In this context, it means for example to not use overly complicated models for prediction, classification or other data science tasks. Most of the time, a simple model provides good enough results. A more complicated model may improve the performance slightly, but at the price of higher complexity and slower calculations. Unless you have strong evidence that your simple model is unsuited for your data, stick to it. Don't forget that at some point, you may have to explain it to your team, your boss, the boss of your boss and so on.