6 Tips for Avoiding Poor Management Decisions

Start with a quantitative score for various options

man thinking with fingers pointing in all directions
New mistakes are something to learn from, but old mistakes can be avoided. Getty Images
Headshot of Shelly Palmer

You should never get mad at yourself for making new mistakes. It’s how we learn. But if you find yourself making too many old mistakes, here are six tips that will help you improve the quality of your decision-making.

A decision matrix

When I need to make complex business decisions, I find it helpful to use a decision matrix—which is a fancy name for a table you can use to evaluate a set of options against a set of specific criteria. The goal is to develop a quantitative score for various options that will help you structure your thoughts. You can download a free Excel template here. Armed with your quantitative scoring tools, here are six tips for avoiding poor management decisions.

1. Tell your team what you are trying to accomplish, not how to accomplish it.

Generally speaking, unless you are an expert in the field and you know exactly what you want and why you want it, you should resist the temptation to tell a vendor (or a direct report who is schooled in the art) what you want them to do. Outcomes will almost always be better if you tell them what you are trying to accomplish, and then ask them what they suggest and why. Hire the best and let them work.

2. Seek out diverse opinions. Avoid echo chambers, sycophants and yes people.

If you want to make good decisions, you need to hear diverse points of view. Young people think differently than older people, they have a different relationship with their technology and they solve problems differently. People from different backgrounds bring a richness of thought to every decision-making process. The worst thing you can do is get stuck in a filter bubble or an echo chamber where only your ideas are considered and only one approach to a decision is analyzed. Diversity rules. Sycophants and yes people need to go.

3. Gather as much data as you can and take the time to consider it.

There is more information available than ever—but information is not knowledge. You need to gather as much data as possible and then analyze it. When making evidence-based decisions, be aware of how easy it is to make careless errors. Here’s an example courtesy of Daniel Kahneman and Amos Tversky: “A bat and a ball cost $1.10 in total. The bat costs $1.00 more than the ball. How much does the ball cost?” About half the people who quickly answer this question say, 10 cents. If you think about it for just an extra second, that’s clearly not the right answer—so take some time to carefully consider the data you’ve gathered.

4. Recognize your biases, and be empathetic.

The most successful CEOs are all unique individuals with unique management styles, but the vast majority of them share a special quality: empathy. They have the ability to put themselves in other people’s shoes and intrinsically know how others are likely to feel after a decision is made. They also understand that they (themselves) have a biased world view. To avoid making poor decisions, imagine yourself on the other side of the table. How would you feel? How would you react? This is one of the most helpful thought experiments you can conduct before you make an important decision.

5. Identify alternatives. What happens if you don’t do this?

What will happen if we do this? What will happen if we don’t? One of the best ways to identify alternatives is to imagine that it is five years later. Your decision led to a failure. Why? Then turn it around. Your decision led to success. Why? Argue both sides. State the obvious. Sometimes, the answers are not as obvious as they should be, and this simple technique will also help you understand what other information you need to make a better decision.


@shellypalmer Shelly Palmer is CEO of The Palmer Group, a strategic advisory, technology solutions and business development practice focused at the nexus of media and marketing with a special emphasis on machine learning and data-driven decision-making.
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