I think the most important thing you can do to make better decisions is to learn from the decisions you make. It sounds easy, but very few people do so effectively.
The best strategy to learn from decisions is to:
- predict the results of your decisions
- write down those predictions
- Then analyze the results
- Then adjust future predictions and decisions based on what you learn
Failing to do this leads most people to fail to learn from there decisions. It is hard to improve when you don’t learn. The similarity to the PDSA improvement cycle is not a surprise. Both are about learning so you can adopt more effective strategies.
Related: Predicting Improves Learning – Management is Prediction – Making Decisions, Taking Risks – Knowledge Management: Management is Prediction – confirmation bias
The problem with decision making is that many people can’t accept the limitations of it. That being that there is no guarantee that the decision you make will lead to the desired outcome. In effect, if the outcome is not achieved then the decision is branded “bad” – but that is simply not the case. We have both written in the past about Deming’s views on the complexities of this and also permanent inconvenience of the “unknowables”, and that just means that sometimes the outcome turns out to be bad because of a set of circumstances or dynamics we could not have accounted for. That doesn’t mean the process is necessarily wrong, or that we might not take the same approach again. Next time round, in fact, we may have a greater chance of success if we learn from our experiences, that is to factor in that which was previously “unknown” but which is now “known”
The use of data to drive decision making simply reduces our odds of a bad decision. It does not guarantee the desired outcome, but that doesn’t make it a bad thing to do
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Good advice on writing them down. I would also try to avoid these common pitfalls in decision making.
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