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How Improv Classes Taught Me To Be Better At Analytics

ConcentricComplexity Science How Improv Classes Taught Me To Be Better At Analytics
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How Improv Classes Taught Me To Be Better At Analytics

When analytics is done in a silo, the breadth and depth of the analysis will be constrained to the experience and expertise within that silo.

 

We find that organizations get more effective answers when working collaboratively as part of a team. Some improvisation is required to define business questions more broadly and integrate a wider range of data and insights from across multiple disciplines.


This blog shares my personal experience and evolution from a “go-it-alone” statistical modeling mentality to a more agile, collaborative, and effective analytics approach.

 

“You should sign up for improv classes.”

 

I was pretty surprised when our CEO suggested this to me a couple of years ago.

 

I had joined Concentric as the Manager of Analytics, so I expected that any additional training I took on would be in machine learning, Bayesian statistics, or discrete choice analysis. Improv seemed about as far afield from what I should be learning as you could get.

 

My previous analytics experience had trained me in a world of monologs. I was given a prompt. I pulled together the data. I ran various analyses. I observed the trends. I drew some conclusions. I presented the results. I answered the questions. I was the expert. I had control. The analytics monolog is a solo act, after all.

 

Although this experience allowed me to bask in the glory of my acquired expertise, I found I was grossly unprepared for the challenges of analytics outside of the protection of a hierarchy. When I started attending sessions with a wide range of stakeholders, with problems that were not yet clearly synthesized, and with a plethora of potential ideas and data sources…I froze. I was paralyzed by the ambiguity. Things shifted too quickly for me. I was not nimble or agile enough to match the pace. The methods that I was most comfortable with were not sufficient to answer the questions being asked. I did not understand every facet of the disparate data sources. I could not do it alone.

 

And then I realized the problem: I was trying to do it alone. I needed to think of myself in the context of a team. The monologs could only take me so far and could provide precise but incomplete answers at best.

 

I needed to think of myself in the context of a team! Click To Tweet

 

The key tenet of improv is to take what your scene partners give you and to build upon it. You are never telling the story alone. Ultimately, the story will be far more compelling and entertaining when a group’s creative energy is employed. Improv was a key training exercise in teaching me to stop going it alone, and to integrate into a team. As the months of improv training passed, I stopped freezing as I became agiler. I was able to tackle more challenging problems than I ever could alone because I was part of a group. I began to work with a wider team to garner and synthesize insights. And ultimately, as a team, we were able to answer questions to some of the most challenging business problems.

 

So ask yourself, is your analytics team performing monologs or practicing improv?
Improvisational analytics is collaborative. It harnesses the collective talent, expertise, and insight of the team. It allows the conversation to keep moving fluidly towards an end. And ultimately, it allows an analytics team to be agile and effective in solving problems that are too challenging and comprehensive to tackle alone.

 

Is your analytics team performing monologs or practicing improv? Click To Tweet

 

We are firm believers in collaborative analytics at Concentric and strive to enable teams to improvise in the face of an ever-changing world.

John directs training on Concentric Market and provides guidance to our users on setting up market simulations, using various data sources, validating simulation models, and applying simulation to answer key questions. With a background in engineering and quantitative risk modeling, he provides analytics insight to our users through direct interactions with our customers and efforts to continuously improve Concentric Market algorithms.

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