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Adjusting the Expert Filter

DIKW ProgressionI recently read an article on KM World that questioned our overall progress with knowledge. There seems to be a lot of information out there. We are inundated with it. Yet, amid all the noise and distraction, how can we trust the information that we encounter? In this article (http://www.kmworld.com/Articles/Column/David-Weinberger/Progress-and-knowledge-87238.aspx), author David Weinberger made an excellent observation: “To become knowledge, something had to be filtered by experts.” The way that we involve experts becomes increasingly important as we refine our methodologies to promote information to knowledge.  As the diagram illustrates, there is a progression from data to information to knowledge to wisdom.

In reading the article, it occurred to me that the proliferation of content from a variety of sources means that the audience accepts a certain level of risk, depending on the source of information. David asserts that our collaboration and editing processes must change to accommodate the new waves of information that constantly crash around us. Frequency of tweets, for example, indicates interest in a topic, but not necessarily expertise. In order for a knowledge source to be trusted, an inherent practice of content validation must exist. In my technical communication training, the approach was to build several iterations of expert review into the document development cycle. The desired end result was a deliverable that was already vetted and validated – content that the user could completely and immediately trust because it had already been fully validated prior to publishing.  To me, the traditional methodology was to publish knowledge that had been fully filtered by experts. Any wisdom gained would hopefully feed into the data phase for the next deliverable. Hopefully. Because all too often, once a deliverable was published, knowledge workers were on to the next initiative. Wisdom was not always harvested and incorporated into the next data phase.

However, the new methodology moves some of those iterative revisions to the post-publishing phase. These days we are more inclined to post information and revise it multiple times, based on audience or user feedback. The expert filter still exists, but instead of making assumptions about what the user wants to know to deliver a masterpiece in the first edition, knowledge workers are learning to apply a more iterative approach on the back end. As a result, initial content may be sparse. There may be a few basic procedures and concepts introduced. Then, as the user community provides feedback, the experts are consulted and the deliverable is revised. It becomes a living document. The ongoing updates and conversations around content keep audiences engaged and reassure them that the most up-to-the-minute knowledge is being presented in the document they are referencing. This process is also beneficial for the expert because they can learn more from this direct user feedback and immediately apply it to the updated document as well as future projects. Everything becomes more comprehensive and collaborative. I believe this model also suggests a strategic partnership between those providing the knowledge and those applying the knowledge. It becomes a reciprocal relationship that benefits everyone involved and accelerates the pace of learning.

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