Rather, the hardest is often defining what the actual problem is that you are trying to find the solution for.
Take for instance a VBA problem, or a college Econ problem set: 90% of “solution” is setting up the problem correctly. Computers solve for the solution. Thank God for that or else I would’ve flunked out during my college first semester.
And while definite solutions to problems exist in college homework, it’s not the same for real-life problems. Try to estimate a market size, calculate GDP, etc.; sure there is one “right” solution (as in there is a number, accurate down to the penny) of what the GDP or a market size of something is — but it’s probably not worth the time. The actual problem then becomes where to optimally stop the search for the real number and settle for an “estimate” with error – an argument for strategic quitting. In a simplified graph, it would look like a diminishing returns graph from an Intro to Econ course. In a world with complex problems, finite estimationsÂ trumpÂ infinite explorations. Resources may be better allocated somewhere else.
So don’t get stuck at this definition stage, get overwhelmed, and head down the wrong path or give up entirely. If the practical validity of the solution is paramount, then obsess instead over the integrity of definition, data collection methods, and analysis – instead of the actual solution.