Biomedical Engineering Reference
In-Depth Information
no chance to be implemented and so, however intrinsically brilliant, have no
value: a harsh pragmatic reality. Such teams usually begin with a high-medium-
low binning before any ideas are excluded; Figure 6.4 shows the results with
high
0 points, though the weighting factors made
no qualitative difference to the results.
This is a very interesting result, supporting neither above hypothesis but
rather suggesting that idea value is independent of whether the author is pro-
lifi c or occasional. In other words, if 1 in 100 ideas is valuable (for example),
then we might expect one valuable idea from the three people who put in 50,
30, and 20 each and also might expect one valuable idea from the 100 people
who only put in one each. Note how accurately this parallels the value proposi-
tion in iTunes or Amazon's Kindle bookstore, where songs cost about 99
cents and books cost $10, roughly constant from best sellers to the most
obscure titles.
Now we can return to the overlay of Figure 6.1, the cumulative area under
the log-log graph. This represent not only the cumulative number of entries
but also the cumulative value. About half the value is contributed by authors
1-300, and the other half by authors 301-4000. If your reaction is to think “I
want those top 300!” you are missing the opportunity of large-scale collabora-
tion in three important ways. First, exactly which 300 you need is going to
change for every given business problem. Innovation must be specifi c and
purposeful; calls for “we only want big game-changing ideas” are guaranteed
to fail [4], and so successful campaigns are quite content specifi c and useful
contributions draw on deep personal expertise and experience. Second, tradi-
tional teams become dysfunctional beyond a dozen or so participants [13].
Even scheduling meetings for a team of 20 becomes infeasible. If you fall back
to the idea of “top 10” for a team, Figure 6.1 tells us that you will knowingly
miss 90% of the value you could have had. If your organization does not have
4000 people in it, that is still true, they just do not all work for you. Third, and
optimistically, recall the lessons of Chris Anderson: Do not run from the long
tail, exploit it. Systems designed to facilitate, run campaigns, and manage
evaluation and next steps are readily available [2], and the cost is not the
system but the opportunity lost by ignoring the value in the tail (which you
now know how to predict).
In fact, the tail value is greater for individual campaigns than Figure 6.1
suggests. The only exceptions to power law behavior seen to date are from
nonvoluntary campaigns. The electronic tools for mass collaboration work
equally well in a “command” situation; for example, it is very productive to
have a half-day meeting with several background presentations followed by a
“ fl ash event” in which every member of the audience is exhorted to spend the
next 15 minutes writing down four ideas for how their work team could
support the presented proposal [14]. In these cases the result is not a power
law distribution [9] but closer to a Gaussian; on a rank-frequency plot the tail
drops quickly. The Pfi zer data are an aggregate of many campaigns including
large involuntary ones of this type, which probably accounts for the deviation
=
10, medium
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4, and low
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