The data model is life. In some senses it really is. The data model of life is contained in DNA.
The data mutates and you get something different. Is this a data transmission defect or is it something that allows for information to change and for innovation to occur?
Organizational data is somewhat like that. You want the data to fit within the model. When the data “mutates” and doesn’t fit the model anymore, we are not sure whether to throw out the model or throw out the data.
As a general rule (and I dislike rules as much as anyone), it it easier to throw out the data than the model. Typically, there is a lot more riding in the model so it is a lot easier to ditch the offending data.
But sometimes this data is an early warning signal that something is wrong with the model.
Models can’t account for everything and to think that they do is to set yourself up for big trouble. The fact that data models need to change as business models change is one more reason why businesses often seem out of sync.
Big systems rely on big models which are built on assumptions that won’t last forever. There is a lot invested in these big models and people are very reluctant to change them.