Knowledge Cycle

Team collaboration and social networking software are all the buzz right now, however we need to look at the overall contribution these technologies bring to the enterprise in terms of value before we determine if they should be the new “cool” technology.  Are IT programmers ready to answer the CIO’s question of “How will team collaboration software add value to our business?”?  Maybe?  Maybe not?  What I hope to describe is how you can answer that question and what to look for in order to get your company started using collaboration software to solve real business problems.

Which leads us to our first real question we need to answer and that is – how does collaboration add value to the bottom line of a business?  The short answer is that it doesn’t, not in and of itself.  Collaboration in the right context and under the right conditions can produce spectacular results in the form of quick customer response times, better organizational knowledge, or faster time to market.  Each of these business metrics can be enhanced through the use of collaboration tools, if done correctly, and harmed if done incorrectly.  Imagine this scenario: You thought you addressed your multi-national organization’s collaboration infrastructure needs, but it’s not looking so good. Your IT department provides many popular tools to assist your workers, such as wikis, blogs, threaded discussions, email, file shares, and more. But users are burdened with having to learn multiple applications with different user interfaces, data is often isolated in one application, users report that information is hard to find and sometimes gets lost, and none of these applications reflect your core business processes (instead, you often bend your processes to cater to the way these tools work).  This is the scenario your CIO fears most!

To begin the process of understanding how to implement collaboration software successfully in your organization, we are going to start with a high level view of how knowledge gets created in an enterprise and how collaboration tools can aid in the understanding and categorization of knowledge.

Knowledge Cycle

The diagram above represents the process information flows through as we consume and understand it.  There are 3 main phases that aid in the understanding of content and information, the first being the Publish phase.  During this phase tacit knowledge is converted into something that is explicit and consumable by others.  This phase represents the transfer of one person’s understanding, education, and wisdom into a tangible good.  The publication format can be nearly anything – a blog, wiki, document, anything that is consumable by another person.

The next phase of the cycle is the “Discover” phase.  During this period the output of the publication event is found by another person.  This can be done via search, browsing, or by someone sharing the document with you.  This is a particularly important phase because without discovery the publication event will go unused and is useless.  This area represents a significant area where technological advancement, not only in search technology but also in personal relevance, can be applied and utilized.  Services like Twitter, Digg, and TechCrunch are using community and network recommendations to provide useful metadata about discovered publications.  These services are providing a valuable filtering mechanism to help you maximize the quality of publications you consume.  RSS and Atom feeds also allow you to stay focused on publication events that occur in areas you are interested in.  The confluence of search technology, community reviews, and feeds are making the Discover phase of the Knowledge Cycle easier than ever and is providing valuable insight into what items are relevant to you.

Another interesting component of the Discover phase that has yet to be exploited is network relevance and its implications to you.  Some services such as Twitter and LinkedIn allow you to monitor a “network” for events.  LinkedIn for instance provides information about changes in your social graph as people are added and relationships forged.  This graph is a highly relevant graph to you.  It maps your closest work and personal relationships and also allows you to get a glimpse of the internetworking among your peers.  Why is this important?  Because this graph represents your preferences and social circles.  I would argue that this information is used less than it should be to help determine the publication events that we consume.  If techniques existed that would allow us to mine our social graph for not only relationships but also consumed publications and the quality of those publications, this information would be extremely helpful in shaping our information consumption.  This area of the Knowledge Cycle, I feel, will yield great results in the future in helping us discover and apply the correct information to our problem domain.

The next area of the cycle is the Discuss phase.  During this phase information has been found and consumed but may not be fully understood by the consumer.  This phase is a critical component of the Knowledge Cycle because it promotes and encourages questioning and full understanding of the material.  Many tools can be used in this phase:  phone, IM, email, forums, etc.  The requisite for the tool is that a Q&A can take place to facilitate understanding between parties.  To better disseminate information and to make it more accessible to others in the future public exchanges should be used when possible.  Communication channels like phone, IM, and email are great mechanisms that enable questioning and understanding but also limit the consumption of this “give and take” process to a very few parties.  This  sometimes is sought after behavior but in many cases the Knowledge Cycle would be more efficient if the discussions where held in public and fully searchable and cataloged with contextual elements.

“Personalize” is the phase of the cycle where the information you discovered and read is now fully understood my you, the consumer of the content.  At this point in the process you can fully appreciate the implications of the content and are beginning to understand how this new information can be applied to your own scenarios.  The collaboration events in this phase focus on your adding value to the initial publication event so others in your network can more quickly find and understand the same information.  This involves adding public tags, reviewing the content, adding Digg style ratings to the publication, or annotating the publication so that others can better understand its meaning from your perspective.  This phase adds a new dimension of information to the original content that is extremely useful especially if the contextual usage of the information is maintained either via your network or some other means.  This means that the context in which you are trying to apply the knowledge from the original publication is just as important and relevant as the value added components you associate with the document.  This context allows your network or others in similar environments to have a relevant perspective on the original information which can greatly reduce the understanding lifecycle.

The last phase of the Knowledge Cycle is the Extend phase.  In this phase we are applying the knowledge, wisdom, and education that was distilled in the original document into scenarios that we own and are a part of.  In this way knowledge has been transfered to us from someone else and used in, perhaps, totally new ways.  The context of the original information may only tangentially apply to these new scenarios, if at all.  A great example of the extension of knowledge into new areas was the seminal work by Christopher Alexander “A Pattern Language:  Towns, Buildings, Construction”.  In this book Alexander makes the case for developing construction practices based on the fundamental notion that harmonious patterns have existed in architecture for centuries and that these patterns should be reused if they add value and harmony.  This work was later applied to computer science by several researchers, most notably Erich Gamma, Richard Helm, Ralph Johnson, and John Vlissides and a new field in computer science was formed with the same basic tenets applied to computer science as Alexander had originally written about.  This example clearly demonstrates the Extend principal (although on the extreme perhaps) that allows knowledge designed for one audience and context to be discovered, discussed, and transformed into another entirely different area of study.

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