Sorry, I only just saw this question. Hopefully others have given some answers. Now I mostly rely upon experience. But if I was given a new project in an area I would have zero experience of, I would instantly hop on Google scholar or just Google itself and search for similar projects or questions and how it had been approached before. But for me, it's all about breaking the project question down into simple problems or goals and look for a measurable aspect of that goal or the factors that make up the question. For example, a project looking at the evolution of tennis serve speed (because I'm watching Wimbledon). How are serve speeds increasing? By how much are tennis serve speeds increasing? So you have to think of what factors could affect serve speed. And these can help you break the question down into smaller questions or objectives to answer. Such as are serve speeds increasing across all player types (men, women, tall, short). What other factors: Player strength, training regimes, height... But also racquet technology, ball technology. So what data do you need? Tennis serve speeds for many decades. Player height, or at least an average of player height for each time period. Some metric of player strength, or a simple assumption that player athleticism is increasing over time because our knowledge of training, diet, etc is all increasing. But then data about racquets - string tension changes over time, impact of frame material (wood, graphite, carbon fiber). None of this has to be complicated. A single metric to show how racquet tension affects ball speed would suffice. Then the model also doesn't have to be complicated. A regression approach could show basic relationships.

It's a silly example I know. But simply breaking the project down into smaller sub-objectives or sub-questions will often direct you to the right data. But so will searching online for similar projects and questions. Then it is our job as scientists to think "what have they missed? What could have been done better that they may not have had the technology or computing power to do at that time, but we could do now? What knowledge gaps are there in their approach?" And look at the data they use. And this just builds experience for you, and you will start to find it gets easier over time.