# The Mechanics of Organizational Analysis By:: [[Brian Heath]] 2024-04-27 If one is observant, reflective, and aware of organizational and human systems, one will eventually see patterns and become adept at predicting likely outcomes. Within the popular treatment of analytics, this is called predictive analytics and often takes the form of statistical machine learning models. However, these quantitative data and mathematical models are limited when encountering small sample sizes. For example, one can only work for a handful of organizations. So, it is unlikely that one could produce an effective machine learning or statistical model to predict the outcome of a newly encountered organization. Certainly, one could ask others to contribute their experiences, but their data points are skewed by their perception of reality. In a purely mathematical and scientific sense, these data points can only be incorporated by violating many assumptions. Yet, despite all of these, a significant number can effectively predict the systemic outcomes of human organizations. How is this? Within the popular treatment of analytics, they are just statistically lucky. But their size indicates that it isn't just a bunch of people winning the lottery. For example, when an organization is on the path to failure, many individuals can accurately predict its fate well before the alarms go off. How these individuals do this comes down to understanding how systems work. It's about more than collecting enough statistically valid samples and putting them into a massive machine-learning model to see what comes out the other side. All one needs to do is understand how something works, and everything will unfold. Just as a good car mechanic can diagnose a car's failure from a short description, so too can a good metamodern analyst diagnose the fate of an organization from a short time observing it. The failure of the modern, popular analyst and their training is the focus on the mechanics of statistical models rather than the mechanics of life, humanity, and organizations. #### Related Items [[Organization]] [[Systems Thinking]] [[Analytics]] [[Statistics]] [[Understanding]] [[Science]] [[Life]]