Engineering Tip: Don't underestimate the importance of good quality data
Engineering analysis and modeling are only as good as the data collected. If you collect good quality data, then your analysis and models will be more accurate. If you collect crappy data, all of the fanciest models and calculations won't matter. Collecting good quality data is the first, and perhaps the most important step when arriving at a meaningful and accurate answer. Don't underestimate the importance of good data.
I suspect that most of us are aware this. So why then is it that we often settle for crappy data? It's because the crappy data is cheaper. I'll give you an example that I see often in well testing. Well test analysis relies heavily on bottomhole pressure. The best way to get bottomhole pressure is to run gauges downhole and measure it directly. However, this costs a lot more than just estimating it from surface pressure. So what people often do is go the cheap route and estimate bottomhole pressure from surface, and settle for a less accurate answer. This is a fine strategy and there is nothing wrong with it, so long as you are honest with yourself that the results will be less accurate. We all work for companies who are in the business of making money, and we should all be mindful of costs. However, there is a cost to making decisions on low-accuracy assumptions. Just be mindful of this.
So how do we decide when good data is necessary and when it's safe to cut corners? Well, if the answer you are trying to find is really important and has large implications, then I believe that good data is worth the price. However, if your potential answer is less important and an "estimate" is all you really need, then I'd say that lower-quality data is a fine choice. Keep costs low where you can, so you have a little more to spend where it's really necessary.