In this YouTube monologue, Bill Canady recounts Edward Lorenz’s 1972 discovery of chaos theory and the butterfly effect, illustrating how tiny variations in initial data can produce dramatically different outcomes. Lorenz’s weather‑prediction algorithm initially rounded calculations to the thousandth decimal place, yielding inaccurate forecasts, but increasing precision to the hundred‑thousandth decimal place dramatically altered the results, revealing the sensitive dependence on initial conditions. Canady draws a parallel to business planning, arguing that early data—especially the first 100 days of a turnaround—are often too coarse to provide reliable guidance. He proposes an iterative 4‑phase cycle—segment, simplify, zero up, grow—repeated over the typical 3‑5‑year business plan horizon. Each iteration refines focus on the critical few, sharpens predictions, and improves adjustments, moving the organization progressively closer to optimal performance, though never achieving perfect certainty. The video concludes with a call to action to subscribe.
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The butterfly effect means your business plan will never be perfect. Deal with it. Edward Lorenz was an MIT professor and a meteorologist who in 1972 gave a presentation at a meeting of the American Association for the Advancement of Science he called Predictability. Does the flap of butterfly wings in Brazil set off a tornado in Texas? In the days long before the advent of AI, Lorenz was developing a digital algorithm to analyze the effect of atmospheric phenomenon on weather so that could better predict weather conditions in any given place at any given time. He tested the first iteration of this program by applying it to the historical weather data so that he could evaluate the accuracy of the predictions the program produced, comparing predictions to known outcomes. His software predicted sunshine in certain locations on a certain day. Great, he thought. And what had actually happened in that place and on that day? It was rain. Lots and lots of rain. As a scientist, Lorenz understood that failure teaches us as much as success. He dug back through his work to search for a wrong term, but as far as he could make out, the software had done everything right. Confirmation is always comforting, but a scientist looks even harder for an exception, which is always more useful. After much review, he finally recognized a possible significant variable. Back in 1972, computing power and digital memory were trivial in comparison to what we take for granted today. This being the case, Lorenz sought to save bytes and processing time by performing his calculations only to the thousandth decimal place. Which seemed to him both economical and more than sufficient. But as he asked himself, what if it wasn't? What if the calculations were simply too coarse to reveal the truth? So he tried calculating the same data to the 10,000 decimal place and got the same erroneous result. Undeterred, he reran the algorithm to the 100,000 decimal place. With this adjustment, the forecast changed to rain. A humble weather scientist, Lorenz suddenly found himself the pioneer of a new field, chaos theory. Having developed a mathematical model to track how air moved around the globe's atmosphere, he discovered that the most minute differences in the data he put into his algorithm, the difference between the rounding to a thousandth decimal place versus the hundred thousandth, yielded enormously different weather forecasts. This prompted the conclusion that within the apparent randomness of large dynamic systems such as global atmospheric conditions, minute differences in the initial inputs can trigger major unexpected changes that later in time, and even at downstream locations quite remote from those inputs, He called the phenomenon a sensitive dependency on initial conditions, but it came to be known as the butterfly effect, which is a dramatic shorthand for the fact that some seemingly insignificant input, for instance, the flapping of a butterfly's wing in Brazil, can affect the trajectory of something as momentous as, say, a tornado in Texas. Lorenzo discovered that his forecasting calculations were insufficiently granular, so gross in fact that they missed the most critical cause and effect relationship altogether. For business leaders, the first 100 days of an effort to earn the right to grow will yield an abundance of data that can be used to guide initial initiatives from segmentation and simplification. But given the urgency of a company in need of a turnaround and the limitations of short-term data, the numbers will likely be too gross to create an optimal predictive guidance. Only repeat iterations of the 4-phase cycle of segment, simplify, zero up, and grow throughout the first 4 years of the deployment of the new business plan will enable executives to tune the focus on the critical few in the business more precisely and productively. As the iterations are repeated throughout the full span of the business plan, Typically 3 to 5 years, the focus will become sharper and sharper, predictions more and more accurate, and the adjustments more and more effective in driving profitable growth. Will you eventually find perfection? No, but you will be getting warmer all the time. Thank you. Have a great day, and don't forget to subscribe to our channel.
