For example, some people point to the huge DC snowstorm as proof that global warming is a myth. Um, first of all, data isn't proof. It's evidence. And second, the storm might actually be evidence of a warming trend, because warmer air can hold more moisture and thus create larger storms. Or maybe the blizzard is just the result of an El Nino pattern and it might have nothing to do with climate change at all, man-made or otherwise. And please understand - this post is not about global warming. It's about understanding & interpreting data.
Me, I'm convinced data never speaks for itself. It must always be processed and interpreted. That's particularly true on projects where we collect metrics. And there are many kinds of metrics - predictive, descriptive, etc. Sometimes we simply collect the obvious & easy ones, which may or may not tell us anything about the project team's performance. Other times we misread good data. It can be done well - but it takes deliberate effort (and a grain of salt).
I don't have an easy answer - all I'm trying to say here is that we're fooling ourselves if we think the data is self-interpretive. We're taking a dangerous shortcut when we fail to reflect on the data and the many ways it can be shaped (or mis-shaped), interpreted (or mis-interpreted).