For some problems, your principal challenge will be to find relevant data. But with the ever-increasing power of search engines, now most of the time you will have tons of information related to your subject and your real challenge will be to stay afloat above all this. A powerful way to achieve this is to go fishing for data with a precise idea of what you’re looking for.
Here is an analogy: theoretically there are two ways to go fishing. The first is to isolate a volume of water, evaporate it—i.e. boil the ocean—and see what sort of animals you’re left with at the end. The second one is to first identify a place where the fishes that you’re looking for are, then cast your net. In theory, both could work but, obviously, the second is much less work-intensive.
So we’re talking about a ground-up versus a top-down approach. The ground-up approach is fine if you have a lot of time. Then you can collect data and more data and then go through the inductive process of identifying what it means as a group. But in the end, you’ll probably have used only a fraction of the information you’ve gathered, so the signal-to-noise ratio is low and you’re wasting a lot of time and energy gathering all that useless data.
So, the ground-up approach doesn’t work so well. After all, before you leave your house to go somewhere new, you first look at the map, you don’t just start driving, hoping you’re going in the right direction.
But that begs the question: If the top-down approach is so advantageous, why then do we rush into gathering data before identifying precisely what we need?
The short answer is: because it’s counter-intuitive to do so. Planning is hard work and not quite as gratifying as finding stuff, so we tend to go with the “finding stuff”. It’s the same conversation that we had with the “how” versus “why” trees: “how” trees are more attractive because you’re already actively thinking about implementing a solution, but if you don’t know why you have the problem in the first place, you’ll waste lots of energy finding and testing irrelevant solutions. Also, we’re not really taught to spend much time in planning our analyses—apart, maybe for computer programmers that are directed to spend a significant amount of time planning their code before writing it.
First, identify what you’re looking for: build hypotheses
We’ve talked about it: for complex problems, issue trees/logic trees are extremely powerful tools because they allow you to structure your analysis. Your logic tree helps you explore all the possible answers to your key question. To best exploit this tool, you need to develop your tree up to the point where you get to discrete hypotheses: clear statements that you need to disprove or “prove” (for a note on “proving” hypotheses, see here) and that, by doing so, will help guide your thinking.
Second, build your analysis plan
Once you have stated your hypotheses, identify how to test them: what data do you need and where can you get it? For a business problem, standard sources will include browsing the internet and interviewing your suppliers and/or clients, coworkers, industry specialists, competitors, etc. Be as specific as you can in your analysis plan; in particular, explicitly state the questions that you want to ask others. That way, your issue tree also becomes your interview guide and, when talking with one source, you can ask them about all your questions without having to get back to them repeatedly.
Only then, get the information that you need
Now that you are ready to get your data, get it, understand its “so what” and capture it in your issue tree. Also capture your conclusion. If you disproved your hypothesis, cross it (don’t erase it, it will be useful for later reference). Chances are that, in your analysis, you will have uncovered new ideas; modify your tree accordingly and create new branches if you have to. Just remember to first think about what data you want to get before initiating on your data-gathering trip.
So the concept is simple: three easy steps. The hard part is changing your habits. Ther I can’t help you much. But give it a shot, it’s worth it.