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Leo Sadovy


How Steep Is Your Learning Curve?

On analytics and organizational mentors

Published: Wednesday, December 9, 2015 - 12:04

Having a mentor is the No. 1 factor in increasing the steepness of your personal learning curve. So says my oldest, Garik, a Park Scholar at North Carolina State University (class of 2012), during a discussion he recently had with the incoming Park Scholar class of 2019.

To accept the value of mentoring first requires one to understand the centrality and importance of the learning curve. Garik asked the students to imagine plotting the characteristics of two people on a simple X-Y axis. Person A comes to the game with only a moderate amount of resources at his disposal, but importantly, also a relatively steep learning curve, such that a plot of his capabilities has him crossing the Y-axis at an intercept of 1 and with a slope of one-half. Person B, in contrast, has much greater resources at her current disposal: time, talent, smarts, money, education, experience, but for whatever reason has a shallower learning curve, such that her plot on the graph intercepts higher up the Y-axis at 2 but with a shallower slope of only one-quarter.

Unless you think you’re going to die before the two lines cross, you’d of course be better off as Person A. Based on Garik’s domestic and international experiences as an undergrad and grad student, as a researcher and employee, and as part of two startups (so far), his conclusion is that, although there are several factors affecting the steepness of that learning curve, none is more important than having chosen good mentors.

Businesses can be said to have learning curves as well, and my discussion with my son got me thinking about what factors would have the greatest bearing on organizational learning curve steepness.
Machine learning: A method of data analysis that automates analytical model building, using algorithms that iteratively learn from data, machine learning allows computers to find hidden insights without being explicitly programmed where to look. My favorite (albeit fictional) example of this  is the scene with the mothership in Close Encounters of the Third Kind, where, after manually establishing the basics of the common tonal language on the keyboard, the scientist announces that the computers are “taking over this conversation, now.” When it comes to the algorithms underlying machine learning, success breeds success and a steeper learning curve.
Fast failure: This is one part culture, one part analytics. The culture is to not just tolerate failure but also encourage it within the context of the learning curve. And the analytic framework is to support rapid prototyping, model building and testing, and what-if and scenario planning.
Knowledge sharing: Data integration, breaking down and connecting the data silos, and then serving it up so that everybody has access to the same data and as well as user-friendly analysis tools such as visual data discovery.

Aside from the converse of the above (i.e., information hoarding and keeping employees in the dark), what specific factors might work in the reverse direction to inhibit and flatten the learning curve? A few that come to mind include:
NIH: “Not invented here” has been a bane to every organization since the first tribe reinvented the wheel. There is no better distinction between being a manager and being a leader than this: It takes a leader to overcome NIH syndrome. Not being open to new ideas and practices is a sure path to a perpetually flat learning curve.
Risk avoidance, vs. risk management: Too often, the knee-jerk response to risk is 100 percent avoidance at all costs, an impossible task, and a certain learning curve killer. Every aspect of your business is subject to varying levels of risk at all times—your job is not to entirely eliminate them, but to assess and quantify their magnitude and variability, and then to employ various risk management strategies as appropriate. This includes anything from insurance to outsourcing, from inventory policy to warning flags and alerts, from cyber threat detection to portfolio-level risk analysis.
Post mortems that neglect to focus on what was learned, but only on what went wrong and who’s to blame.
• Viewing the training budget as an expense to be minimized, and development and training as more of an afterthought or a tick-in-the-box, rather than a learning “contract” as part of performance management.

As for mentoring, I think it’s remarkable that 18-year-olds today are so open to the idea. I don’t believe the term, or my first true mentor, entered my life until my late 30s. Although to be honest, despite the positive impact mentoring would have had on my early college self, I was likely too much of a go-it-alone, do-it-all-myself person at that age to have benefited much from someone’s efforts on my behalf.

It’s not so much about lack of teamwork, but about being willing to have someone you trust with your best interests at heart to give it to you straight up and unadulterated. Come to think of it, organizational mentorship couldn’t hurt, either: External board members and consultants can constantly challenge your assumptions and strategies.

Maybe mentorship is the No. 1 factor affecting the steepness of the organizational learning curve as well—the one core competency and differentiator under your control.

First published Nov. 10, 2015, on the SAS blog.


About The Author

Leo Sadovy’s picture

Leo Sadovy

Leo Sadovy handles marketing for analytics and performance management at SAS. Before joining SAS, he was vice president of finance for business operations for Fujitsu, managing a team focused on commercial operations, customer and alliance partnerships, strategic planning, process management, and continuous improvement. Sadovy also developed and implemented the ROI model and processes used in all internal investment decisions. He started his management career in laser optics fabrication for Spectra-Physics and later moved into a finance position at the General Dynamics F-16 fighter plant in Fort Worth, Texas. Sadovy has an MBA in finance and a bachelor’s degree in marketing.


Need some help before judging the curve

It looks like I need some mentoring here. What does "anizational" mean in the context of learning curves or any other topic for that matter? Is it a short hand for organizational? If so, it leaves the work out since org is related to erg.



What, you never heard of anizational? Google it. Oops. No... wait. Don't do that. Sigh... we had to make at least one more mistake before the end of this year. Thanks for spotting that.