I love playing sports and being in the outdoors, but I don’t have what you might call an active lifestyle. I go through spurts where my daily physical activity amounts to walking from my bed to my desk. I was mired in one of those phases a few months ago, when my younger sister, concerned about my sedentary ways, suggested that I enroll in a fitness bootcamp.
I was reluctant (it required waking up at 5:30am) and skeptical (5:30am? Really?!), but somehow, she convinced me. So for six weeks, I found myself rolling out of bed at a god-forsaken hour (did I mention I had to wake up at 5:30am?) and driving to Golden Gate Park, where I spent an hour running and doing calisthenics.
Surprisingly, I enjoyed it. It felt good to get back into shape, to be in the park in good weather when no one else was around, and to finish a workout with the whole day still ahead.
We did an assessment at the beginning and at the end of the six weeks to track our progress. I was a bit frustrated by the final assessment, because I didn’t complete it as easily as I thought I would. But when I looked at the numbers, I saw that I had made significant, tangible improvements. The trainer said, “People always feel like they didn’t make progress, but when they check the numbers, they see that they always do.”
I thought that this was a wonderful metaphor on the importance of measuring impact. If you try to go entirely by gut feel, you won’t get a real sense of whether or not you’re making improvement. I sometimes feel dissatisfied with the pace of our learning here at Groupaya, but when I check our benchmarks, I usually find that my impatience has masked the reality.
A few weeks ago, I had a great conversation with Hawaii Community Foundation’s Christine van Bergeijk about impact, and I related this story to her. Chris, no stranger to sports or to measuring impact, retorted, “Yes, but it’s also important to assess how you feel. If you’re hitting your numbers, but you feel terrible, that’s not good either.”
I found her point to be extremely important. You need to look at your data, but you also need to remember what your data is supposed to represent and to assess whether it’s actually doing that. Doing one without the other is only slightly better than doing neither.