The following is an excerpt from Leadership Laboratory. A private group of 100 leaders from around the world who are building their leadership capacity and developing better teams & organizations. Curious? Head to HoldTheStandard.com to learn more now!

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Defining the Appropriate Observable Window of Performance

Want to ruin a good thing? Check on it too much. When it comes to development and improving results there’s an art to the frequency in which you tune in to the data.

If you check too infrequently and you could miss opportunities to make adjustments faster. Check too often and you might be misreading dips in performance on the way to a great long term trajectory. 

Making adjustments requires getting a good read. 

Getting a “good read” on your performance requires getting the right amount of believable insight to make an educated decision. But, we’re in the information age, remember? We’re bathing in data. The scale in your bathroom, for example, is the perfect case study for how you could make a massive data frequency error. 

Consider an experiment:

We clone a young man for a weight-loss trial. Both have the exact DNA, meals, training regimen, and lifestyle factors. SPOILER ALERT: they both will lose exactly 25 pounds on this protocol in six months time. 

Clone A 

Clone A checks the scale three times across a 12-week trial (one in the beginning, once in the middle, and once at the end). His experience of the data might look something like this:

Weight-Loss Check #1: 200lbs – “Wow! I didn’t know I weighed that much!”

Weight-Loss Check #2: 192lbs – “Wow! It’s working! I love this plan!”

Weight-Loss Check #3: 184lbs – “YAY! I’m so proud! I bet I can lose 25lbs by the six month mark if I keep it up!”

Clone B

Clone B checks the scale every two hours across the same 12-week trial. His experience of the data might look something like this:

Weight-Loss Check #1: 200lbs – “Wow! I didn’t know I weighed that much!”

Weight-Loss Check #2: 200lbs – “That’s fine. We just started.”

Weight-Loss Check #3: 201lbs – “WTF! Does coffee weigh that much?”

….

Weight-Loss Check #21: 200lbs – “Is this ever going to move?”

…

Weight-Loss Check #62: 198lbs – “Here we go!:

…

Weight-Loss Check #21: 202lbs – “UGH – I’m not working this hard to get fatter!” 

…

Weight-Loss Check #78: 194lbs – “That’s something I guess..”

What a rollercoaster of unnecessary negativity!

Mathematician and philosopher, Nassim Taleb, made the same point when discussing the tendency for humans to suffer from narrative fallacies (unhelpful story telling to make sense of our complex reality) in the same way with regards to picking a winning investment. 

If you invested in Apple in 2008 and checked the share price once in 2008 and once today, you’d have one feedback loop experience of the data: WE CRUSHED IT! 

APPL: Dec 2008 – $3.26

APPL: Dec 2025 – $272.05

If you checked the share price every two years, you’d have the following experience: 

APPL: Dec 2008 – $3.26

APPL: Dec 2010 – $13.66

APPL: Dec 2012 – $21.20

APPL: Dec 2014 – $29.70

APPL: Dec 2016 – $27.59

APPL: Dec 2018 – $46.12

APPL: Dec 2020 – $121.01

APPL: Dec 2022 – $148.21

APPL: Dec 2024 – $237.27

This experience of the data is mostly smooth sailing to a bonafide winner, except for a moment of concern with the signal received in 2016. 

Now, I’m sure you get where I’m going with this, but if you check the APPL stock value every five minutes on the five minutes since 2008, you’d have exactly 50% negative news and 50% positive news.

Making adjustments for peak performance requires the right interval for interventions. 

If you’re reading this, you likely aren’t looking for stock tips and probably aren’t here to learn how to lose weight. You’re a leader and leaders are responsible for moving their group closer to its peak potential. 

Intuitively, you know you likely shouldn’t do a performance review every day at lunch with your subordinates, but what is the right interval for intervention? 

Think of it this way. In the weight-loss example, we’ve got to let the program do its thing, so checking bodyweight at an interval that is more frequent than the biological processes of weight-loss is just wasteful and misguided. 

Similarly, APPL as a company isn’t turning in reporting every five minutes for the stock price to reflect a meaningful data point to the health of the company. 

Similarly, you’ll need to answer the following question: “what is the length of time that makes for a quality window of observation for change?” In a developmental culture, we rely on experiments to drive adaptation and find objective data for which to give feedback on. 

Some intervals for improvement are short, like when you offer technical feedback on how to start a customer service email. Other intervals are long, like when you offer adaptive feedback for someone to address their blind spots around issues with authority. 

An appropriate observable window of performance is relative to the type of change you’re seeking. 

So, when observing the performance of a sales person, you’ll need to observe a true sales cycle to have data that a decision can be made upon. When observing skill development in a craft based job like a barista creating latte art or a sport coach navigating high pressure 4th quarter defensive coaching skills, your interval for feedback should be more gracious. 

It’s time to reflect on your observable windows of performance and ultimately your feedback cadence. Where are you too quick? Too slow? 

TAKE ACTION:

1. AUDIT. Declare key windows of observation for performance to put in the “Hot Seat.” Brainstorm the effects of changing these windows of observations both for longer and shorter.
2.  PRACTICE. Adjust any window of observation to better allow adaptation to occur or to tighten up the time it takes to make technical adjustments.