Slipping Down the Knowledge Funnel

Slipping Down the Knowledge Funnel


I recently read Roger Martin’s 2009 book The Design of Business. It’s a lovely book, and I recommend it highly for anyone interested in innovation, creativity, and/or understanding organizational structures.


Early in The Design of Business Martin introduced a conceptual schema to describe knowledge development. Think of all discoveries, Martin suggests, as starting with a mystery. Someone observes something about the world that he or she doesn’t understand. It could be a universal question that’s interwoven with the nature of the universe (as the mystery of why things fall to the ground is interwoven with natural laws), or it could be something much more localized and specific, such as how to address a specific market demand.


The mystery is the broad end of what Martin calls the “knowledge funnel.” At this point, no one really knows how to answer the question, or even how to phrase it productively. The second stage is the heuristic stage, where “an incomplete yet distinctly advanced understanding” of the former mystery has been articulated (p. 12). At this stage, you can act to solve the problem, but it takes a fair degree of expertise. Some mysteries, like the creation of art, are never systematized beyond the level of the heuristic, and always require expertise among practitioners.


Our understanding of many mysteries, however, can and is taken further. We reach the third stage of the knowledge funnel, that of the “algorithm.” At this stage, the mystery is completely understood. It is reduced to the level of step by step directions which anyone can follow and get the same results. This is a great boon to industry, and to society. It took a master craftsman to make most things in a pre-industrial age, but workers on an  assembly line can create far more advanced and dependable products while bringing a much lower level of craft to the job.


Martin does not discuss educational systems in this book. (He is dean of the Rotman School of Management at the University of Toronto, but his focus is on the corporate world.) However, when I read this book, I couldn’t help but apply this model to the situation of adjunct faculty. During our training, we are grounded in the heuristics of our disciplines. Our dissertations are demonstrations of our mastery of those heuristics, though there’s always the hope that we’ll rise to the level of the mystery, and we both study and venerate those who do. Tenure is awarded for successful application of a disciplinary heuristic; academic status is won through mastering mysteries.


And…adjunct labor focuses almost entirely on the level of the algorithm. Class structures and materials are often mandated by the institution, while individual adjuncts often repeat the same course materials time and again in a kind of self-defense, because they simply have no time to otherwise. We may rise to the level of the heuristic when teaching, as it’s not possible to totally routinize human interaction, but we don’t reach the level of mystery.


Martin argues that individual organizations who come to depend too highly on algorithms will eventually be left behind: their focus on exploiting knowledge while leave them vulnerable to those who create it in new areas. I would add that this can apply to an entire field, namely higher education. It will be bypassed if it defaults on knowledge creation, which it does through cutting adjuncts out of the mystery loop.


I would add a second point, one implied but not stated in Martin’s book: delving into the mystery rejuvenates. It is why the Picassos of the world stay young even as their bodies age. In cutting adjuncts out of the mystery loop, and relegating them to the realm of the algorithm, higher education is cutting us out of the spiritual fountain of youth.





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