staff meetings<br />Individual<br />Reallocation of decision-making<br />Private and public commitments to change behavior<br />
    32. 32.
    33. 33. Practice Points<br />
    34. 34. Basic Steps<br />Identify the business problem and the scope of the network<br />Collect data about the relevant relationships<br />Use computer analysis tools<br />Validate the findings through interviews and workshops<br />Design and implement interventions to change the network<br />Follow up<br />
    35. 35. Using Metrics to Pinpoint Key Roles<br /><ul><li>Degree
    36. 36. most likely to influence and be influenced directly
    37. 37. Closeness
    38. 38. most likely to find out first
    39. 39. Betweenness
    40. 40. most likely to broker and synthesize diverse info
    41. 41. Eigenvector
    42. 42. most likely to influence and be in the know</li></ul>Source: Bruce Hoppe<br />
    43. 43. Analytics in Large Data Sets<br />Coordinator- This person connects people within their group.<br />Gatekeeper - This person is a buffer between their own group and outsiders. Influential in information entering the group<br />Representative - This person conveys information from their group to outsiders. Influential in information sharing.<br />Consultant – This person acts as a mediator between two people<br />Liaison – This person connects two people in different groups<br />
    44. 44. Maps Can Measure Progress<br />Map: MWH Global Sep 2004<br />Map: MWH Global Aug 2003<br />
    45. 45. Issues and Challenges<br />Constructive interpretation<br />Remembering that the network analysis doesn’t provide “truth” but that its primary value is to provoke really, really good questions<br />Privacy and confidentiality<br />Responsible consultants address these in the design and communications program that is part of an ONA. Results in many cases are shown only anonymously.<br />
    46. 46. Emergence of Social Media<br />Blogging (c. 1999)<br />Communispace (1999)<br />Friendster, Ryze (2002)<br />LinkedIn, SpaceBook, Del.icio.us, (2003)<br />Facebook (2004)<br />Twitter (2006)<br />
    47. 47. Social media in action (this week!) <br /><ul><li>DARPA’s Red Balloon Challenge</li></ul> -- Find the 10 balloons in the U.S. -- Identify by Latitude and Longitude<br /> -- In one day -- $40,000 prize<br /><ul><li>MIT’s winning entry:</li></ul> -- Web site registration and broadcast -- Distributed prize money to finders and<br /> those who linked to them<br /> -- Details of how they mapped the links to<br /> the winners is yet to be disclosed<br />
    48. 48. Data Gathering<br />email mining is common in research environments <br />apis are available for most social networking/ media apps<br />“Easy” to gather data about who’s connected to whom<br />Graphs of thousands and hundreds of thousands of people are possible<br />
    49. 49. What is a relationship?<br />
    50. 50. How do you measure “goodness”?<br />Activity?<br />Operational trends?<br />Behavior change?<br />Organizational outcomes?<br />Social Capital<br />Quantitative<br />Qualitative<br />
    51. 51. Summary<br />The work of the next decade is to develop our capabilities in creating and managing network structures<br />The science continues to advance our understanding<br />We can use our knowledge of the structure of networks and theirproperties to better serve individual, organizational, and societal goals<br />
    52. 52. <ul><li>patti@pattianklam.com
    53. 53. http://www.pattianklam.com
    54. 54. http://www.twitter.com/panklam
    55. 55. http://www.theappgap.com/?author_name=panklam</li></ul>Thank you.<br />Question<br />40<br />