We make hundreds of decisions every day, both professionally and in our private life. From deciding to not order that cookie with our morning coffee, to making critical decisions that can impact millions of people for years to come. Would stand to reason that we are all expert decision makers? There is a lot of evidence to the contrary. Here are common traps people fall into:
Judging decision by outcome
Most of us judge how good a decision was by its outcome. Take a moment to think of a good decision you made recently. Chances are, you picked it because it had a good outcome. When things go south, we automatically assume we made a bad decision.
Its folly is hard to see because it usually works. For example, you gained 20 kgs probably because you have been ordering too many cookies with your morning coffee.
However, imagine that you had $1,000 to invest and you see that your local lotto has $100M jackpot. Here is a chance to 100,000x your savings. The bigger the jackpot, more people shell out for tickets. I've spoken to some retirees who play lotto religiously, spend well over $1,000 a month, hoping to turn their life around one day when they win. Well, there is no world where this "investment" makes sense. Even if you win, it was a stupid decision. Because if you made this bet a million times, you would probably not win the jackpot all of those times. The only time you should make this bet is if the lotto is rigged and you have inside information on which numbers to pick.
A good outcome doesn’t make a decision good, and a bad outcome doesn’t mean the decision was bad.
Data driven decision making
Most of us have not built a healthy enough scepticism for numbers. Give us a powerpoint slide with a nice story using a few tables and charts and we would sign our life away. After all, you are making data driven decision. What can go wrong?
Way too many things unfortunately. Was the data used accurate? relevant? complete? Is the analysis done correctly? Is the story told in a way that is not misleading? Are the conclusions drawn from the data correct?
For example, imagine we put together a team to thoroughly analyse the fortune 500 companies. Identify the top 10 attributes that are common among the ultra successful. If we hired a good writer to put together a book on our findings, it is guaranteed to be a bestseller. You'll find plenty of papers in management research literature just like this. We have instagram influencers and marketing firms send out surveys to their followers and publish reports on their data that supports the conclusions they want to promote.
This, unfortunately, could not be any less scientifically rigorous. Even when there is no fudging going on, there is huge selection bias on the data. The data is incomplete. Most likely not representative of the population or reality. If you looked at all the companies instead, you would find lots of example of companies that are not anywhere as successful but have those 10 attributes.
You make decisions on bad data and/or analysis, your decisions will be just as bad. Garbage in, garbage out.
Technology driven decision making
This is similar to data driven but comes from blind trust in technology. Think of people's reliance on GPTs for example over traditional searching. Not that searching online always gives you the right answer. For example, studies have found that these models most often confidently return incorrect information on current affairs. This makes perfect sense as it takes time to re-train these models with the latest data.
We have a blind trust in technology in general. I was once part of a company where the CEO came to me one day to discuss how some machine learning could be integrated into our product. Another team member had "trained a model" to predict when customers would churn or something like that. So I went over to have a chat. After giving plenty of compliments, I asked simply, how accurate the model was. He could not give me an answer. That told me all I needed to know and killed all my enthusiasm. He probably copy pasted some example python code from the internet and tried to fit our data into it. I asked how he validated the model. "What is that?" he replied. If I had no experience with machine learning, I probably would have been jumping up and down to integrate it too like our CEO, waste weeks of my teams time working on something that misleads everyone.
This probably comes from our fear and awe of the unknown. There is this thing that I don't understand. I also don't have the time, energy or ability to understand it. But there is a lot of hype around it. There must be something to it. So when someone tells me everything I want to hear, I am going to trust it blindly, play cheerleader and hope it solves all my problems. Wishful thinking at its finest. However, ignorance is not bliss.
Authority & Incentives
I think there is no avoiding this one. Because of the complexity of the world we live in, we have to rely on experts to help us navigate and make good decisions. But it also does not hurt to apply a bit of healthy scepticism.
To give a personal example, I am losing my hair. There is androgenic alopecia in my family. My wife sees a lot of social media marketing promoting hair clinics says, "why don't you go to one of those." I look at the same clinics and I see mis-aligned incentives, lot of paid reviews and unsatisfied customers saying their treatments do not work. Programmers want to write code, butchers want to cut and hair clinics want to sell hair treatments. There is nothing wrong with that. Before I resign myself to a lifetime of paying for repeated hair loss treatments, shouldn't I at least talk to my doctor first - who is not incentivised to sell me hair treatments?
So when your consultant from the company that specialises in building data lakes says you need a data lake, even if you know nothing, maybe it is a great idea to get a few second opinions from people who has other things to sell?
Conclusion
Making good decision is hard and there is a lot more to be said on this topic.
A good decision isn’t one that just feels right or happens to work out. It’s one made with clear thinking, decent inputs, and an awareness of your blind spots. It means digging deep, checking your assumptions - doing your due diligence. That means not getting too swept up by flashy data, not trusting tech just because it sounds fancy, and definitely not outsourcing your brain to someone with a sales target.
You don’t need to be perfect. You just need to be a little more curious, a little more sceptical, and a little more willing to ask the dumb questions.
