Edward Lorenz was an MIT professor and 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 a Butterfly’s 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 phenomena on weather so that he could better predict weather conditions in given place at a given time. He tested the first iteration of his program by applying it to historical weather data, so that he could evaluate the accuracy of the predictions the program produced, comparing prediction to known outcome. His software predicted sunshine in a certain location on a certain day. Great! But then he looked at 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 as much as success. He dug back through his work in search of a wrong turn. 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 exception, which is always more useful. After much review, he finally recognized a possibly 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, he asked himself, What if it wasn’t? What if the calculations were simply too coarse too reveal the truth?

So, he tried calculating the same data to the ten thousandth decimal place and got the same erroneous result. Undeterred, he reran the algorithm to the hundred thousandth 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 in the global atmosphere, he discovered that the most minute differences in the data he put into his algorithm—the difference between rounding to the 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 later in time and even at downstream locations quite remote from those inputs. He called this phenomenon a “sensitive dependence on initial conditions,” but it has come to be known as the “Butterfly Effect,” which is dramatic shorthand for the fact that some seemingly insignificant input—for instance, the flapping of a butterfly’s wings in Brazil—can affect the trajectory of something as momentous, say, as a tornado in Texas.

Lorenz discovered that his forecasting calculations were insufficiently granular, so gross, in fact, that they missed the most critical cause-and-effect relationships altogether. For business leaders, the First Hundred Days of an effort to earn the right to grow will yield an abundance of data that can be used to guide initial initiatives of 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 optimal predictive guidance. Only repeated iterations of the four-phase cycle of Segment, Simplify, Zero-Up, and Grow throughout the first full year of the deployment of a 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 three to five years—the focus will become sharper and sharper, the 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.