Growing With Gray

Several years ago, a company I worked for was targeting a new market, and the team felt that our current product was not good enough to meet the specifications. We went to the lab and made significant improvements to our product, but never achieved commercial success. Why? At the same time that we started improving our product, a competitor went to market with their offering—a product similar to the grade that we chose not to market because it wasn’t good enough. It turns out that it truly was not good enough, but by working with the market, the competitor built a solid position that we were not able to overcome. Our need for the perfect product kept us from having any product.

The quest for 100%, to have the perfect product, to have all of the questions answered, can be paralyzing. When you reach that elusive 100%, the logjam is broken and making decisions becomes (relatively) easy. With all of the information in hand, we can make better decisions, so isn’t that that the best way to do things? 

In a world where time and resources are unlimited, delaying decisions until you reach 100% works well, and it greatly reduces the number of bad decisions (yes, you can and will still have decisions that go wrong even when you think you have all of the information). But in the real world, where time and resources are serious constraints, and with competition trying to beat you to the finish, waiting until you have all of the answers before making a decision can be damaging or even crippling. The reason is that often the first 50%, 80%, or even 90% of the answers come faster and more easily than the rest. Indeed, the last 10% of data may take as long in both time and resources as the first 90%. If you, or your competition, can make sound decisions with only partial information, it can deliver a strong advantage in both cost and speed. 

So how do we make good decisions with limited information? First, it’s important to accept that the answer is not an absolute. 80% might work some times, but the real percentage will vary with every situation. If the consequences of making a mistake are large, for example, if someone could die or become sick, the environment destroyed, or your company will greatly suffer, then you need to be absolutely certain that you have all of the right information before acting. But in the majority of cases, the consequences of a wrong decision are far smaller, so you have an opportunity to take action before you reach 100%. The balance of consequences, resources, cost, competition, and other factors will determine how much information is needed to act. 

Second, recognize that not all information is equivalent. In any data set we will find a large mass of low-value data along with a small amount of critically important information. If we can foresee the things that will be critical to success, and we test well for show-stoppers, then we are front-loading our data set with the most relevant pieces of information. This way, we can often make very good decisions with an incomplete data set. 

Still, many people are uncomfortable making decisions without all of the data. There is always one more experiment to do, or one more piece of market data to gather. To make decisions when when the picture is not black and white, we must become comfortable with gray. This is not natural to most of us, but there are ways for us to practice so that we improve how we deal with gray.  

• Routinely consider ‘what consequences do I face if I act early and am wrong’? If the answer is something small, then you can be more comfortable to move forward without all of the answers.
• What are the negative consequences of waiting until I have all of the data? Perhaps it raises the cost of the project, or delays another important project, or allows the competition to be first to market.
• Ask your team, is the information we are gathering the most important data, or is it simply what is quick and easy for us to obtain?
• Take on, or assign to your team, projects that are ‘gray.’ There can be frustration dealing with these poorly defined assignments, but these will make us more comfortable when we inevitably face uncertain projects and situations in the future.

In the end, the choice of when to make the decisions and with how complete of a data set will depend on the situation, on your level of comfort, and the culture in which you work. But regardless of the choice, having a good understanding of the risk of action, or of inaction, will help you make better decisions.