20% Likelihood of Fatal Crash: Acceptable Risk?
Elizabeth Engel and I published a research paper last week
about evidence-based decision-making (see http://bit.ly/1jwXcDX).
The paper offers a variety of insights regarding the role of data, value of new
analytical tools, the importance of intuition in the decision-making process, etc.
Whenever I finish a project like this, however, I invariably
come away with a multitude of second thoughts and questions. Two stand out at
- What does it really mean to “practice” decision-making? Is
this, in fact, a technical skill like hitting a serve in tennis that can be
improved with a variety of mental exercises? Can technology help us build our
decision-making muscles and technique?
- How do we find the “sweet spot” between acceptable risk and
certainty? How much failure can we tolerate? (For those of you who saw the film
“Rush”, you may recall that one of the drivers would not race if he believed
the likelihood of a fatal crash exceeded 20%!) Are there valid
scenario-building tools that will help us accurately calculate the level of
risk associated with specific strategies?
Of course, it would seem good decision-making should always start
with asking the right question. The most common false positive I hear in the association
world arises from the question, “How do we get more members?” Should the better
question be “How do we get the right members?” The right answer to the former
may have little to do with the right answer to the latter.
I don’t have a one-size-fits-all answer to any of these
questions, but I suspect that we associations still underutilize both the data
we have on hand as well as the freely available large external data sets which can
be overlaid on our internal data to give us a more complete picture of our
members and their world. I would also suggest this represents a clear
opportunity for the association tech community to increase its strategic utility
and presence in the C-suite by introducing and activating the growing number of
new tech tools that allow us to more effectively collect, curate, mine, analyze
and share data.
Data for all and all for data!