Improvement implies change. Change requires doing things differently. That requires making different decisions. And that requires innovative thinking. And that requires new knowledge.
We are comfortable with the idea of adding new knowledge to the vast store we have already accumulated.
We are less comfortable with the idea of removing old knowledge when it has grown out-of-date.
We are shocked when we discover that some of our knowledge is just wrong and it always has been. Since the start of time.
So we need to prepare ourselves for those sorts of shocks. We need to be resilient so that we are not knocked off our feet by them. We need to practice a different emotional reaction to our habitual fright-flight-or-fight reaction.
We need to cultivate our curiosity.
It comes as a big shock to many when they learn that it is impossible to determine the cause from an analysis of the observed effect. Not just difficult. Impossible.
“No Way!” We shout angrily. “We do that all the time!”
But do we?
What we do is we observe temporal associations. We notice that Y happened after X and we conclude that X caused Y.
This is an incorrect conclusion. We can only conclude from this observation that ‘X may have played a part in causing Y’ but we cannot prove it.
Not by observation alone.
What we can definitely say is that Y did not cause X – because time does not go backwards. At least it does not appear to.
Another thing that does not go backwards is information.
Q: What is 2 + 2? Four. Easy. There is only one answer. Two numbers become one.
Let us try this in reverse …
Q: What two numbers when added together give 4? Tricky. There are countless answers. One number cannot become two without adding uncertainty. Guessing.
So when we look at the information coming out of a system – the effects and we attempt to analyse it to reveal the causes we hit a problem. It is impossible.
And learning that is a big shock to people who describe themselves as ‘information analysts’ …. the whole foundation of what they do appears to evaporate.
So we need to outline what we can reasonably do with the retrospective analysis of effect data.
We can look for patterns.
Patterns that point to plausible causes.
Just like patterns of symptoms that point to possible diseases.
But how do we learn what patterns to look for?
Simple. We experiment. We do things and observe what happens immediately afterwards – the immediate effects. We conduct lots and lots of small experiments. And we learn the repeating patterns. “If the context is this and I do that then I always see this effect”.
If we observe a young child learning that is what we see … they are experimenting all the time. They are curious. They delight in discovery. Novelty is fun. Learning to walk is a game. Learning to talk is a game. Learning to be a synergistic partner in a social group is a game.
And that same child-like curiosity is required for effective improvement.
And we know when we are doing improvement right: it feels good. It is fun. Learning is fun.