I have been thinking about the most ubiquitous model there is, one that we all use daily, I’m thinking of course, about language. It’s ubiquity and plainness makes it difficult to appreciate the fact that we each carry around inside our heads a highly complex model whose elements we instantiate every time we speak. It's easy to forget that though we all use language, we each have different proficiency with it, the great orators, writers and word smiths practiced extensively to get good at what they did.
But limiting it to just speech would be foolhardy, it goes deeper than that, language may actually limit our thought process. There are examples of how language limits the thought process in many areas of life. The Korean airlines for example, started focusing on improving the grasp of the English language of pilots and co-pilots to help them break the cultural taboo of questioning your superiors in Korea. People who speak more than one language fluently frequently realize that there are things that can be better expressed in other languages.
One reason why we use models in EA, and other disciplines as well, is to help us predict out of sample. It might be beneficial to use multiple models when predicting out of sample but EA usually captures data to form a single model from each layer. This limits how effective it can be in making out of sample predictions.
We also need to be careful in considering what kind of model we choose to create in each layer of the organization, it has to be focused on the intended use of the model. Just like you wouldn't bring a gun to a knife fight, don't bring up the class model when discussing business capabilities.
But limiting it to just speech would be foolhardy, it goes deeper than that, language may actually limit our thought process. There are examples of how language limits the thought process in many areas of life. The Korean airlines for example, started focusing on improving the grasp of the English language of pilots and co-pilots to help them break the cultural taboo of questioning your superiors in Korea. People who speak more than one language fluently frequently realize that there are things that can be better expressed in other languages.
One reason why we use models in EA, and other disciplines as well, is to help us predict out of sample. It might be beneficial to use multiple models when predicting out of sample but EA usually captures data to form a single model from each layer. This limits how effective it can be in making out of sample predictions.
We also need to be careful in considering what kind of model we choose to create in each layer of the organization, it has to be focused on the intended use of the model. Just like you wouldn't bring a gun to a knife fight, don't bring up the class model when discussing business capabilities.
