Let’s dive right in with a spoiler.  In a few paragraphs, I am going to suggest that you “make your performance management process less efficient to make it more effective.”  Hopefully you’ll read that far.  Hopefully by the time you get there, I’ll have convinced you to at least entertain the idea.

As a big fan of metrics, I am deeply aware of how easy it is to fall into the Point in Time trap of performance management.  Whether talking about an individual, a product, or an organization, assessing performance and progress towards a goal based on a narrow window of assessment can encourage shortsighted thinking that misses trends.  You can even end up rewarding undesirable behaviors.  

Of course, this perspective is hardly new or novel.  We’ve all seen the graphic that shows the tragically plunging line on a graph that turns out to be just a tiny dip in a broadly very positive trend.  So if this particular fallacy is well documented and often warned against, why does performance management continue to be so fraught?  And, perhaps more importantly, what can we do about it?

When it comes to the underlying issues, I’ll limit myself to my two favorites.  The first is simply that we are human.  As Matthew Cahill of the Percipio Company says, “If you have a brain, you have bias” and, in this context, recency bias is a particularly insidious culprit.  The second is that American working culture has collectively agreed to sacrifice the rich potential of performance measurements on the altar of efficiency.

You may be skeptical about that second claim but let me tell you a story about watching football with my husband.  I noticed that the teams had roughly comparable win/loss records but the score was very uneven.  When I expressed surprise that two “evenly matched” teams were playing in such a blowout, he laughed at me! 

Apparently, the win/loss record is insufficient information for a true fan to even guess how a matchup will go, let alone the rest of the season.  Winning the championship is a clear goal for any professional sports team but no matter how hard I pressed him, my husband insisted that there really wasn’t any single metric that I could lean on to tell me how “good” a particular team’s chances were of achieving that goal.

Yet in business, we pick “God metrics” all the time!  We zero in on task completion rates for our employees.  We fixate on new users for our products.  We treat each quarter of earnings like a championship of its own.  Basically, we are willing to predict the future of our employees, products, and companies based on fewer dimensions of information than a semi-engaged sports fan tracks for their favorite team.

But is change really necessary?  And, if so, what could we actually do differently?  As article thus far has likely made clear, I absolutely do think that we should be doing something.  The “obvious” answer is to collect more data and do more powerful analysis before making decisions.  Fortunately, more powerful data collection and processing mechanisms are already driving some changes in performance management.

That is all well and good for large companies with lots of data and capacity for processing it.  Unfortunately, a solopreneur like me, or even a mid-level manager with a small team in a larger organization, is unlikely to benefit directly from that particular remedy.  So for the rest of us, I suggest a counterintuitive approach: Make your performance management process more complex and intentionally less efficient.

To be clear, I am *not* suggesting that you create a rigid structure of complex analysis that you run whatever data you do have through.  What I *am* suggesting is that you leverage the power of process to force yourself to look at the big picture and truly consider your options before the actual analysis begins.  For example, you could require that each time you sit down to do a performance assessment, you have to identify 5 metrics could theoretically have been used for the assessment but were deemed irrelevant.

Such a process could go a long way towards breaking you out of the Intuitive realm and force you into Deliberate consideration.  Yes, I am suggesting that even data analysis can slip into intuitive patterns, particularly when it comes to defining the data that will be analyzed.  We generally put a lot of time and energy into defining our key metrics up front but then we forget to ask ourselves if they still the best metrics we could be using today.  Sometimes new information emerges that might make our analysis more powerful.  If we don’t intentionally ask the question, we won’t ever know the answer.

This article continues my theme of counterintuitive advice for someone whose work centers around creating efficiency but I believe that this particular point comes back to going slow to go fast.  Adding a bit more time and energy into your performance evaluation processes will drive better metrics and a more holistic assessment.  This will, in turn, create clarity and ultimately generate useful, immediate definitions of performance that unify effort and achieve results faster.