In my career, and often at home, I use data to answer key questions: At work: “how much should we charge…how should we allocate resources…what is the right incentive compensation plan…how do we measure the financial impact?” are some examples.
At home: “should I buy or lease…should I clean my own gutters…should we get a season ski pass”?
These questions are pretty straight forward, and I know that for many of you, upon reading these questions, you immediately see the Excel formula or the simple algebraic equation in front of your eyes. After all, these questions represent clear problem statements with an implicit definition of how the outcome can be evaluated to make a decision.
I have noticed that the simplicity referenced above can turn to chaos when decisions, which may be more complicated than the above but not by much, are opened up to teams. Recently, I have been part of multi-day meetings with tomes of PowerPoint slides, many of which have cut-and-pasted squint-print Excel tables within, with multiple attendees squished into a stuffy conference rooms, where the inertia becomes palpable.
More often than not, these meetings, which are typically called to present, review, and make a decision, end (at best) in a decision which is made in arbitrary manner because time has run out , or (at worst) in a decision to have another meeting.
Why do teams have so much trouble making decisions or reaching conclusions? My hypothesis is that somewhere in the process, the team fails at defining and agreeing on the problem statement. Instead, it glosses over this part and embarks on a mad hunt for more data, without a clear understanding of the specific data needed to inform a decision.
What I have observed is that instead of spending more time on the problem statement (such as “x is not scalable), and instead of agreeing on the conditions for success (Y will generate Z in three years), many folks enjoy the sport of pursuing every piece of data imaginable with some misguided hope that by analyzing reams of data, a brilliant strategy will emerge.
I don’t think it works this way, regardless of how good teams are at gathering data, or how data centric an organization is. Most data is not actionable, and the hope or expectation that the right strategy or decision will emerge simply from the collection of vast data sets is unlikely to be productive.
The analogy I will use here is chocolate. Let’s say you like chocolate. A certain type of chocolate. Let’s say it’s this local Seattle chocolate (which is fantastic by they way, and they do tours of their factory in Fremont).
Well, you can spend some time focusing on the kind of chocolate you want, then figure out where to buy it, then go out and get yourself some.
Or, you can go trick or treating. You will carry your platstic pumpkin around, picking up all sorts of stuff. Reese’s Pieces, Laffy Taffy, etc. You will spend a lot of time doing this, and get a whole bucket full of crap. You may even weigh that plastic pumpking in your hands and say “now this was a successful Halloween!” However, the probability that you ended up with the chocolate you wanted is pretty low.
When I hear more than three data points tossed out in a meeting, or – even worse – hear that we are embarking on another trick or treating excercise to get more data – I try to ask:
- What is the problem statement or statements?
-How are we going to make a decision (what are the constraints or conditions for success)?
-Who is making this decision?
I think there are some folks – or teams – who have become professional trick-or-treaters of data. I’d rather stay home, maybe pour myself a glass of wine, and enjoy my chocolate.
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