Metrics Need Context

I made a mistake the other day.  As I was leaving for work, I checked the weather report to see how warmly I needed to dress.  The forecast said 40 Fahrenheit.  So, my brain went through the following fairly logical steps:

  1. On the Fahrenheit scale, freezing occurs at 32 degrees.
  2. Today’s temperature is only 8 measly degrees above freezing.
  3. Therefore, it is practically freezing and I should dress warmly to avoid practically freezing myself.

So I put on my winter coat and walked out the door.  Moments later, it was clear that I had misunderstood the data.  I saw people walking in light jackets and, in a couple of slightly crazy cases, in shirtsleeves.  Where did I go wrong?  While 40 is fairly close to freezing, in New York City in January it can feel balmy — especially if it comes on the heels of a cold snap.  If you doubt this, think about how you  dress in the autumn in New York City as the temperature is plummeting towards winter.  Warmly, right?  To be specific, would you wear a sweater if it were 50 degrees Fahrenheit in September?  Yes, most probably.  Now think about a 50 degree day in March.  In New York City, you’re likely to see folks wearing shorts and T-shirts.

What is critical to this analysis is knowing that we’re talking about New York City rather than Miami AND we’re talking about specific times of year.  Both elements of context have a huge impact on how we interpret the bald data of temperature.  It is no different when thinking about the metrics you’ve so carefully collected (I hope!) to help understand the efficacy of your Enterprise 2.0 or knowledge management project.   Knowing that activity levels have risen may be interesting, but knowing that happened against a backdrop of falling business levels makes for interesting analysis.  What’s going on?  Why?  The metrics by themselves don’t tell the complete story.  They need faithful, honest interpreters who can place them in their correct context and draw appropriate conclusions.  We need to be those faithful, honest interpreters.

By the way, it’s snowing heavily in New York City as I write. I’ll be dressing warmly.

[Photo Credit: Qiao-Da-Ye]

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