D3: Data-Driven Decision Making

As an Army infantry scout in the 1990s, I often had the opportunity to fire numerous types of weapons.  During Operation Desert Storm, I was a machine gunner.  In the jungles of Central America I carried a grenade launcher mounted to the bottom of my M-16 rifle.  I qualified on .45 and 9 mm pistols, and was armed with both an MP-5 and a 12-guage shotgun for counter-narcotics missions.  Each had its time and place, and no one weapon was suitable for all environments and situations.

So, it strikes me as odd when leaders charge headlong into operational solutions without first taking into account the specific needs of the “mission.”  What I mean is without first identifying the unique outcomes, contingencies, and personnel needed to achieve optimal output.  Instead, we blindly launch onto the corporate battlefield hoping beyond hope that what we need is a 9 mm handgun, instead of a bazooka.

One leader tried explaining it this way.  He stated rather matter-of-factly, “We don’t have the luxury of evaluating all the data before making a decision.  In our world, we need fast, decisive leadership.”

We can all respect the concept of decisive leadership, as long as it derives from sound experience and results in positive outcomes.  But decisiveness, in and of itself, is not necessarily a virtue.  My nephew is incredibly decisive.  He knows exactly what he wants and needs, and he won’t hesitate to make it happen.  But he’s six, and his decisiveness can sometimes be interpreted (accurately) as stubbornness.

So too, decisiveness without basis can actually worsen situations.  Even quick decision making should be data driven.  But the use of data is also fraught with peril that many a leader fails to recognize.

How often have you seen quantitative data built to support a particular idea or strategy?  We’ve all see it happen, and we’ve all heard the phrase, “statistics can be found to support any idea.”  So, what’s a leader to do?

  1. Form a hypothesis – First of all, this doesn’t have to take long.  But keep in mind that the key to a good empirical study (even a very brief one) is to DISPROVE your hypothesis.  Only if you can find no evidence to disprove it can you have confidence that what you believe is happening IS happening.  If you only look to prove what you already believe, you’ll accomplish it every time, but with little positive effect.
  2. Employ solid data analysis – Don’t accept that everyone has strong quantitative or statistical competence.  If you don’t personally, find someone who can interpret your data with a critical eye to issues of validity and reliability.  Make sure they know their stuff, and trust them to provide you with unbiased interpretations of results.
  3. Accept the results – Particularly if your hypothesis is found to be untrue, accept it and be willing to change your planned course of action.  Don’t fall prey to the temptation to simply bulldoze forward.  That’s your ego at work, not your intellect!
  4. Make decisions based on actual findings, not on preconceptions – So important is this last notion, that it makes the list twice (slightly rephrased).  Believe in the data.  Trust the expert analyst.  And make the best decisions possible.

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