BUS206 3.1.2 Discussion about Data Driven Decision Making

In a sense, all decisions come down to balancing your gut perceptions against objective data. Many of us don’t consciously perceive that balance, but nonetheless, it’s an inherent process to responsible decision making.

We look at comfort vs price, for example, when evaluating heating our homes. Intuitively, we know that we’re most comfortable within a certain temperature range- for some members of a family that range might be more narrow than for others. There’s a “gut feeling” that the family will function more smoothly and be more harmonious if we can make sure that the basic comfort level of each family member is met. We know, objectively, that being uncomfortably cool is not dangerous, that humans are able to survive within certain temperature ranges, even if we may not be particularly happy at those temperatures. However, our gut feelings tell us that mere survival is not the goal we want to set.

Some of the data that needs to be gathered to decide what temperature to heat the house to in the winter is the temperature ranges that each family member is comfortable in. If family member A is comfortable in a range between 66 F and 80 F, but family members B and C are comfortable in a range between 72 and 86 F, it makes sense to try to aim for a range between 72 and 80 F.

Additional data is needed though- how much will it cost to heat the house to each potential temperature? What types of heat are available, and how will blending those heat sources reduce costs? In my home we have three potential heat sources: passive solar, electric space heaters, and an oil furnace. Passive solar is by far the least expensive, however it is only available for certain periods of the day and is of variable strength on any given day. Electric space heaters are next in terms of expense to run, however they are less safe and cannot be left running unattended. They are also slightly less efficient, which offsets some of the cost savings. The oil furnace is the most efficient, and is always available, but is the most costly.

By combining all of the data points, we can evaluate that it is best to aim for a temperature of around 72 F. If by using the lesser expensive heating sources we’re able to achieve a slightly higher temperature, then that’s all to the good- but 72 is the lowest temperature that all family members are comfortable at, and it is the least expensive to reach via the most expensive option.

Right now, it is 22 F outside my home, but it is sunny, so with the windows uncovered, and the space heaters running intermittently, my home is at a comfortable 74 F, and the oil furnace is not running, so we are in optimal conditions.

That is a very specific example of using gut intuition and data points to drive making a decision, but this process holds true in virtually all decision making processes. Some decisions can be weighted more in one direction than the other- the desire to have children or pets for example, is often weighted more heavily than the data about the impact additional members of the family. However, even then the data can influence the decision making process. Perhaps you decide to have fewer children, or to delay having children for a time based on a cost analysis. Perhaps you decide to get a small dog or a cat, rather than a St. Bernard. Less emotionally weighted decisions such as which gas station to purchase your fuel at may be almost entirely data based, rather than “gut feeling.” Even then, though, gut feeling about the neighborhood the gas station is is
might influence your decision.

Ideally, it is important to be aware of which decisions you are making based upon objective data and which decisions you are making more along your intuition. Evaluating each decision in light of how much emotional or intuitive weight you can afford to give it is a good practice to get into, as well as making sure you are gathering as much relevant objective data as possible to provide balance.