How steep is your learning curve? On Analytics and Mentors ...


Having a mentor is the number one 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.

learning-curveTo 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 their disposal, but importantly, also a relatively steep learning curve, such that a plot of their capabilities has them 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 their current disposal:  time, talent, smarts, money, education, experience, etc …, but for whatever reason, has a shallower learning curve, such that their 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 his domestic and international experiences as an undergrad and grad student, as a researcher and an employee, and as part of two start-ups (so far), Garik’s conclusion is that, while there are several factors impacting the steepness of that learning curve, none is more important than that of having chosen good mentors.

Businesses can be said to have learning curves as well, and my discussion with my son got me to 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 example of this (albeit, fictional) 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 to not just tolerate failure but to encourage it within the context of the learning curve. And the analytic framework 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 in a BI environment congenial to both everybody having access to the same data, and having access to user-friendly analysis tools such as visual data discovery.

Aside from the converse of the above (i.e. information hoarding / 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 re-invented 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, versus Risk Management: Too often the knee-jerk response to risk is 100% risk 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 – 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 thirties.  Although to be honest I have to admit that, despite the positive impact it 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 much benefited 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 constantly challenging your assumptions and strategies.  Maybe mentorship is the number one factor affecting the steepness of the organizational learning curve as well – the one core competency and differentiator under your control.


About Author

Leo Sadovy

Marketing Director

Leo Sadovy currently manages the Analytics Thought Leadership Program at SAS, enabling SAS’ thought leaders in being a catalyst for conversation and in sharing a vision and opinions that matter via excellence in storytelling that address our clients’ business issues. Previously at SAS Leo handled marketing for Analytic Business Solutions such as performance management, manufacturing and supply chain. Before joining SAS, he spent seven years as Vice-President of Finance for a North American division of Fujitsu, managing a team focused on commercial operations, alliance partnerships, and strategic planning. Prior to Fujitsu, Leo was with Digital Equipment Corporation for eight years in financial management and sales. 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. He has a Masters in Analytics, an MBA in Finance, a Bachelor’s in Marketing, and is a SAS Certified Data Scientist and Certified AI and Machine Learning Professional. He and his wife Ellen live in North Carolina with their engineering graduate children, and among his unique life experiences he can count a singing performance at Carnegie Hall.

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