I agree with you that decisions seem to be seldom based on data resulting from an analytical data model or report. So much for the data-driven enterprise!

Data is not enough to support decision-making; one also needs a mental or mechanistical model that can be used to explain the data. If you look at the history of science, there is progress in many areas made based on data.

Unfortunately, analytics-based data are used more to support one’s biases than to understand the business. Managers tend to use their intuition when making decisions, though the heuristics they use can be quite random, occasionally based on what they remember from school or seen in practice.

Breaking things into pieces and making associations between them is part of causal modeling, The model can be used then with a set of casual maps to simulate a real-world scenario.

Problem-solving methodologies have their own area of applicability, and from what I’ve seen aren’t by far perfect – there’s no free lunch.

There are many separate ideas and I’m trying to see the bigger picture and it seems a bit challenging without reading the book.