The nearly poetic tweet by Marcia Conner appeared at the top of my feed with just the title of a linked article: When “Good Enough” is Good Enough. And it was good enough, compelling me to click and read. As I read, I thought about many applications of “good enough” in Learning and Development (L&D), particularly concepts such as minimum viable product, which is a, “strategy used for fast and quantitative market testing of a product or product feature,”1 or, “the product with the highest return on investment versus risk.”2
I also realized the concept of “good enough” had application across seemingly unrelated things like the 70:20:10 framework for workplace learning, the scientific process, and the Pareto Principle (80-20 rule). In fact, these are all related and applicable to L&D in many ways.
The Pareto Principle
Simply put, the Pareto Principle states that 80 percent of consequences stem from 20 percent of causes.3 In the case of learning and development, it might be seen as 80% of learning results from 20% of the total time spent on an activity, or that 80% of a product is completed with just 20% of the work. The converse then is that the remaining 20% of an effort or desired results takes most of the time to accomplish.
It stands to reason then that redefining “good enough” could positively affect on our work, particularly when faced with declining budgets and hectic work schedules. Too often we spend enormous amounts of time refining and honing a product until it’s as perfect as possible, only to be surprised at roll-out that we’d missed something we would have caught earlier had we only released it in an earlier state of completion. Perhaps a 90% solution is good enough at times, or even a 95 or 99% solution. While real life doesn’t fit a perfect 80-20 Pareto distribution, the savings could still be dramatic. Mathematically, a 95% solution would be completed twice as fast a 99% solution and a 90% solution seven times faster.
The Pareto Principle and the 70:20:10 Framework
The 70:20:10 framework suggests that only 10% of workplace learning should be (or is) formal and therefore substantially engaging the training department. 20% should be self-directed, social, and informal, leaving 70% for experiential learning in the workplace. Clark Quinn mapped the learning curve against this framework,4 charting employee’s performance against the totality of their learning over time. Like the Pareto curve, the learning curve follows a power law. Clark looked at formal learning, the 10% portion of learning, as the enabler of the remaining 90% – first the 20% that is self-directed, social, and informal, and then the 70% that is experiential and in the routine flow of work. Learning (training) is the cause, and knowledge or workplace performance is the consequence. This may be the case for an individual’s overall performance or capability in the workplace, so the formal education that led to landing the job is a significant but brief part of overall workplace performance.
In the context of the effort expended to train employees once they’re in the workplace, a different view emerges by charting overall employee learning against effort. Because it’s in the flow of work, experiential learning (the 70% of 70:20:10) is accomplished with the least effort on the part of training organizations. This is followed by the next 20% that is informal and social, resulting from a moderate but more prolonged effort. The formal learning component that originates primarily with the L&D department, which results in just 10% of workers’ learning, requires the most effort, as represented by the prolonged tail on the Pareto curve (shown in the accompanying figure).
Of course, this is an imperfect analogy, but it does serve to accentuate that the relatively large effort expended by L&D affects the smallest small fraction of overall learning in the workplace. It stands to reason that our efforts should focus on solutions that are faster to market and target more the 90% solution than the 99% version.
The Scientific Method applied to the work of L&D
People often confuse science and engineering, thinking of science as being characterized by precision. At times, scientists are indeed interested in measuring a particular physical constant to many decimal places, but more often than not science is about quick but informed experiments to gain insight and discover yet more things to explore. A great deal of science – even the scientific mentality itself – is about learning as much as possible without complexity. Scientists approach complex experiments with confidence, having made countless, less complex experiments along the way.
Learning and Development can take a cue from science by doing small, less complex experiments and learning from the results. We know how to help people learn in contrived (formal) settings, and we used to exclusively build face-to-face courses, which were relatively quick to produce and inherently flexible, more akin to experiments than engineered masterpieces. With e-learning, expectations to get things right before reaching users led to approaches more akin to engineered solutions at the 99+% level. With blended solutions, we tended more toward this engineered solution than more experimental approaches.
What to do?
Now is the ideal time for us to experiment with different approaches to workplace learning in much the same way our employees are learning on their own, whether it’s with MOOCs, social learning with media outside or inside organizational boundaries, or any of the countless ways to produce microlearning products. Our challenge however, is to be fearless and try something new, being willing to learn from the experiments no matter the result. In the end, perhaps, we will return to using flexible approaches to organizational learning and a “good enough” mentality. This is not the same as being complacent, lazy, or sloppy. It means intelligently choosing approaches for which “good enough” really is, and being able to better meet the needs of many without the restrictions of approaches that require the 99+% solution. This should mean a substantially improved ability to deliver the most effective blend of performance improvement solutions for all of our customers.
Thanks for reading!
- Bank, Chris. “Minimum Viable Products – Defined by The Experts.”Onextrapixel – Web Design and Development Online Magazine. N.p., 24 Oct. 2014. Web. 03 Mar. 2015. <http://www.onextrapixel.com/2014/10/13/minimum-viable-products-defined-by-the-experts/>.
- “Minimum Viable Product.” Wikipedia. Wikimedia Foundation, n.d. Web. 03 Mar. 2015. <http://en.wikipedia.org/wiki/Minimum_viable_product>.
- Bunkley, Nick. “Joseph Juran, 103, Pioneer in Quality Control, Dies.” The New York Times. The New York Times, 02 Mar. 2008. Web. 03 Mar. 2015. <http://www.nytimes.com/2008/03/03/business/03juran.html>.
- Quinn, Clark. “Learnlets Clark Quinn’s Learnings about Learning (The Official Quinnovation Blog).” Learnlets » 70:20:10 and the Learning Curve. N.p., 27 Jan. 2015. Web. 03 Mar. 2015. <http://blog.learnlets.com/?p=4179>.
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