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Collaborative Knowledge Networks Diagnostic

The Collaborative Knowledge Networks (CKN) diagnostic project represents a collaboration between the Center for Digital Strategies and the MIT Sloan Center for Coordination Science.

CKN Presentation
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CKN Sponsors
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SWARM CREATIVITY by Peter Gloor Center research fellow Peter Gloor's Swarm Creativity

The mission of the CKN diagnostic project is to discover and understand the novel ways people join and work together in self-organizing groups that we call "Collaborative Knowledge Networks" (CKN) and what these CKNs contribute to enterprises or value chains. Collaborative Knowledge Networks are groups of self-motivated individuals from various parts of an organization or from multiple organizations, empowered by the internet, who work together, driven by a common vision and goals.

This project works to understand and realize the potential of CKNs within and across organizations by:

  1. Analyzing electronic interaction logs such as email to find CKNs within organizations;
  2. Identifying structural properties and parameters of successful CKNs;
  3. Finding the people that make a CKN successful by identifying the role profiles crucial for the success of CKNs;
  4. Defining a metric to measure the success of CKNs;
  5. Developing a framework and set of organizational guidelines that help nurture and foster CKNs within and across organizations and make them more useful;
  6. Creating an Internet portal that stimulates collaboration of individuals in CKNs.

The project delivers benefits in 4 areas:

  1. By locating CKNs, organizations learn about the innovations, which are underway. This enables them to better spot hidden business opportunities, also cutting the time to market for new inventions.
  2. By supporting hidden CKNs, organizations become more efficient in working together - they can better identify their knowledge sources, streamline communication processes, and locate inefficiencies in the organization. They also get the opportunity to re-engineer business processes based on transparent communication flow.
  3. Because key contributors can be identified through transparent CKNs, companies have a better chance to adequately reward key collaborators.
  4. By making the communication flow transparent, "brick and mortar" and Internet security risks alike are exposed and security will be improved. Additional trust is created by establishing a more transparent working environment.

"TeCFlow - A Temporal Communication Flow Visualizer for Social Networks Analysis," Peter A. Gloor & Yan Shao
"Temporal Visualization and Analysis of Social Networks," Peter A. Gloor, Rob Laubacher, Yan Shao & Scott Dynes
"Visualization of Interaction Patterns in Collaborative Knowledge Networks for Medical Applications," Peter A. Gloor, Rob Laubacher, et al