If you ask someone from the American Midwest how they feel in 80°F weather, they’ll likely say it’s too hot. But ask someone from the tropics, and they’ll tell you it feels just right. The same temperature, yet completely different reactions—why does this happen?
Now, think about a thermostat. It regulates temperature based on data, adjusting heating or cooling to reach a set number. But the real goal isn’t just achieving a specific temperature—it’s creating a comfortable environment for people. The problem? People don’t adjust the temperature based on numbers alone; they do it based on how they feel. So why do we often rely on data without considering its real-world impact?
Let’s break it down. If the objective is simply to control temperature, then using a thermostat works perfectly. But if the goal is comfort, using temperature as a stand-in for how people actually feel falls short. Temperature is just a secondary metric—it points in the right direction but doesn’t directly measure comfort. The data model behind this decision-making lacks the full picture.
While this may seem like a minor example, it highlights a bigger issue. When decisions are based on secondary data that doesn’t directly affect the system’s true purpose, the results are unreliable. Effective decision-making requires more than just data—it requires the right data.