Data as a basis for decision making.
Organizations prefer to make decisions based on objective data. In terms of data, this often works, but the objectivity of management is often the problem.
“We rely on God. Everyone else brings data, ”said Edwards Deming. The key to making objective decisions doesn’t just lie in the reliability of your data. The context in which decisions are made is at least as important. This context is often already established, for example within statistics and other exact sciences. But within organizations, management decides on the context. And often afterwards.
Many decision makers think they are data driven when looking at their KPIs. Often the decision is inspired by the data but not objective. This is because one has not defined the context beforehand. If you are undisciplined in your attempts to use data for decision making, your approach is prone to cognitive prejudice.
A major problem with data-based decision making is confirmation bias. This means that the decision maker observes and highlights facts that correspond to what he already “believes” subconsciously. This has the underlying effect of moving the goal posts while assessing the “facts.”
How often do you decide in advance about the maximum amount you want to spend on a holiday flight ticket? In almost all cases, we decide on this while comparing prices.
It is for this reason that the decision criteria are determined in advance in science to test the hypothesis. If you fail to do this, you will selectively handle the data and look for those figures that match your views and make you feel better. In particular, the urge to always strive for a better feeling, ensures that even in the highest regions of the business community people would rather talk about the upcoming takeover than to face the fact that the solvency of the organization has deteriorated for years.
The Ikea effect is the subconscious over-valuation of things for which [much] effort has been made. If people have spent a lot of time and attention on a spreadsheet model or project, they will estimate the value high. This does not have to be the case for the organization. The opposite may be true. The contradiction that then arises between the designer and the organization causes downplay. People then start negotiating with themselves and the environment: “… but the performance of my new prototype is not that bad, I could still release this …”
When drawing up tenders and RFQ, it is often the case that, unconsciously, people attribute themselves to a specific model or supplier. Because how do you objectively determine the different assessment values of the criteria within the tender?
To break free from your bias and establish a measurable objective context in advance, you must be firmly in your shoes. Especially if you cannot immediately obtain other data that do fit within the frameworks you set. You will then have to go back to the actual problem you are trying to solve. What goal do you really pursue?
You then determine the criteria that contribute to achieving your goal. Then answer how these criteria meet them? Form your own burden of proof. Do you have the frameworks clear and sharp? Then you can determine your default action.
The flight ticket for destination A has a lower limit of xxx and an upper limit of yyy with unchanged conditions.
In case of deviating conditions you can of course set a different upper and lower limit. As long as you ensure that the deviating conditions are expressed in a predetermined measurable value.
The frameworks that you have outlined and the standard action that you have determined determine the collection of your data. Your current set of data may be insufficient. Don’t haggle with that. If you do give in to that, then the above has suddenly become useless and you will again fall into the same bias. However, it is possible to develop frameworks and decision-making in advance against different data scenarios.
The success of your organization, improving your organizational performance is largely determined by the predictability of the actions you take. The data is often blamed when deviations arise. Realize that the vast majority of decisions you make are not consciously rational but have long been made by your subconscious mind. It is a long-term discussion among neurologists / psychologists: Is your consciousness witness or suspicious of your subconscious decisions?
Data driven decision making [dutch]