As covered previously in my article about observations, innovation is an investment of time, money and everyone's energy and therefore isn't worth doing unless it produces measurable results. The operative word there is measurable. The ways in which our world and the way we experience it are rapidly evolving ensures that innovation, and experimentation as a means to it, are unavoidable. But that kind of risk-taking without due diligence is costly—just ask any novice poker player. That being the case, making sure we are prudent about those experiments is the key to ensuring we are taking intelligent risks and investing of our time and of ourselves wisely.
So how then do we exercise due prudence in the process of experimentation and innovation? The secret to that is through measurement. By measuring the known variables in a given experiment and knowing how those variables interact, we can derive a great deal of meaning from the changes in their relationship to one another.
"The grandest discoveries in science have been but the rewards of accurate measurement and patient long-continued labor in the minute sifting of numerical results."—Lord Kelvin
Now I know half of you are rolling your eyes, saying "Obviously!" but while it seems that everyone knows measurement is important when testing our ability to affect different variables, when the time comes to do it everyone seems to have a litany of perfectly good excuses as to why this is a time when they can just skip over that part. The truth is you can't.
We never want to take any measurement as a given unless we have credible evidence to stand in place of our own measurement. If I'm testing the thermal impact of the sun on a face of a building, that doesn't mean I should measure every aspect of the sun. I'm saying use quality measurements from credible sources to stand in their place. A report from NASA counts. What doesn't count is, "Well, see, I've been doing this for years and I know that..." No. Just no.
Our ability to make the objective subjective and insert our own bias far outpaces the human knack for discovery. You'll never overcome it. That's why it's extraordinarily important that we do everything we can to guard against it. When preparing to run an experiment in the pursuit of innovation, anecdotal evidence almost never has a place.
The McNamara Fallacy
That being said, we don't want to rely too heavily on data alone. That's what's referred to as the McNamara Fallacy, named for Robert McNamara, Secretary of Defense during the Vietnam War, and his over-reliance on the quantification of success, which he believed ultimately led to the US defeat.
The important thing is to allow the data to inform our conclusions. Not be the conclusions themselves. Life is too complex not to create a deeper understanding of your data within context.
" My training: 'If you can't measure it doesn't exist.'
My findings: 'If it's measurable, it's probably not too important.'" – Brene Brown
Innovation is always going to be a high-stakes gamble, and oftentimes the only return we are going to see is going to be the meaning we derive from our experiments. Any type of meaningful innovation starts with observation, but it's through measurement that we uncover its significance. Measurement of our observations both before and after experimentation teach us the lessons our experiments are meant to unlock, adding rigor to what otherwise is little more than guesswork. But that small amount of rigor, quickly equates to big gains in precious time and budget saved as well as much frustration avoided.
