Analytical Skills for AI and Data Science

Chapter 5. From Actions to Consequences: Learning How to Simplify

We’ve mentioned several times already that we impact our business objectives by making decisions that may or may not have the consequences we expect. But how do we know what to expect? And how did we decide that this set of actions was worth the time spent experimenting in the first place? In this chapter, we will learn one of the most important analytical skills: the ability to simplify the world and solve restricted problems. This will hopefully take us a bit closer to analytical enlightenment.

Revisiting the graphic shown in Figure 5-1, we will now put our focus on the connection between actions and consequences, in a process mediated by causation. This is where we put into practice our outstanding ability to create theories about how the world works. But since the world is complex, we’d better learn how to simplify.

Figure 5-1. From actions to consequences

Why Do We Need to Simplify?

Imagine that we start with a project to solve a new business question. Following the recommendation in Chapter 3, we set up the problem by starting with the question and moving backward. We first identify some drivers behind our objectives and then come up with theories that map our levers to these consequences.

It is not uncommon, however, that for any given consequence we’re going after, there are many levers we can pull. Each ...